WO2007090605A1

WO2007090605A1 - A method and a system for identifying potentially fraudulent customers in relation to electronic customer action based systems, and a computer program for performing said method

Info

Publication number: WO2007090605A1
Application number: PCT/EP2007/000995
Authority: WO
Inventors: Per Flemming Eldon; Nicolai Bentsen; Jacob Thomsen
Original assignee: Mediakey Ltd.
Priority date: 2006-02-06
Filing date: 2007-02-06
Publication date: 2007-08-16
Also published as: EP1816595A1; US20090307028A1

Abstract

The invention concerns a computer-implemented method, system and computer program for performing said method, for identifying potentially fraudulent entities in relation to electronic entity action based systems, associated with for example at least one client website 450. Said method comprises the following steps: providing information at least concerning said entity's behaviour over time in relation to said at least one action based system by monitoring at least one entity induced event 120; and determining said at least one behaviour profile using behaviour classification algorithms to analyse said information in order to identify potentially fraudulent entities or in order to detect the interest of said entity. Accordingly, in a unique way, mathematical analysis may be utilized to process data concerning events in relation to electronic entity action based systems, which are recordable. This provides the possibility of indicating fraud during for example the entire sales process in relation to an ecommerce activity, not merely in direct relation to the payment act, as provided with known fraud detection systems and methods. Examples of entity induced events are entity reactions on the website when being involved in the sale, e.g. the entity is clicking and selecting a certain product item in order to purchase it, or is entering entity data for the transaction, or is moving around between different pages on the merchant website, or is reluctant to enter data or to move around between pages. Further, it is no longer necessary to provide accurate transaction details, such as correct credit card number, or personal entity information, which are to be kept confidential and which are to be validated e.g. against databases containing stolen credit card numbers, in order to provide accurate and timely fraud indication. In addition to being applicable to payment on a merchant's website the disclosed method and system is also capable of detecting fraudulent behaviour in relation to mobile telecommunication networks and in credit card payment systems.

Description

A METHOD AND A SYSTEM FOR IDENTIFYING POTENTIALLY FRAUDULENT CUSTOMERS

IN RELATION TO ELECTRONIC CUSTOMER ACTION BASED SYSTEMS, AND A COMPUTER

PROGRAM FOR PERFORMING SAID METHOD

The present invention relates to a method, a system and a computer program for performing said method, for identifying potentially fraudulent customers in relation to electronic customer action based systems.

Electronic customer action based systems, which term comprises electronic systems, wherein the customer's actions are required in the use of the system, such as mobile phone systems, network based commerce activities, and systems for credit card payment, are becoming increasingly important in our society.

Network based commerce activities, which term comprises sales, in particular involving payment, of products or services between consumers or customers and merchants performed through a network, such as the Internet, by the use of e.g. personal computers, Personal Digital Assistants or PDA's, or mobile phones, have gained substantial popularity and prominence in the global economy. The resulting gain and corresponding losses due to fraud encountered in particular in the completion of these payments, i.e. during the completion of the funds transaction, have been increasing. Accordingly, several attempts have been made to conceive methods and systems for reducing or at least detecting payments by fraudulent customers, before these are completed.

The commerce activities in a sale, also a network performed sale, see fig. 1, comprises the collection of activities in relation to consummating the sale, e.g. the merchant displays product/service for sale, entry of a customer, the customer selects the product/service to be provided and request to purchase it, the customer agrees to price and terms of sale, payment is performed, the customer acquires product/service and exits. Network performed sales may further include activities relating to the sale such as the customer contacting the merchant by e-mail or phone, the merchant contacting the customer to confirm sale, and other sales related activities.

The term "transaction" comprises the activity of completing the payment made on the network, e.g. validating a payment using e.g. a credit card or by transferral of a payment from one bank account to another.

The term "sniffer" comprises a computer that listens to the network traffic between for example a server and the Internet. Such a "sniffer" can construct the relevant events from this traffic and send them to the service providers system.

The term "record of events" comprises the data concerning a specific event such as the character, the time, and the duration of the event.

The term "event" comprises the steps of an interaction between a user and a cell phone, the interaction between a user and a website for ecommerce. In principle, a non-action also constitutes an event.

The term "entity" covers various concepts such as a customer in relation to a website for ecommerce, or a user of a cell phone, or any other concepts that can interact with an action based system.

The term "behaviour profile" comprises the information derived from the analysed data that have been acquired through the tracking and analyzing aspects of the invention. Said information comprise the fraud risk potential, a record of the tracked and analysed events, and a summary of said tracked and analysed events

In a direct sale a consumer or customer enters a merchant's website in order to purchase goods and/or services. During said sale, the consumer generally provides information concerning him/herself and mode of payment, e.g. using a credit card by entering its number in order to pay for the products/services being offered on said site. The merchant submits the charge to the credit card company and deletes the transaction with the consumer once the credit card company authorizes the charge, in a straight-through type processing.

If the consumer refutes or disputes the charge, claiming he has not ordered the purchase or authorized the transaction, a charge-back occurs on the consumers credit card account, and payment to the merchant is reversed. Not only does the merchant lose a sale, he must also absorb the cost of the non-completed transaction. He is fined every time a charge-back occurs with one of his customers, and if his charge-back percentage of his total sale exceeds a small value, he may be charged with large fines, and ultimately be relieved of his merchant account. The same is true in case of fraudulent transactions, e.g. where the customer appearing on the website is not the owner of the credit card used for payment.

Many products and services are available for sale in a network, such as the Internet, examples of products being SMS-messages or call time available for mobile phones, mail order gifts, ware house items, and of services being hotel or travel bookings, and examples of network based services being long distance performed server updates, downloads of music, movies and other entertainment, and the like. In particular when providing services on the Internet the consummation of the sale with a low risk of fraud is essential to the merchant providing such network based service.

At present, there are many existing systems for detecting fraud in relation to the payments per se, where data directly relating to any one payment, e.g. amount, customer and his history, number of items etc, is analyzed according to numerous rules based on different variables. For example a simple fraud detection scheme could analyze a transaction using two rules only, e.g. "if more than X number of orders have been placed within the last Y hours" and "if the total value of the present order is over Z dollars", then the transaction should be considered fraudulent. The indicator values of X, Y and Z would then be set according to the history of frauds encountered. The first rule, i.e. "more than X numbers of orders have been placed within the last Y hours" is combined with the second rule, i.e. "the total value of the present order is above Z dollars" into a rule set. A rule or rule set may be said to include criteria such as "more than", or "value of ... is above" applied to events such as "X number of order placed within last Y hours". The rule set is the applied to the selected variables encountered during a transaction in order to determined whether the transaction is potentially fraudulent. Some analyzing tools apply weights to each such event in order to rank these events in order of their importance for the combined result of the two or more events.

United States patent application US 2005/154676 discloses an electronic commerce system for monitoring a given customers interaction with a website during the transaction to process an electronic purchase order. A fraud detection mechanism determines the likelihood that the customer is fraudulent based on user-entered information and factors relating to the customers real-time interaction with the website during the transaction.

A system for identification of fraud in relation to credit card payment on a website is disclosed in United States patent application US 2002/0099649. This system takes customer specific data such as the customer's name, shipping address, Email, IP address and credit card number and information concerning the transaction such as amount, items ordered, click-stream through the website prior to the purchase into consideration when determining the likelihood of a given customer being fraudulent.

In United States patent application US 2003/069820 transaction parameters, such as customer name, address, check account number, prior events of fraud in connection to said account or individual, etc. are analyzed and characteristics are identified in order to provide an indication as to whether the transaction is fraudulent. This system breaks down the transaction into a number of select component data parameters and single variable relationships. Points are assigned to each parameter/relationship based on the information, it represents, and whether it matches with known data. If the sum of points or fraud score exceeds a certain threshold, potential fraud is indicated and the transaction in question is not validated.

In European patent EP 0 669 032 is described a fraud detection system utilizing a complex predictive model in the form of a neural network, which performs self- learning of relationships among variables based on historical transaction data. The system uses the complex model to analyze a transaction and predict whether or not a transaction is potentially fraudulent. Such model is able to automatically correlate relationships among all of the parameters of the transaction to each other, and not just the single variable relationships of the above mentioned detection system. However, such self-learning based systems are complex to structure, difficult to develop, and require significant training and maintenance to maintain accuracy and to understand the results from properly.

The known fraud indication methods and systems, including the ones mentioned above, are applied around a point in time, which lies in direct connection with a specific payment, i.e. in direct relation to the transaction to be completed, see fig. 1, indicated by a black box, e.g. when requesting/receiving a validation check for the transaction. Accordingly, they are applied and used for a limited time before and during the transaction in question, i.e. many calculations and checks are to be performed within a relatively short time period. It is not expedient to provide a fraud determination in direct relation to the transaction in question, because this increases the time period needed for a transaction approval, which may put a strain on non- fraudulent customer patience, is not effective in a web-based environment e.g. for providing the above mentioned web-based services, as well as increases the risk of providing an erroneous positive or negative fraud indication.

Information concerning possible fraudulent individuals and/or payments is thus simply not available to a merchant in the case where a sale has not been consummated, e.g. when the customer is leaving the website without completing the payment.

Also, a problem of prior art systems have been to decide on and thus limit them to a fixed rule set in order to apply an optimal strategy and apply them in due time to either stop or at least delay a fraudulent transaction in order to reduce the resulting losses from such transactions.

In this light, it is an object of the present invention to provide a computer- implemented system and method, and a computer program for performing said method, using real-time behaviour classification algorithms to identify potentially fraudulent entities in relation to activities in at least one electronic entity action based system, such as a credit card payment system, a telecommunication system for cell phone communication, or a network or website for e-commerce activities, which system, method, and computer program alleviates the above mentioned disadvantages provided by known systems, and which is able to determine a reliable behaviour profile.

This object is achieved according to the invention by a computer-implemented method and system, and computer program for performing said method, using realtime behaviour classification algorithms in order to identify potentially fraudulent entities in relation to activities in at least one electronic entity action based system, such as a website for ecommerce activities, a system for mobile telecommunication, a system for credit card payment, and the like, by monitoring the behaviour of said entity within at least one of said action based systems, said method comprising the following steps: providing input information over time by substantially continuously monitoring at least one of said action based systems concerning the behaviour of said entity in relation to said at least one action based system by tracking a plurality of entity induced events; and sequentially analyzing said input information, at least after each said entity induced event for providing at least one behaviour profile, wherein the significance and meaning of said entity induced events are substantially dynamical and may change in time; and displaying said behaviour profile.

By continuously calculating a cumulative entity profile based on entity induced events, said events being induced over an extended period of time, the calculation extent and duration needed for each induced event can be reduced significantly as compared to known fraud detection methods in relation to e.g. electronic payment, e.g. website trade, mobile phone payments, server updates, and the like.

Accordingly, in a unique way, by the provision of for example website technology, behaviour analysis utilizing behaviour classification algorithms may be used to track entity behaviour on or in relation to an action based system, i.e. mathematical analysis may be utilized to process data concerning events, which are recordable. This provides the possibility of indicating fraud during the entire interaction with said action based system, not merely in direct relation to for instance the payment act of an ecommerce session, as is provided with known website related fraud detection systems and methods. Examples of entity induced events are customer reactions on a website for ecommerce when being involved in a sale, e.g. the customer is clicking and selecting a certain product item in order to purchase it, or is entering customer data for the transaction, or is moving around between different pages on the merchant website, or is reluctant to enter data or to move around between pages.

Hereby, an alternative or supplement to existing payment or sale fraud detection methods and systems is provided, which allows a merchant to keep track of the behaviour of individual consumers on a website, which may again directly reflect the possibility of these for committing fraudulent transactions over time.

Further, it is no longer necessary or it is at least optional to analyze the transaction process for a confirmed website sale in order to determine the possibility of fraudulent customer transactions. Based on a client and/or system provider determined selection of customer events to be tracked by said system and method, a reliable behaviour profile is determined, which leads to a reliable identification of fraudulent website customers.

Further, it is no longer necessary in order to provide accurate and timely fraud indication to provide both accurate and sensitive transaction details, such as correct credit card number, or personal customer information, which are to be kept confidential and which are to be validated e.g. against databases containing stolen credit card numbers. Such details may obviously optionally also be provided, however, it is not necessary for sensitive personal or financial data to be entered by a entity for the fraud tracking to effectively indicate a positive fraud situation. Further, the provision of inaccurate data may be tracked and indicated by the inventive system and method.

One or more action based systems may be surveyed simultaneously by the method and system according to the invention in ways known to the skilled person, e.g. when a merchant is providing more than one website, or if the fraud indication system is provided by a fraud system provider externally from said merchant. The fraud system provider is thus able to concurrently scan and survey several websites from different merchants subscribing to such service. Further, the merchant or system provider is provided the opportunity to correlate information between the websites being surveyed.

A computer program for performing the method according to the invention is provided according to claim 82. Preferably, said program is installable partly in relation to the action based system, partly in relation to the fraud indication service provider's action based system. A default rule set, and code for including the above mentioned other embodiments of the method and system according to the invention may also be provided in code, said computer program being executable e.g. from the client's action based system. Thus, the computer implemented method and system is able to be installed and run from a client's or fraud system provider's computer systems or servers, such as preferably web servers on a website for providing fraud indications.

In a preferred embodiment of the method according to the invention, said at least one behaviour profile is determined substantially continually and at least after each entity induced event in relation to said at least one action based system, thereby providing at least one substantially time variable continuous behaviour profile. Accordingly, a behaviour profile is provided continuously and for every stage of the entity activity on the action based system, which increases the reliability of the behaviour profile determined. Thus, the fraud indication is not based on a transaction being performed nor depending upon said transaction being completed. This is a major advantage, because fraud may thus be determined even without for example a payment on a website being performed. Further, it is possible not only to indicate suspicious behaviour in connection with an interaction with the action based system, but also to indicate suspicious behaviour in connection with the entity or/and the payment and/or the service/product delivery in question.

In a preferred embodiment of the method according to the invention, said at least one behaviour profile is determined by registering from which action based system said entity entered said client's said at least one action based system and/or by placing for example a "sniffer" in front of the web server of the action based system, said

"sniffer" being a computer that listens to the network traffic between for instance a server and the internet and/or by online use of log files from for instance a the web server hosting an ecommerce website, said log files comprising all movements on said website and/or by collecting event information directly on the customer's own

PC through for example via a plug-in on the clients web browser.

Alternatively, in another embodiment of the method according to the invention said at least one behaviour profile is determined intermittently by the client after tracking said at least one entity induced event in relation to said at least one action based system, thereby providing at least one substantially time variable intermittent behaviour profile. Thereby, the fraudulent entity may be identified at predetermined points in time, based on batches of tracked events and other information, such as before and/or after a transaction is being authorized in a website based ecommerce system. This reduces the need for continuous communication between the action based system part and the analyzing part of the system.

In two further embodiments of the method according to the invention said at least one behaviour profile is determined at least right before and/or right after a transaction for a payment is authorized in a website based ecommerce system. Accordingly, a behaviour profile is determined for an identification of a fraudulent entity before the transaction is authorized, i.e. there is a reduced risk of a charge-back occurring due to fraud. When performing the determination after the transaction authorisation, the client such as a merchant and/or a payment provider may be provided with an indication as to the likelihood of a charge-back being experienced with the transaction, in which case data concerning the fraud risk and the transaction may be supplied to the merchant for a decision of whether to ship or perform the ordered product or serve, respectively.

In one embodiment of the method according to the invention, said information further comprises environment induced events, comprising system and/or calendar induced events. Thus, the stability of the method and system is increased, and the reliability of the determined behaviour profile is increased, based on further information concerning system induced events, e.g. system down times, system maintenance periods, system updates, etc, and calendar induced events, e.g. the determination of the behaviour profile is able to distinguish fraud related sale during a low season, e.g. near the end of the month, and during a high season, e.g. the days before Christmas, the days after Thanksgiving in the US, during a promotional week etc, because the number of items being bought would vary substantially accordingly, and such natural variation would not indicate any type of fraud.

In another embodiment of the method according to the invention, said information further comprises data concerning the payment, comprising transaction data and/or customer data. Thus, the reliability of the determined behaviour profile is further increased, based on such information, which transaction data may comprise customer, client and/or fraud system provider input data, e.g. comprising credit card details, customer transaction history data, and transaction type data, respectively. Such data concerning the payment is optional.

In a further embodiment of the method according to the invention, the step of determining comprises applying a timeline sequence analysis onto said information. Accordingly, the customer induced events being tracked and other information, provided from or in relation to the sale, may be applied during determination in such a way, that a reliable behaviour profile is derivable, which is not dependent upon a high number of tracked events being recorded. Further, the extent and focus of said determination, i.e. number, type and field of events being tracked, may easily be adjusted according to client needs, and may be modified by input from the client, the fraud service provider and/or the system itself, providing an adaptive system. Thus, a flexible determination may be provided.

In a preferred embodiment of the method according to the invention, the step of determining comprises applying a predetermined rule set onto said information. Thus, client and/or fraud system provider are able to create, regulate, expand, and control the use of the information provided for the method and system in a way that may be easily implemented in a computer program structure. The use of weights and other analyzing tools known to the skilled person may also be applied. The predetermined rule set may thus be changing over time and in accordance to client needs and the type of fraud indication service provided. Alternatively or as a supplement the step of determining comprises applying a predetermined predictive model onto said information. Thus, not only are events and data combined to predict the possibility of fraud, the complexity inherent to the predictive model is not detrimental to the prediction speed of the inventive system and method, because the calculations and determinations based thereupon may be performed progressively by the method and system according to the invention.

In a further embodiment of the method according to the invention, the step of determining comprises applying one or more cross references onto said information. Accordingly, the events tracked and other information retrieved may then be interrelated and correlated, which further improves the behaviour profile determination, providing further flexibility and reliability.

In another embodiment of the method according to the invention, the step of determining comprises comparing a set of recently tracked events with an event pattern history, i.e. a predetermined selection of previously tracked event sets and their corresponding resulting fraud risk potential, respectively, and if there are one or more positive matches, reducing or increasing the fraud risk potential by for instance multiplying with each corresponding resulting fraud risk potential or by using any other relevant mathematical algorithm. Accordingly, an event history inventory may be provided, e.g. by storing the more significant event histories, either for indicating positive fraud or the opposite, manually or automatically, which improves and supplements said determination.

In a further embodiment of the method according to the invention, it further comprises the step of displaying said at least one behaviour profile, for example to said client. In order to make a qualified determination of possible fraud, the behaviour profile is made available to an analyst, which analyst may be provided by the fraud system provider or the client. The behaviour profile may be displayed continuously, or at certain predetermined moments before, during and after the sale process, or even at a client specified time, e.g. providing a summary of all customers or payments and their associated behaviour profile or the like. The behaviour profile or profiles may be displayed via a separate or the same website, or via a separate link, or in other ways, such as by e-mail or SMS-message.

In yet another embodiment of the method according to the invention, the step of providing information comprises tracking customer induced events within a predetermined time interval. Thus, the fraud determination method and system is available in client and/or system provider selected time intervals, e.g. only during daytime in the clients country for a client reaction to fraud to be timely, or only when the customer is present on the website, thus system use time may be reduced leading to increased client satisfaction with the available fraud determination service, and increased possibility of using system time elsewhere.

In another preferred embodiment of the method according to the invention, said predetermined time interval lies between a first moment, when said customer is entering said at least one website and a second moment, when a transaction has been authorized. Thus, all stages of a specific sale may be surveyed, and a plurality of events may be tracked, such as customer behaviour before, during, and after being on the website, e.g. by tracking the event "customer reads the merchant's e-mail receipt" confirming the sale.

In a further embodiment of the method according to the invention, said predetermined time interval lies between a first moment, when said customer is entering said at least one website and a second moment, when said customer is leaving said at least one website again. Thus, the customer induced events are the actions performed by the customer on or around said website, which may be advantageous in localities, where regulation dictates privacy laws, which reduces the possibility of surveying or tracking customer behaviour by the method according to the invention.

In a further embodiment of the method according to the invention, said predetermined time interval lies between a first moment, when said customer is entering said at least one website and a second moment, when the purchased item has been made available to said customer. Thus, the customer induced events are not only the actions performed by the customer on or around said website, but also actions not directly related to said website, such as making a phone call to the clients customer support or rapid use of the entire value of a purchased phone card.

In another embodiment of the method according to the invention, the client is a merchant, which provides said at least one website for offering a customer services and/or products in a direct sale. Alternatively, clients may be an organisation, who provides a website for donating charity, or a financial institution providing a possibility of transferring funds, or the like. The term "payment" are thus not to be conceived as a payment in order to receive a web-based virtual or real product or service in the normal sense of the term, but comprises the acts of the customer ordering a funds transaction and the merchant receiving the validation thereof.

In a preferred embodiment of the method according to the invention said client is providing a payment solution for said at least one website. Thus, in the case where the functions of the payment per se have been outsourced to a payment solution provider, such as a financial institution like a bank, a credit card provider, or a loan agency, or such as a private company acting as the third-party risk taker, and/or as the connection between financial institution and merchant, the method and system according to the invention is provided, e.g. as an add-on for the payment solution providers websites or indeed for the websites, which these payment solutions providers are servicing. Thus an increased number of websites may be surveyed at any one time, and synergy effects may be taken advantage of, such as increasing the number of event pattern histories available, which provides an increased statistical basis, which further increases the reliability of the behaviour profile determination. This increases the need for computer processing power, but this is already a requirement today for such providers, and is to be solved by a simple increase in computer power as it is known to the skilled person.

In a preferred embodiment of the method according to the invention, it further comprises the step of providing at least an indication of positive fraud, i.e. the identification of a fraudulent customer, if said at least one fraud risk potential passes a predetermined threshold or one or more highly suspicious events have occurred. Thus, a human or computer-implemented analyst is not necessary, and is integrally provided. Accordingly, clients may be advised only or as a supplement in the case of a positive fraud situation, and by the same or a separate website, a direct computer line, an e-mail or SMS-message or the like. A display of all or some of the behaviour profiles being determined may also be provided, or alternatively, none are provided, i.e. only positive fraud indications are provided. This reduces the time spent by the client for extracting reliable fraud indications from the fraud indication system.

In yet another embodiment of the method according to the invention said at least one fraud risk potential is providing a binary indication, either positive or negative fraud, such as 1 or 0. Thus, a simple, yet effective indication is provided, either the customer actions indicate he is committing any type of fraud, or they indicate he is not. The above mentioned threshold is thus 1.

In a further embodiment of the method according to the invention said at least one fraud risk potential is divided into levels between predetermined values, such as between 0 and 9. The number and gradations of levels being used are not important, but a graded potential leaves the possibility of providing a further graded fraud indication, such as providing e.g. red, yellow, green areas, i.e. fraud highly likely, less likely and not likely. Many other gradations may be used, all which may be conceived by the skilled person. The above mentioned threshold may thus be determined according to application or client desire.

In a preferred embodiment of the method according to the invention, at least part of the system events, calendar events, data concerning the payment, the predetermined rule set, and/or cross-references are provided by the client. Thus, these data change according to client needs, and the fraud indication is substantiated by the client communicating or entering in such new or modified data. Accordingly, a flexible determination is provided, since these data may be created, modified, and improved iteratively manually or automatically, e.g. also based on the information being gathered by the method and system according to the invention, and the information provided by the client, both by behaviour on the website and by entering transaction and personal data. Said means for providing client data may be a computer program for performing the method according to the invention running on the client's server or web server, which program is in communication with the fraud system provider's server or web server.

In a further embodiment of the method according to the invention, said at least one website is provided on the Internet. This is an advantage, as the Internet has now becoming accessible to many customers through the use of many different interfaces, such as home or work based computers, mobile phones, PDA's, touch screen applications in home appliances etc. Alternatives are websites on an Intranet, or on other types of LAN's or WAN's.

In a further embodiment of the method according to the invention, a first behaviour profile is determined in order to indicate fraud in relation to a specific transaction using transaction identification. Thus the fraud indication is transaction specific, and indicates customer fraud in relation thereto. Alternatively, other types of behaviour profiles are conceivable, such as customer fraud in relation to several different clients collectively, e.g. one customer being committing bank fraud by switching assets between banks on their websites, or such as affiliate fraud in relation to an affiliate of the client. Thus, different types of behaviour profiles may be determined which expands the power and width of the system and method according to the invention.

In an alternative embodiment, a second behaviour profile is determined in order to indicate fraud in relation to a specific customer using customer identification. Thus, the fraud indication is related to the customer by using a customer ID, e.g. a customer identifying ID-number provided in a cookie, or the customer entering a customer unique code before entry to the website in question, or the like, as is known to the skilled person.

In a further embodiment of the system according to the invention, the system is provided substantially as part of or in connection with said client's website or websites, for example in the web server for said at least one website. Thus, the client is also the fraud system administrator and this provides the opportunity to adapt the system for a consistent fraud determination, and all updating and parameter input is controlled and maintained by the merchant himself, keeping down system maintenance costs.

In a preferred embodiment of the system according to the invention, the system is provided substantially as part of or in connection with a web server being provided by a fraud system provider. Where this provider is an entity external to the client, maintenance, storage, processing, updating etc. are alleviated for the client. In addition, centralization of the fraud detection process allows multiple clients to pool information resources, thereby enhancing the reliability of the indication of fraudulent customers.

In a further embodiment of the system according to the invention, the system is provided entirely externally to the merchant's at least one website and in communication with it. The fraud system provider is thus allowed full control over the two parts of the system and it is not necessary for the client to perform neither maintenance nor event tracking for a reliable fraud indication. The system may be provided as a "black box" service, for example, by the external fraud system provider, which eases the maintaining and developing of the system for the fraud system provider, but requires a well established communication with the client for responding to client needs and requirements for the system.

In a further embodiment of the method according to the invention, the method and system are used to detect fraudulent behaviour in relation to mobile telecommunication systems, wherein customers use mobile telecommunication devices, such as cell phones, for phone calls and transferring text messages, pictures and video sequences. A behaviour profile is determined substantially continuously, i.e. at least after each customer induced event in relation to said at least one mobile telecommunication network, by monitoring the behaviour of said customer on said at least one mobile telecommunication network and analyzing said behaviour by applying said real-time behaviour classification algorithms to said at least one customer induced event, thereby providing at least one substantially time variable continuous behaviour profile. This behaviour profile can be displayed to the client, who in this case is the provider of the telecommunication service, and said client can then be warned, when there is a potential misuse so that corrective actions can be taken to prevent further misuse.

Typical data, that are tracked and analyzed in relation to a given customer induced event, are the time, duration and cost pr minute of a given call and the location of the customer and the location of the person receiving the call. Also the number of calls made over a given period of time and the number of text messages or pictures or video sequences send from the mobile telecommunication device within a certain period of time can be used when determining the behaviour profile and the fraud risk potential. The system furthermore records various characteristics for a given customer such as commonly used phone numbers. The behaviour classification algorithms can also analyse the use of the any additional features that the mobile telecommunication device is equipped with, such as a photo camera, a video camera, an FM radio and a music player such as an mp3 player. The protection against fraud can also be improved by using location-based tracking of the mobile telecommunication device to provide information on where said customer works and socialises, and Bluetooth pairing can be used to generate a profile of who the user socialises with. The behaviour profile and the fraud risk potential also react to data concerning customer behaviour that is not directly related to the mobile telecommunication network, such as phone calls between said customer and said client's customer support. The system is a stand-alone system that can be integrated in existing telecommunication systems.

In a further embodiment of the method according to the invention, the method and system are used to detect fraudulent behaviour in relation to a network for credit card payment, where said customer induced events comprise the withdrawal of money from a bank account, the payment in a shop, the use of the credit card as safety in a hotel, and credit card payment over the internet. The fraud risk potential and the behaviour profile are determined by tracking and analysing various data concerning the customer induced events, such as the number of purchases made per day, the character of the purchased goods, the time of purchase, and the location of purchase. The safely of the system is improved by cross-referencing with customer specific data, such as customer's profession, age, country of residence, travel frequency, hobbies, and interest. The behaviour profile and the fraud risk potential also react to data concerning customer behaviour that is not directly related to the credit card payment network, such as phone calls between said customer and said client's customer support. The system is a stand-alone system that can be integrated in existing credit card payment system. In a further embodiment, the method and system, and computer program for performing said method can also be used for characterising the behaviour of a given entity in relation to a website, wherein customized display of advertisements and goods is requested. The behaviour classification algorithms are in this context used to classify a given customer's interest in specific types of advertisements and/or goods available on this at least one website, to predict whether said customer is inclined to click on specific advertisement links shown on said at least one website or by specific types of goods.

It will be appreciated, that the system and method disclosed herein relate to alternate divisions of the systems components between the client and the external fraud processing entity and that every possible combination is contemplated no matter how the division of the system component parts and method steps are made.

In the following the invention is described in more detail with reference to the accompanying schematic drawings, in which:

Fig. 1 is an overview illustrating of how known systems for indicating fraud in relation to network based sales have focused upon the transaction process per se;

Fig. 2 is an overview of how a system according to the invention for determining fraud in relation to network based sales is able to focus upon the entire sales process;

Fig. 3 is a overview of a preferred embodiment of a system according to the invention being applied to a merchant's website; and

Fig. 4 is a view of a time line indicating tracking of customer activity on a website according to one embodiment of the method according to the present invention. Behaviour analysis for the sales process is known in relation to sales in the real world. US 2005/102183 describes a monitoring system and method for the brick- and-mortar world, which based on information prior to the point of sale about a shoppers location and behaviour prior to approaching the check-out counter is used at the point of sale for comparing a list of acquired items with a list of purchased items. However, this system does not solve the above mentioned problem on the network, because this action is irrelevant for a web based system and thus this system and method are not applicable. Further, this also is applied at the moment of transaction, reducing the scope and time period for affirmative action.

Within different types of risk based industry, such as the insurance industry, it is known to perform risk analysis such as event sequence studies by providing time lines over different significant events in order for analysts to identify fraudulent customers. However, as far as the applicant is aware, this approach has not until now been applied when analyzing for fraud during sales on the Internet.

As is shown in fig. 2, the method and system according to the present invention is able to provide for instance a reliable identification of website customers, which are likely be fraudulent, during all phases of the sales process, which identification method and system will be explained further below.

In the following, the invention is initially described in relation to detecting fraud in relation to a network or a website for ecommerce. It is to be understood, that the method and system, and the computer program for performing said method according to the present invention can be applied to characterize the behaviour of a given entity in variety of different electronic entity action based systems, such as credit card payment systems, telecommunication systems for cell phone communication, websites for e-commerce activities, and the like. The following describes the situation, where the electronic entity action based system is a merchant's website, wherein services or products are brought into sale. A merchant may be a provider or seller of goods or services on said website, interactive promotional activity, such as an e-mail or SMS promotional and sales activity, to which the customer is looking to acquire these products or services. A merchant may also be a financial institution, such as a bank or card provider. In this situation, the entity is a customer who is visiting the website in question, i.e. a potential or actual physical or virtual buyer of the service/product, which customer may be another merchant or even an automated system or application performing fraudulent acts on the website, such as an application for using a stolen credit card number for purchases as fast as possible on as many sites as possible. In the following, a client means a client to whom the service of indicating possible fraud for a web based sale is provided, the client often being a merchant selling products or service or alternatively an organisation, who provides a website for donating charity, or a financial institution providing a possibility of transferring funds, or the like. In a preferred embodiment, said client is a provider of a payment solution for said at least one website. The client may further be a website provider of one or more merchant's website or -sites. Further, there may be more than one client for each fraud indication at a time, e.g. fraud may be indicated to the payment solution provider and the merchant, who the payment solution provider is servicing, or alternatively, the fraud indication is provided to a predetermined group of merchants, whom the payment solution provider is servicing.

The term "payment" is thus not to be conceived as a payment for a virtual or real product or service in the normal sense of the word, but comprises all or part of the acts being performed, when the customer is ordering a funds transaction for the merchant to receive the validation thereof.

The term "tracking an event" is intended to mean to collect and record type and e.g. duration of event, possibly including a time indication, such as time of day, and/or at least beginning and/or end of the duration, where an event is a an occurrence in a particular place, e.g. on a website or in an email system, during a particular time interval. Further data concerning the event may also be recorded during tracking, which data are depending on the event being tracked and may comprise e.g. a unique ID number for customer and/or purchase in question, system condition data, etc.

In fig. 3 is shown an overview of a computer-implemented system 100 according to one embodiment of the invention. Generally, the system 100 is consisting of two parts, a data tracking part (indicated by wide arrows) provided integrally within or in relation to a merchant's website 450, which part is providing data for a data analysis part 110, which part is provided as a service from e.g. a fraud indication service provider 1100. Obviously, other embodiments are conceivable, in which the entire system is tracking and analyzing on and for one specific merchant's website, or where data is collected and analyzed externally from said website by said service provider, or intermediate solutions thereof.

Said fraud indication service provider 1100 is applying his part 110 of the system 100 as a computer implemented service to a merchant or client 300 in order to analyze for and determine fraud risk in connection with the customer performing a network based purchase on said merchant's website 450. The service provider 1100 is using a server to implement the system 100, e.g. using a web server for providing a fraud indication website per se, which is accessible only to those merchants, which e.g. subscribes or in other ways are paying for this service.

As shown in fig. 3, the system 100 comprises means 105 for providing information concerning the behaviour of a customer 200 by tracking customer induced events 120 over time on or in relation to a given website 450 being provided by a merchant 300 on the Internet 400. "On or in relation to" is to mean that sales activities, which are not directly performed upon the website 450, may also be tracked by the system 100, such as the registration of an e-mail confirmation of the sale has been read by the customer 200, or the registration of a telephone call made by the customer 200, e.g. asking about the time of arrival of the product/service, or the like.

Said means for determining said at least one behaviour profile substantially continuously comprises tracking said customers use of the mouse pointer and the corresponding movement of the curser on the display used for viewing said at least one website, and by detecting said customer's typing speed and/or number of corrections made while the entering text on said website, and by detecting whether said customer scrolls to the bottom of said website, and/or reads the receipt of the purchase order and/or prints the receipt of the purchase order, and/or resizes the browser window used for displaying said at least one website or not.

Said means for determining the behaviour profile of said customer comprises means for monitoring the behaviour of said entity prior to the transaction to process the electronic purchase order and/or during the transaction to process the electronic purchase order and/or after the transaction to process the electronic purchase order. Thereby a behaviour profile can be determined even if the customer does not initiate a transaction to process an electronic purchase order or alternatively, the system and method can prevent said customer from entering specific parts of said web site, such as keeping fraudulent customers away for, the part of said web site concerning for instance credit card payment. The system and method according to the invention can also track and monitor the behaviour of said customer in relation to said at least one website after the transaction is final and the purchased item is made available to said customer, thereby providing a method for indicating to said client if a transaction that initially appeared trustworthy in fact is fraudulent or may be fraudulent, so that for instance the actual capture for the funds that were previously authorized can be rejected in order to avoid chargebacks or the shipping of the purchased items can be stopped. Other parameters that may be evaluated are from which website said customer entered said client's said at least one website or if for example phone calls between said entity and said client's customer support were made.

Various techniques can be applied to acquire data regarding the customer's activities in relation to said at least one website such as placing a "sniffer" in front of the web server of the e-commerce site and/or gathering information from online log files from the web server hosting the ecommerce website, said log files comprising all movements on said website and/or collecting event information directly on the entity's own PC through for example via a plug-in on the clients web browser.

Said means for tracking and reporting of the system 100 of customer induced events 120 on the website 450 may be provided and applied in different ways known to the skilled person. For example, the events may be recorded and sent via a HTTPS, i.e. a secure website connection to the fraud indication service provider 1100 by means of a XML-document, e.g. in HTML code. Alternatively or as a supplement, a predetermined developed Application Programming Interface (API) is installed on the client's web server, and the client sets up and configures his website pages 451 - 454 accordingly. This helps the client in installing and adapting the tracking of customer induced events to his particular needs and the website at hand, and relieves the effort needed to be performed by the API application developer, i.e. the fraud indication service provider 1100. This also allows for a local tracking being performed in conjunction with the website per se, and for said website code to transmit individual or batches of customer events having been tracked by said website to the service provider for an analysis being performed there.

As seen in fig. 3, the customer 200 is presented with a website 450 on the Internet

400 when e.g. using a web browser on his or her computer and providing the merchant's web address to said browser. The website 450 is provided by a merchant 300, who maintains and runs it via a web server, which site 450 contains four web pages: Terms 451, Sales 452, Payment 453, and Acknowledgement 454. It is to be noted, that the website or websites being surveyed by the system 100 may be provided in many different ways, e.g. not comprising any of these types of pages, but instead a single page or a plurality of interrelated or linked websites, or any conceivable collection of pages and functionalities provided therein.

The position and extent of said event tracking means may be decided by the application in question, and may be selected, maintained and adapted accordingly over time by the merchant or client 300 and/or the fraud indication service provider 1100.

For example, one event being tracked, i.e. surveyed and recorded, by means of customer event tracking may be the fact that the customer is opening the Terms page 451 and the duration thereof, e.g. in combination with the event of the customer reopening the Terms page 451, which may indicate a serious and non-fraudulent customer 200. The position surveyed on the website page Sales 452 or trigger to track an event by the system 100 may be to track total number of times, the customer 200 has selected by clicking one or more specific product items, e.g. over a specific time interval or in total. Type and number of product item selected, and time interval between selections of said specific product items may be registered and sent e.g. in batches to the fraud indication service provider 1100.

Other examples of customer induced events 120, which may be tracked, are the following, where the list is not to be perceived as being comprehensive, and further examples are provided below: The customer 200 has

• entered the website page containing the refund policy

• visited the same website more than one week ago

• entered the credit card number incorrectly

• spent more than three minutes on the payment website page • read the merchant's e-mail receipt • contacted the merchant's sales support department.

Further, the system 100 comprises means for providing further information concerning the payment itself, e.g. personal customer data 130 provided by the customer 200, and transaction data 150C, 150B provided by the customer 200 and/or merchant 300, respectively, and concerning merchant and/or service provider predetermined environment events such as system events 140A, e.g. down times for the website or for the fraud indication system, and calendar events 140B, such as

Christmas time or promoted sales, which increases the number of items purchased in each sale and in total sales.

Information concerning the payment itself is in fact optional, as mentioned above, and may comprise data such as a unique sale ID number, total amount of sale, customer IP address, customer name and postal address for shipping, credit card type and number being entered, and the like, as is known to the skilled person. These data may be supplied by tracking what the customer 200 is entering into the website 450, e.g. on webpage Payment 453, or alternatively or as a supplement be supplied from the merchant's own payment registry (not shown) of each sale, which registry may be in communication with the website 450. Accordingly, in addition to the tracking, a check of these data may be supplied to increase the reliability of the fraud indication, and may provide a basis for a wider selection of events which may be tracked, see above.

Data concerning type and duration of system events 140A may generally be provided by the system manager, i.e. the fraud system provider 1100, but the system events may alternatively or as a supplement be provided by the client 300, if the client is administering all or part of the fraud indication system 100. These system events

140A may for example comprise the duration and time of scheduled updates of the system 100, time periods of merchant's opening hours, periods of expected high or low activities originating from different countries, etc. By providing the system 100 with information concerning system events the determination of the fraud risk potential may be made independent of e.g. system down times.

Data concerning type and duration of calendar events 140B may be provided by the client, i.e. the merchant 300, but may alternatively or as a supplement be provided by the system provider 1100, or inherently as variables or constants in the computer program product. These calendar events may for example comprise expected high sales periods, or low sales periods, or periods in which certain items are known to be sold out or the like. By providing the system 100 with calendar events 140B, the resulting reliability of the fraud risk potential may be increased, and thus the risk of indicating fraud erroneously is decreased. The calendar events 140B may be automatically or manually provided by the merchant 300 to the service provider 1100 upon entry into the system by a simple indication or entry on the fraud indication website of the service provider 1100, or by any other means, such a telephone, mail, SMS-message or the like.

Further, cross references of different types may be used between the different types of data in the system 100 concerning events, e.g. to simultaneous or earlier tracked data concerning the customer or the sale, for example comprising e.g. registration of a change in IP-address, change of country, shorter or longer absences from the website, number of non-concluded sales etc. For the skilled person, about 10-20 different types of very effective cross references are known, and other may be conceived, based on type and amount of the tracked data supplied by the system 100. This provides a wider selection by providing event combinations, based on which the system 100 is able to identify fraudulent customers.

Then, on the basis of the different information 120, 130, 140A, 140B, 150B, 150C, or system provided information an analysis is performed by the system 100 for determining a fraud risk potential 180 in order to identify fraudulent customers. The fraud risk potential determination may be performed in different ways. Preferably, a timeline event sequence analysis comprising rule sets is applied to the events registered by the system 100, previous customer behavioural history, cross references, and optionally checks on payment details. However, as mentioned initially, other analysis methods known to the skilled person are available, such as applying rule sets exclusively, and/or predictive modelling, and/or and the like. The timeline sequence analysis, can also analyse the behaviour of said customer by monitoring which at least one websites said customer visits and the customer's actions on said at least one website.

The recorded events and input data may be entered into a timeline for an event sequence analysis and used with a predetermined and/or adaptive rule set, e.g. by assigning weights to each event or combination of events, which have taken place. Another alternative or supplement is to use predictive modelling schemes which may provide a fraud risk potential even before the customer has performed a clearly fraudulent act. Events may be assigned different weights in the system. The rule set comprising the rule that a specific event is e.g. indicating positive fraud in all circumstances, another that a (later) event indicates no fraud at all.

The predetermined analysis, using e.g. rule sets and/or predictive modelling may be provided as a default in the system, e.g. as part of the computer program product implementing the method according to the invention, or may be created and/or changed according to specific customer needs and payment experience, and may be created/changed by input provided by the fraud system provider 1 100 and/or the client 300.

An example of a piece of pseudo code for analysing the behaviour of the customer can be structured as shown below:

CalculateScore: events := get event set email := extract email from events name := extract name from events similarity := calculate similarity of name and email similarity factor := determine a factor from similarity checkout speed := calculate time spent from 'presented to the credit card form' to 'credit card number received' from events previous chargebacks := check if previous chargeback has been registered in events neural factor := let a trained neural network operate on the events and return a factor score := ( similarity factor + checkout speed constant ^Λ (1 /checkout speed) + chargeback constant * logl0(previous chargebacks) ) ^Λ neural factor

In order to illustrate a preferred embodiment of the method and system for use in relation to an event sequence analysis, a time line as shown in fig. 4 which will be described in further detail below, is set up concerning a given, e.g. new customer's behaviour by a series of tracked events for an event sequence analysis. The analysis comprises comparing recently tracked events with an event pattern history 180 while applying rule sets comprising weights to some or all events and cross references therebetween. By having access to event pattern histories, i.e. a predetermined selection of previously tracked event sets, each indicating a corresponding probability of a fraudulent act or resulting fraud risk potential, the system 100 is provided with further information as to whether a customer is real or fraudulent. Said event pattern histories may for example be provided from one or more clients, and/or from the fraud indication service provider 1100 on the basis of events from one or more merchants websites. A series of events tracked from said new customer may match one or more of these patterns, and each such pattern being matched increases or decreases by the corresponding resulting fraud risk potential. The event pattern histories may be predetermined by an automatic process within the system defined by the client/and or service provider, or manually by the client and/or service provider, e.g. based on weekly, monthly and/or yearly experiences.

In a preferred embodiment, the fraud risk potential 180 and the behaviour profile itself is time variable, i.e. they vary over time as a consequence of it being determined by analysis one or more times, e.g. substantially continually or intermittently over a given time interval. The fraud risk potential 180 may be determined continually or at least one or several predetermined moments in time, as is shown in fig. 4. These moments in time may be right before or after authorizing a credit card, or it may be before the merchant has sent or shipped the service or product, respectively. A continually provided fraud risk potential is an advantage, for example when the system 100 is provided entirely within the client's web server, or when being in continual communication with the client's and/or the service provider's web server or server in order continually monitor the customer or customers visiting the website. In one embodiment, an intermittently provided fraud risk potential and behaviour profile can be determined intermittently upon request from said client or a service provider, which is advantageous for example, when e.g. the analyzing part of the system 100 resides externally from the client's web server or server, in which case the fraud risk potential and/or the positive or negative identification of a fraudulent customer is provided intermittently based on batches of system provided information from the tracking part of the system 100.

The resulting fraud risk potential 180 is a combined value to be regarded as representing the probability of fraud. It 180 may take different forms, depending on preference of the client or provider of the website in question and the application of the system 100. In fig. 3 and 4, it is shown that the potential 180 is graded into several levels, presented as having ten different levels, e.g. from level 0 to level 9, indicating low fraud probability and high fraud probability, respectively. The potential may alternatively be provided with only two levels (not shown), i.e. binary, indicating fraud or no fraud, respectively. Other ways of presenting the risk fraud potential are known to the skilled person, e.g. with a red, yellow, and green zone (not shown), indicating high, medium and low probability, respectively, and depends on preference of the client or provider of the website in question, and the application of the inventive system, which provides the system 100 with flexibility of display and the possibility of evaluation differentiation.

If the resulting fraud risk potential 180 is determined at a level above a predetermined threshold, an indication of positive fraud or identification of a fraudulent customer, is communicated (indicated in the right hand side of fig. 3 by the broad dark arrow) to a client, this being the merchant 300, which provides the website 450 to be monitored for fraud. He is a client of the fraud system provider 1100, which delivers a service consisting of the results from the fraud indication system 100, i.e. the displaying of the fraud risk potential 180 for each of the presently logged on customers, and an alarm indication and customer identification if one of these fraud risk potentials 180 is exceeding a predetermined threshold value. These results, which may comprise relevant check and alert information, and may comprise one or more positive fraud indications, one or more fraud risk potentials of different types and for different customers, customer ID's, transaction ID's, payment amount, etc., and combinations thereof, may preferably be sent to the client subscribing or paying for the fraud service provided. There are many ways in which these results may be sent to the client, comprising a client accessible web page, e.g. using a code or password, or by displaying the results in the merchant's website to be fraud tracked, or by a communication line to the client web server or computer network, or sending an e-mail or an SMS or the like to the merchant's sales department, or similar way known to the skilled person.

The fraud risk potential or the indication of positive fraud, i.e. the identification of a fraudulent customer may be communicated in different ways, either as reactions to a client query to the service provider or as an automatically timed or positive fraud indication induced reminder sent from the fraud indication system 100 to the client. Preferable examples hereof are the client part of the system queries the provider part of the system before or during the customers credit card is being authorized, i.e. before the transaction is being settled, or the provider part of the system reminds the client part of the system after the transaction is settled. In the first two cases, the customer may after being identified by the system as a potentially fraudulent individual, be rejected by the website in order to hinder a charge-back. In the latter case, the merchant must perform a refund of the customer in order to prevent a charge-back. In all cases, the fraud risk potential determination may after such specific point in time, based on subsequent tracked events, alter accordingly.

Thus, the client is equipped with information to hinder, or ultimately invalidate a transaction, sometimes even before it has been initiated, or at least during or after the transaction has been sent to a transaction authorisation, or alternatively after the transaction has been authorized, but before the product/service has been shipped. The system and method of the present invention, and the computer program on a storage medium for providing the code, which when run is employing it thereto, is unique in that it is able to provide the fraud indication information at every stage in the sales process. As far as is known to the applicant, no other web based system or method uses customer behaviour analysis applicable to all stages of the sales process, e.g. in particular before a transaction is initiated or after the sale has been consummated.

The system 100 may be implemented in different ways. In a first option, as described above in relation to fig. 3, part of the system 100 resides within a fraud indication system provider's 1100 computer, e.g. being a web server, continually collecting data from one or more websites 450 of one or more merchants concerning the behaviour of one or more customers etc., and part of the system resides within the web server of each individual merchant 300, e.g. providing data concerning customer e-mails received, confirmation e-mails sent, and e-mail receipts resulting from these sent e- mails by said merchant web server. The second option is that the system may be provided as computer implemented code implemented in communication with or directly on the web server of the client 300, i.e. operable entirely within the client's or merchant's web server. In this case updated data such as a new or updated rule set, input of future system or calendar events and the like, may be provided by the client 300 himself and/or the fraud system provider 1100. This option is advantageous, where the system is operable entirely within e.g. the client's or merchant's web server, or the fraud indication system provider's web server, in the latter case only if it is the same web server as the client's or merchant website is provided within.

The third option is to provide the system substantially entirely within the fraud indication system provider 1100's server, i.e. externally from the clients web server. The system may for example be residing as an executable program on a web server, where also the fraud risk potential 180 is determined and displayed, and where only positive fraud is communicated to the client, optionally together with information identifying either the customer or the transaction which was deemed fraudulent. However, this may reduce the number of customer induced events, which are trackable by the system.

In a preferred embodiment of the system and method, the fraud indication system is an add-on application to a conventional payment site, e.g. as is provided for and/or by a payment provider such as the Danish company DIBS by their Internet based payment solutions. Accordingly, the client is the payment provider, and the fraud indication is displayed to this client and reacted to by him on behalf of one or more merchants on whose behalf he is providing the transaction authorisation, e.g. by installing the tracking part of the system upon all the merchant's websites being serviced. The fraud indication according to the present invention may then advantageously be done independently from and in relation to with the transaction authorisation for each individual transaction. Further, a system and method is provided with the ability to handle information from more than one website at a time, either from the same client or merchant or from different clients or merchants.

In fig. 4, the system and method according to the invention is illustrated by the use of a time line, e.g. for use with an event sequence analysis, which by using crossing lines is indicating customer behaviour induced events El, E2, E3, E4, E5, E6, E7, E8, E9, ElO originating from one customer in relation to a clients website 450, which provides direct sale and which is being tracked by said system and method.

Briefly described, the time line indicates the tracking of a number of said events over time before, during, and after e.g. a transaction T in relation to a payment performed by said customer on the website on the Internet. These discrete events are transmitted to said fraud system provider 1100's server by surveying the merchant 300's website 450 as described above. When desired by the client of the fraud system provider 1100, the system determines and displays a fraud risk potential, FRP(t₀) - FRP(tio), i.e. an indication of the probability of fraud, based on these events and optionally also on data relating to the payment and the like, as described above.

At a point in time to, a potential customer 200, by clicking to agree to the terms and conditions of a commercial merchant website being surveyed by the present fraud determination and indication system, is entering into an active tracking mode of the inventive system 100 in use for said website 450, e.g. a website selling telephone cards and services. This starts the predetermined tracking time interval. In the terms and conditions it may be pointed out, that such tracking and fraud indication system is being accepted to by accepting the terms for entering the website. When the customer is agreeing to the terms and conditions, this is event Eo. The time and events taking place is continually being logged by the fraud indication system according to the invention, in order to based on the given rule set to determine a time variable fraud risk potential FRP. As shown in fig. 4, the time variable fraud risk potential FRP(to) is set to a default low level indication, as indicated with a line, because the customer entering the website is new, i.e. is not previously known to the system. The identification of the customer 200 as performed by the system may be applied in different ways known to the skilled person, e.g. the customer being provided with an ID-cookie, or registration of his computer's IP-address. This may also be agreed upon by customer acceptance of e.g. a terms agreement before entering the merchant's website.

Alternatively, different predetermined time intervals may be chosen, which are suitable for the application for which the fraud system is used. One example is a continuous or intermittent surveying of customers entering and exiting the website, another is a continuous surveying of a potentially fraudulent customer, e.g. over a collection of different websites being serviced by the same or different fraud system providers 1100. Other time intervals may be decided upon according to client specification and fraud system performance and availabilities.

At a point in time ti, see fig. 4, the customer initiates a purchase in event Ei by clicking on a displayed item for sale, e.g. one hundred more SMS-messages to be downloaded at his mobile telephone number. The click at ti and the item chosen is recorded by the system when tracking customer behaviour. The determined level of the time variable fraud risk potential FRP(tj) may be depending upon the time period to - t_ls and the type and number of item chosen, and also on the rule set being applied at that moment in time, and for that specific website.

Afterwards, during a period t₂ - U in event E₂ , see fig. 4, the customer is entering in his or her transaction details for payment for downloading the selected one hundred SMS-messages. During this period, at the point in time t₃, in event E₃ the customer enters in a credit card number for payment of the one hundred SMS-messages, which is recorded by the system as data concerning the payment. At t = U and event E₄, the customer enters in an e-mail address for contact and a validation of the transaction for concluding the payment is performed. At t = t₅ and event E₅, the customer leaves the website in question.

Shortly afterwards, at t = t₆ and event E₆, the same customer re-enters the same website, at t = t₇ and in event E₇ he again clicks to purchase one hundred SMS- messages. The customer is identified by the system as the same person, who entered a short while ago, e.g. by being assigned a customer ID or by his IP-address, as explained above. At t = t₈ and event E₈, he enters a different credit card number from the one used at event E₃, and at t = tg and event E₉, the fraud indication system indicates to the client, i.e. the telephone card provider, that the customer is highly likely to be fraudulent, e.g. by sending an indication to the client webserver. This automatically halts the sale and informs the customer of the action being taken, for example also politely and informatively indicating the reason therefore. This indication may alternatively be transmitted by the merchant's sale department following the halt of the sales process, or before shipping the item to the customer, i.e. before downloading to the customer's mobile phone the possibility of sending one hundred extra SMS-messages, as described above.

At t = t₁₀ and event E_1O, the customer sends an e-mail to the website contact address and explains, that the credit card number used for the last transaction was typed in with an error in it, and the website fraud indication system is able, based on website merchant feedback, to reduce the transaction first fraud risk potential FRP(tio) to a low level, and thus restart or resume the sale by allowing the customer back upon the website.

As was indicated above, more than one website may be tracked using the method and system according to the present invention. This is in particular advantageous in the case where the client is a payment service provider or for the fraud system provider, which may then e.g. be able to correlate information between the websites and thus provide a more reliable fraud risk indication. More than one fraud risk potential, FRPi(t₉), FRP₂(t₉), 180A and 180B respectively in Fig. 4 may be determined, and indicated to one or more clients of the fraud tracking system at the same or different moments in time.

This may be a first potential FRP ifø) 180A for indicating fraud risk in relation to and corresponding to the financial transaction in question, e.g. after event E6. Accordingly, a high risk of customer fraud may be indicated based on among others the event of the same customer using different card numbers to purchase items. Further, a second potential FRP₂(tg) 180B may be determined for indicating fraud risk in relation to the customer in question, e.g. there may be a high risk that the customer is sending spam SMS-messages. This may be indicated, based on among others the fact that e.g. he was attempting to purchase one hundred SMS-messages for the same phone number within the relatively short time period to - t₉.

The step of determining said behaviour profile comprises analyzing data concerning the behaviour of said customer in relation to said website, and data concerning environmental events, comprising system and/or calendar induced events such as festive seasons and national holidays. Said data furthermore comprises data concerning the payment, comprising transaction data and/or data concerning said customer.

The information made available by tracking a number of events can be analysed using behaviour classification algorithms that may comprise one or more predetermined rule sets and/or predetermined predictive models and/or at least one cross reference onto said information. The step of determining the behaviour profile also comprises combining information regarding the present interaction with said at least one website with previously recorded behaviour profiles of said customer or with previously recorded behaviour profiles of an ensemble of customers that previously have visited said at least one website or other websites monitored by said real-time behaviour classification algorithm based method. S least part of the system events, calendar events, data concerning the payment, the predetermined rule set, and/or cross-references are provided by the client.

Determining the fraud risk potential may also involve comparing a set of recently tracked events with an event pattern history, i.e. a predetermined selection of previously tracked event sets and their corresponding resulting fraud risk potential, respectively, and if there are one or more positive matches, reducing or increasing the fraud risk potential by for instance multiplying with each corresponding resulting fraud risk potential or by using any other relevant mathematical algorithm. Accordingly, an event history inventory may be provided, e.g. by storing the more significant event histories, either for indicating positive fraud or the opposite, manually or automatically, which improves and supplements said determination.

The disclosed system furthermore comprises steps for displaying on an interface said at least one behaviour profile, for example to said client, where said interface can be an XML interface and/or a web interface. The displayed behaviour profile comprises means for indicating both positive fraud, i.e. the identification of a fraudulent entity, and/or negative fraud, i.e. the identification of a trustworthy entity, if said at least one fraud risk potential passes predetermined thresholds. The scale for the fraud risk potential may be divided into 3 intervals between predetermined levels, whereby the two extremes on the scale can be used to indicate either a predominantly suspicious behaviour or a predominantly trustworthy behaviour, and the middle interval can be used to indicate that a manual decision preferably should be made. An indication of positive fraud, i.e. the identification of a fraudulent customer, may also be shown if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said customer induced events appears to be suspicious and/or if said at least one behaviour profile shows that one or more predetermined suspicious customer induced events have occurred. Likewise, an indication of negative fraud, i.e. the identification of a trustworthy customer, may be displayed if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said entity induced events appears to be reliable and/or if said at least one behaviour profile shows that one or more predetermined trustworthy customer induced events have occurred.

The disclosed method furthermore comprises the possibility of automatically rejecting, automatically granting, manually rejecting and manually rejecting said customer the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour. An automatically made decision can be changed manually by the client or the service provider.

The disclosed method and system, and computer programme for performing said method, is a stand-alone method that can be integrated in existing electronic purchase order methods provided on said merchants existing said at least one website

The application of the invention is in particular the partaking of a network based commercial sale, in particular payment for client products or services, e.g. in a direct sale, and/or payment of bills via the Internet, e.g. via a payment solution provider. However, it is well within the scope of the present invention to utilize the inventive system and method for other network based applications, such as traditional catalogue/telephone ordering, telephone usage environments, telephone sales and marketing, and even traditional sales between physical entities, in which interactive communication means such as a website is available. The means for accessing such website is not substantial to the invention, and may comprise a personal computer, a touch-screen application, a mobile phone, a PDA, etc.

It is to be understood, that the present invention may be implemented utilizing any number of computer technologies. For example, although the present embodiments are disclosed as operable in connection with the Internet, the present invention may be utilized over any computer network, including for example a wide area network or a local area network. Similarly, the customer computer and/or client or merchant computer may be any computer device which can be coupled to the network, including for example PDA's, web-enabled cellular telephones, hard-wired telephones which dial into the network, mobile and stationary computers, Internet appliances, and the like. Furthermore, the merchant servers providing the websites to be fraud tracked may be of any type, running any software, and the software modules, objects and plug-ins described herein may be written in any programming language, including HTML language. Lastly, the database and storage devices described herein may utilize any storage technology, including for example local computer memory, network attached storage, both fixed and removable, in any known storage medium, such as magnetic or optical.

Claims

PATENT CLAIMS

1. A computer-implemented method using real-time behaviour classification algorithms in order to identify potentially fraudulent entities in relation to activities in at least one electronic entity action based system, such as a website for ecommerce activities, a system for mobile telecommunication, a system for credit card payment, and the like, by monitoring the behaviour of said entity within at least one of said action based systems, said method comprising the following steps:

- providing input information over time by substantially continuously monitoring at least one of said action based systems concerning the behaviour of said entity in relation to said at least one action based system by tracking a plurality of entity induced events; and

- sequentially analyzing said input information, at least after each said entity induced event for providing at least one behaviour profile, wherein the significance and meaning of said entity induced events substantially dynamical and may change in time; and

- displaying said behaviour profile.

2. A method according to claim 1, wherein said action based system comprises at least one website comprising means for commerce activities, in particular involving payment by transferral of a payment from one bank account to another and/or by credit card payment and/or the like.

3. The method according to claim 2, wherein said at least one behaviour profile, is determined substantially continuously, i.e. at least after each entity induced event in relation to said at least one website, by monitoring the behaviour of said entity on said at least one website and analyzing said behaviour by applying said real-time behaviour classification algorithms to a plurality of said entity induced events, thereby providing at least one substantially time variable continuous behaviour profile.

4. The method according to claim 2 or 3, wherein said entity induced events comprises the entering of entity provided information comprising the information itself and/or how this information is provided by said entity.

5. The method according to any of claims 2 to 4, wherein said at least one behaviour profile is determined substantially continuously by tracking said entity's use of the mouse pointer and the corresponding movement of the curser on the display used for viewing said at least one website.

6. The method according to any of claims 2 to 5, wherein said at least one behaviour profile is determined substantially continuously by detecting said entity's typing speed and/or number of corrections made while entering text on said website.

7. The method according to any of claims 2 to 6, wherein said at least one behaviour profile is determined substantially continuously by detecting whether said entity scrolls to the bottom of said website, and/or reads the receipt of the purchase order and/or prints the receipt of the purchase order, and/or resizes the browser window used for displaying said at least one website or not.

8. The method according to any of claims 2 to 7, wherein said at least one behaviour profile is determined by monitoring the behaviour of said entity prior to the transaction to process the electronic purchase order and/or during the transaction to process the electronic purchase order and/or after the transaction to process the electronic purchase order.

9. The method according to claims 2 to 8, wherein the step of determining comprises registering from which website said entity entered said client's said at least one website and/or acquiring information from for example a "sniffer" placed in front of the web server of the e-commerce site and/or gathering information from online log files from the web server hosting the ecommerce website, said log files comprising all movements on said website and/or collecting event information directly on the entity's own PC through for example via a plug-in on the clients web browser.

10. The method according to any of claims 2 to 9, wherein said at least one behaviour profile is determined substantially continuously by monitoring the behaviour of said entity during the entire period the entity is connected to said at least one website allowing for a determination of said at least one behaviour profile even if said entity exits said at least one website without initiating a transaction to process an electronic purchase order.

11. The method according to any of claims 2 to 10, wherein said at least one behaviour profile is determined prior to initiating the transaction to process an electronic purchase order, whereby steps for preventing said entity from entering specific parts of said website or specific websites can be taken.

12. The method according to any of claims 2 to 11, wherein said at least one behaviour profile is determined by tracking at least one entity induced event in relation to said at least one website after the transaction is final and the purchased item is made available to said entity, thereby providing a method for indicating to said client if a transaction that initially appeared trustworthy in fact is fraudulent or may be fraudulent, so that for instance the actual capture for the funds that were previously authorized can be rejected in order to avoid chargebacks or the shipping of the purchased items can be stopped.

13. The method according to any of claims 2 to 12, wherein a plurality of entity induced events occurs independently of the connection to said website, such as phone calls between said entity and said client's customer support.

14. The method according to any of claims 2 to 13, wherein said at least one behaviour profile is determined at least right before a transaction for a payment is authorized.

15. The method according to any of claims 2 to 14, wherein said at least one behaviour profile, can be determined intermittently upon request from said client or a service provider

16. The method according to any of claims 2 to 15, wherein said information further comprises environment induced events, comprising system and/or calendar induced events such as festive seasons and national holidays.

17. The method according to any of claims 2 to 16, wherein said information further comprises data concerning the payment, comprising transaction data and/or data concerning said entity.

18. The method according to any of claims 2 to 17, wherein the step of determining comprises applying onto said information a timeline sequence analysis, that analyse the behaviour of said entity by monitoring which at least one websites said entity visits and the entity's actions on said at least one website.

19. The method according to any of claims 2 to 18, wherein the step of determining comprises applying behaviour classification algorithms that may comprise one or more predetermined rule sets, onto said information.

20. The method according to any of claims 2 to 19, wherein the step of determining comprises applying behaviour classification algorithms that may comprise one or more predetermined predictive models onto said information.

21. The method according to any of claims 2 to 20 wherein the step of determining comprises applying behaviour classification algorithms that may comprise one or more cross references onto said information

22. The method according to any of claims 2 to 21, wherein said at least one behaviour profile is determined by combining information regarding the present interaction with said at least one website with previously recorded behaviour profiles of said entity.

23. The method according to any of claims 2 to 22, wherein said at least one behaviour profile is determined by combining information regarding the present interaction with said at least one website with previously recorded behaviour profiles of an ensemble of entitys that previously have visited said at least one website or other websites monitored by said real-time behaviour classification algorithm based method.

24. The method according to any of claims 2 to 23, wherein the step of determining said at least one fraud risk potential comprises comparing a set of recently tracked entity induced events with an event pattern history, i.e. a predetermined selection of previously tracked event sets and their corresponding resulting fraud risk potential, respectively, and if there are one or more positive matches, reducing or increasing the fraud risk potential by for instance multiplying with each corresponding resulting fraud risk potential or by using any other relevant mathematical algorithm.

25. The method according to any of claims 2 to 24, further comprising the step of displaying on an interface said at least one behaviour profile , for example to said client.

26. The method according to any of claims 2 to 25, further comprising the step of displaying said at least one behaviour profile , for example to said client via an XML interface and/or via a web interface.

27. The method according to any of claims 2 to 26, wherein the step of providing information comprises tracking said at least one entity induced event within a predetermined time interval.

28. The method according to claim 27, wherein said predetermined time interval lies between a first moment (to), when said entity is entering said at least one website and a second moment (tio), when a transaction (T) has been authorized.

29. The method according to claim 27, wherein said predetermined time interval lies between a first moment (to) when said entity is entering said at least one website, and a second moment (t₅) when said entity is leaving said at least one website again.

30. The method according to any of the claims 2 to 29, wherein said client is a merchant, which provides said at least one website for offering at least one service and/or at least one product in a direct sale.

31. The method according to any of the claims 2 to 30, wherein said client is a provider of a payment solution for said at least one website.

32. The method according to any of the claims 2 to 31, further comprising the step of providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, if said at least one fraud risk potential passes a predetermined threshold.

33. The method according to any of the claims 2 to 32, further comprising the step of providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one fraud risk potential passes a predetermined threshold.

34. The method according to claim 33, wherein said at least one fraud risk potential is providing a binary indication, either positive or negative fraud, such as 1 or 0.

35. The method according to claim 33, wherein said at least one fraud risk potential is divided into levels between predetermined values, such as between 0 and 9.

36. The method according to any of claims 2 to 33, wherein said at least one fraud risk potential is divided into at least 3 intervals between predetermined levels, whereby the two extremes on the scale can be used to indicate either a predominantly suspicious behaviour or a predominantly trustworthy behaviour, and the middle interval can be used to indicate that a manual decision preferably should be made.

37. The method according to any of the claims 2 to 36, further comprising the step of providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said entity induced events appears to be suspicious.

38. The method according to any of the claims 2 to 37, further comprising the step of providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, if said at least one behaviour profile shows that one or more predetermined suspicious entity induced events have occurred.

39. The method according to any of the claims 2 to 38, further comprising the step of providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said entity induced events appears to be reliable.

40. The method according to any of the claims 2 to 39, further comprising the step of providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one behaviour profile shows that one or more predetermined trustworthy entity induced events have occurred.

41. The method according to any of claims 2 to 40, further comprising the step of automatically rejecting or automatically granting said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour.

42. The method according to any of claims 2 to 41, further comprising the step of manually deciding to reject or grant said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour displayed in said interface.

43. The method according to any of claims 2 to 42, further comprising the step of manually being able to change the automatically made decision to either reject or grant said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour.

44. The method according to any of claims 2 to 43, wherein at least part of the system events, calendar events, data concerning the payment, the predetermined rule set, and/or cross-references are provided by the client.

45. The method according to any of claims 2 to 44, wherein said at least one website is provided on the Internet.

46. The method according to any of claims 2 to 45, wherein a first fraud risk potential (FRP i(tg)) is determined in order to indicate fraud in relation to a specific transaction using transaction identification.

47. The method according to any of claims 2 to 46, wherein a second fraud risk potential (FRP₂(Ig)) is determined in order to indicate fraud in relation to a specific entity using entity identification.

48. The method according to any of claims 2 to 47, wherein said method is a stand- alone method that can be integrated in existing electronic purchase order methods provided on said merchants existing said at least one website

49. A computer-implemented system to identify potentially fraudulent entities in relation to activities in at least one electronic entity action based system by monitoring the behaviour of said entity within at least one of said action based systems, said system comprising the following means:

- survey means for substantially continuously monitoring a plurality of entity induced events in relation to at least one of said action based systems; and

- process means for sequentially analyzing based on said information, at least after each said entity induced event, for providing at least one behaviour profile, in order to identify potentially fraudulent entities; and

- display means for displaying said behaviour profile.

50. The system according to claim 49, wherein said survey means comprises real- time behaviour classification algorithms for analyzing said plurality of entity induced events, wherein the significance and meaning of these entity induced events are substantially dynamical and may change in time.

51. The system according to claim 49 or claim 50, wherein said survey means comprises means for monitoring the behaviour of a given entity in relation to a client's at least one website and said process means comprises means for analyzing this behaviour of a said entity in relation to client's said at least one website, said website comprising means for commerce activities, in particular involving means for payment by transferral of a payment from one bank account to another and/or by credit card payment and/or the like.

52. The system according to claim 51, wherein said survey means and said process means comprises means for monitoring and analyzing the entity provided information comprising the information itself and/or how this information is provided by said entity.

53. The system according to claim 51 or claim 52, wherein said survey means and said process means comprises means for monitoring and analyzing said entity's use of the mouse pointer and the corresponding movement of the curser on the display used for viewing said at least one website.

54. The system according to any of claims 51 to 53, wherein said survey means and said process means comprises means for detecting and analyzing said entity's typing speed and/or number of corrections made while the entering text on said website.

55. The system according to any of claims 51 to 54, wherein said survey means and said process means comprises means for detecting whether said entity scrolls to the bottom of said website, and/or reads the receipt of the purchase order and/or prints the receipt of the purchase order, and/or resizes the browser window used for displaying said at least one website or not.

56. The system according to any of claims 51 to 55, wherein said survey means comprises means for detecting from which website said entity entered said client's said at least one website and/or means for acquiring information from for example a "sniffer" placed in front of the web server of the e-commerce site and/or means for gathering information from online log files from the web server hosting the ecommerce website, said log files comprising all movements on said website and/or means for collecting event information directly on the entity's own PC through for example via a plug-in on the clients web browser.

57. The system according to any of claims 51 to 56, further comprising means for preventing said entity from entering specific parts of said website or specific websites.

58. The system according to any of claims 51 to 57, further comprising means for tracking at least one entity induced event in relation to said at least one website after the transaction is final and the purchased item is made available to said entity, thereby providing means for indicating to said client if a transaction that initially appeared trustworthy in fact is fraudulent or may be fraudulent, so that for instance the actual capture for the funds that were previously authorized can be rejected in order to avoid chargebacks or the shipping of the purchased items can be stopped.

59. The system according to any of claims 51 to 58, wherein said survey means and said process means comprises means for monitoring and analyzing any entity behaviour that is not directly related to said at least one website, such as phone calls between said entity and said client's customer support.

60. The system according to any of claims 51 to 59, further comprising means for determining said at least one behaviour profile intermittently upon request from said client or a service provider

61. The system according to any of claims 51 to 60, where said information further comprises environment induced events, comprising system and/or calendar induced events such as festive seasons and national holidays and/or .

62. The system according to any of claims 51 to 61, where said information further comprises data concerning the payment, comprising transaction data and/or entity data.

63. The system according to any of claims 51 to 62, further comprising means for displaying said at least one behaviour profile, for example to said client

64. The system according to claim 63, wherein means for displaying said at least one behaviour profile, comprises a XML interface and/or a web interface.

65. The system according to any of claims 51 to 64, where said at least one client website is a merchant's website for offering a customer services and/or products in a direct sale.

66. The system according to any of claims 51 to 65, where said at least one client website is a payment solution provider's website for providing a payment solution for merchant's website or websites.

67. The system according to any of claims 51 to 66, further comprising means for providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, for example to a client, if said at least one fraud risk potential passes a predetermined threshold.

68. The system according to any of the claims 51 to 67, further comprising means for providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one fraud risk potential passes a predetermined threshold.

69. The system according to any of claims 51 to 68, where said at least one fraud risk potential is providing a binary indication, either positive or negative fraud, such as 1 or O.

70. The system according to any of claims 51 to 69, wherein said at least one fraud risk potential are divided into levels between predetermined values, such as between 0 and 9.

71. The system according to any of claims 51 to 70, comprising means for dividing said at least one fraud risk potential into at least 3 intervals between predetermined levels, whereby the two extremes on the scale can indicate either a predominantly suspicious behaviour or predominantly trustworthy behaviour, and the middle interval can be used to indicate that a manual decision preferably should be made.

72. The system according to any of claims 51 to 71, further comprising means for providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said entity induced events appears to be suspicious.

73. The system according to any of claims 51 to 72, further comprising means for providing at least an indication of positive fraud, i.e. the identification of a fraudulent entity, if said at least one behaviour profile shows that one or more predetermined suspicious entity induced events have occurred.

74. The system according to any of claims 51 to 73, further comprising means for providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one behaviour profile shows that more than a predetermined a number and/or a predetermined ratio of said entity induced events appears to be reliable.

75. The system according to any of claims 51 to 74, further comprising means for providing at least an indication of negative fraud, i.e. the identification of a trustworthy entity, if said at least one behaviour profile shows that one or more predetermined trustworthy entity induced events have occurred.

76. The system according to any of claims 51 to 75, further comprising means for automatically rejecting or denying said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour.

77. The system according to any of claims 51 to 76, further comprising means for manually deciding to reject or deny said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour displayed in said interface.

78. The system according to any of claims 51 to 77, further comprising means for manually changing the automatically made decision to either reject or deny said entity the right to purchase said client's service and/or product based on said indications of fraudulent or trustworthy behaviour.

79. The system according to any of claims 51 to 78, wherein at least part of the system events, calendar events, data concerning the payment, the predetermined rule set, and/or cross-references are provided by the client.

80. The system according to any of claims 51 to 79, wherein said at least one website is provided on the Internet.

81. The system according to any of claims 51 to 80, wherein said method is a standalone system that can be integrated in existing electronic purchase order systems provided on said merchants existing said at least one website.

82. A computer program being stored on a computer readable medium, which program is operable for performing a method according to any of the claims 1 to 48 said computer program comprising:

- code for providing input information substantially continuously concerning a given entity's behaviour over time in relation to said at least one website; and - code for sequentially analyzing said input information, at least after each said entity induced event for providing at least one behaviour profile, wherein the significance and meaning of these entity induced events are substantially dynamical and may change in time.

- code for displaying said behaviour profile.

83. A method according to claim 1, further comprising one or more of the features of claims 4, 13, 15, 16, 19 to 27, and 32 to 44, wherein said electronic entity action based system comprises at least one service for mobile telecommunication using at least one mobile telecommunication device.

84. The method according to claim 83, wherein said at least one behaviour profile is determined substantially continuously, i.e. at least after each entity induced event in relation to said at least one mobile telecommunication network, by monitoring the behaviour of said entity on said at least one mobile telecommunication network and analyzing said behaviour by applying said real-time behaviour classification algorithms to a plurality of said entity induced events, thereby providing at least one substantially time variable continuous behaviour profile.

85. The method according to claim 83 or 84, wherein said at least one behaviour profile is determined substantially continuously by monitoring the behaviour of said entity during the entire period the entity is connected to said at least one mobile telecommunication network allowing for a determination said at least one behaviour profile even if said entity exits said mobile telecommunication network without using any service provided by said client.

86. The method according to any of claims 83 to 85, wherein said information further comprises data concerning entity behaviour that is not directly related to said at least one mobile telecommunication network, such as phone calls and emails between said entity and said client's customer support.

87. The method according to any of claims 83 to 86, wherein said method is a standalone method that can be integrated in existing mobile telecommunication operating methods.

88. The method according to any of claims 83 to 87, wherein said entity induced events comprises the time and/or duration and/or cost pr minute of a given call.

89. The method according to any of claims 83 to 88, wherein said entity induced events comprises the number of text based and/or picture based and/or video based messages that are send within a certain period of time.

90. The method according to any of claims 83 to 89, wherein said information comprises the location of the entity and/or the location of the person receiving the call.

91. The method according to any of claims 83 to 90, wherein said information comprises the number of calls made over a given period of time and/or a record of commonly used phone numbers.

92. The method according to any of claims 83 to 91, wherein said information comprises the use of at least one additional feature of said at least one mobile telecommunication device.

93. The method according to 92, wherein said at least one additional feature of said at least one mobile telecommunication device comprises a photo camera and/or a video camera and/or a sound based application such as an FM radio and/or a music player such as an mp3 player.

94. The method according to any of claims 83 to 93, wherein location-based tracking provide said information, where said information comprises where said entity works and/or socialises.

95. The method according to any of claims 83 to 94, wherein said information comprises a profile of who the user socialises with, said profile being built using

Bluetooth pairing.

96. A system according to claim 49, further comprising one or more of the features of claims 49, 50, 52, 59 to 64, and 67 to 79, wherein said network comprises at least one network for mobile telecommunication using at least one mobile telecommunication device.

97. The system according to claim 96, further comprising means for determining said at least one behaviour profile substantially continuously, i.e. at least after each entity induced event in relation to said at least one mobile telecommunication network.

98. The system according to any of claims 96 or 97, further comprising means for determining said at least one behaviour profile by based on data concerning entity behaviour that is not directly related to said at least one mobile telecommunication network, such as phone calls between said entity and said client's customer support.

99. The system according to any of claims 96 to 98, wherein said process means for determining said at least one behaviour profile comprises real-time behaviour classification algorithms for generating at least one fraud risk potential by analyzing said plurality of entity induced events in relation to said at least one mobile telecommunication network.

100. The system according to any of claims 96 to 99, wherein said system is a standalone system that can be integrated in existing mobile telecommunication systems.

101. The system according to any of claims 96 to 100, wherein said survey means comprises means for monitoring the time and/or duration and/or cost pr minute of a given call.

102. The system according to any of claims 96 to 101, wherein said survey means comprises means for recording the number of text based and/or picture based and/or video based messages that are send within a certain period of time.

103. The system according to any of claims 96 to 102, wherein said survey means comprises means for detecting the location of the entity and/or the location of the person receiving the call.

104. The system according to any of claims 96 to 103, wherein said survey means comprises means for detecting the number of calls made over a given period of time and/or a record of commonly used phone numbers.

105. The system according to any of claims 96 to 104, wherein said survey means comprises means for detecting the use of at least one additional feature of said at least one mobile telecommunication device.

106. The system according to 105, wherein said at least one additional feature of said at least one mobile telecommunication device comprises a photo camera and/or a video camera and/or a sound based application such as an FM radio and/or a music player such as an mp3 player.

107. The system according to any of claims 96 to 106, wherein said survey means comprises means for location-based tracking of the entity.

108. The system according to any of claims 96 to 107, wherein said survey means comprises means for Bluetooth pairing for determining a profile of who the user socialises with.

109. A computer program being stored on a computer readable medium, which program is operable for performing a method according to any of the claims 83 to 95 said computer program comprising:

- code for providing input information substantially continuously concerning a given entity's behaviour over time in relation to said at least one mobile telecommunication network; and

- code for sequentially analyzing said input information, at least after each said entity induced event at least one behaviour profile, wherein the significance and meaning of these entity induced events are substantially dynamical and may change in time.

- code for displaying said behaviour profile.

110. A method according to claim 1, further comprising one or more of the features of claims 4, 13, 15, 16, 19 to 27, and 32 to 44, wherein said electronic entity action based system comprises at least one service for credit card payment.

1 11. The method according to claim 1 10, wherein said at least one behaviour profile is determined substantially continuously, i.e. at least after each entity induced event in relation to said at least one credit card payment network, by monitoring the behaviour of said entity on said at least one credit card payment network and analyzing said behaviour by applying said real-time behaviour classification algorithms to a plurality of said entity induced events, thereby providing at least one substantially time variable continuous behaviour profile.

112. The method according to any of claims 110 or 111, wherein said information further comprises data concerning entity behaviour that is not directly related to said at least one credit card payment network, such as phone calls between said entity and said client's customer support.

1 13. The method according to any of claims 110 to 112, wherein said method is a stand-alone method that can be integrated in existing credit card payment methods.

114. The method according to any of claims 110 to 113, wherein at least one behaviour profile comprising at least one fraud risk potential is generated by using said real-time behaviour classification algorithms to analyze how said entity interacts with said at least one credit card payment network during said plurality of entity induced events.

115. The method according to any of claims 110 to 114, wherein said information comprises the number of purchases made per day and/or the character of the purchased goods and/or time of purchase and/or location of purchase.

116. The method according to any of claims 110 to 115, wherein said information comprises is cross- referenced with data concerning said entity, said data comprising the entity's profession and/or age and/or country of residence and/or travel frequency and/or hobbies and/or interest.

117. A system according to claim 49, further comprising one or more of the features of claims 50, 52, 59, 60, 62, 63, 64, 66 to 77, wherein said electronic entity action based system comprises at least one network for credit card payment.

118. The system according to claim 117, further comprising means for determining said at least one behaviour profile substantially continuously, i.e. at least after each entity induced event in relation to said at least one credit card payment network.

119. The system according to any of claims 117 or 118, further comprising means for determining said at least one behaviour profile by based on data concerning entity behaviour that is not directly related to said at least one credit card payment network, such as phone calls between said entity and said client's customer support.

120. The system according to any of claims 117 to 119, wherein said process means for determining said at least one behaviour profile comprises real-time behaviour classification algorithms for generating at least one fraud risk potential by analyzing said plurality of entity induced events in relation to said at least one credit card payment network.

121. The system according to any of claims 117 to 120, wherein said system is a stand-alone system that can be integrated in existing credit card payment systems.

122. The system according to any of claims 117 to 121, wherein said survey means comprises means for monitoring the behaviour of said entity by detecting the number of purchases made per day and/or the character of the purchased goods and/or time of purchase and/or location of purchase.

123. The system according to any of claims 110 to 115, wherein said process means comprises means for cross-referencing with data concerning said entity, said data comprising the entity's profession and/or age and/or country of residence and/or travel frequency and/or hobbies and/or interest.

124. A computer program being stored on a computer readable medium, which program is operable for performing a method according to any of the claims 110 to 116 said computer program comprising: - code for providing input information substantially continuously concerning a given entity's behaviour over time in relation to said at least one credit card payment network; and

- code for displaying said behaviour profile.

125. A method according to claim 1, further comprising one or more of the features of claims 2 to 7, 9, 13, 15 to 23, 25, 26, 30, 31 and 44 to 48, wherein said electronic entity action based system comprises a client's at least one website for ecommerce, and said behaviour classification algorithms are used to classify a given entity's interest in specific types of advertisements and/or goods available on this at least one website, to predict whether said entity is inclined to click on specific advertisement links shown on said at least one website or by specific types of goods.

126. The method according to claim 125, wherein said website shows user adapted advertisements chosen from said classification of said entitys interests.

127. The method according to claim 125 or claim 126, wherein said website prioritise the presentation of the available goods based on said classification of said entitys interests.

128. The method according to any of claims 125 to 127, wherein said website is used for non-profit purposes such as collection of financial support to organizations.

129. A system according to claim 49, further comprising one or more of the features of claims 50 to 57, 59 to 66, and 79 to 81, wherein said electronic entity action based system comprises a client's at least one website for ecommerce, and said behaviour classification algorithms are used to classify a given entity's interest in specific types of advertisements and/or goods available on this at least one website, to predict whether said entity is inclined to click on specific advertisement links shown on said at least one website or by specific types of goods.

130. The system according to claim 129, further comprising means for showing said entity user specific advertisements.

131. The system according to claim 129 or claim 130, further comprising means for prioritising the presentation of the available goods sold on said website based on said classification of said entity's interests.

132. The system according to any of claims 129 to 131, wherein said website provide means for non-profit purposes such as collection of financial support to organizations.

133. A computer program being stored on a computer readable medium, which program is operable for performing a method according to any of the claims 125 to 128, said computer program comprising:

- code for providing input information substantially continuously concerning a given entity's behaviour over time in relation to said at least one website; and - code for sequentially analyzing said input information, at least after each said entity induced event at least one behaviour profile, wherein the significance and meaning of these entity induced events in principle are dynamical and may change in time; and code for displaying said behaviour profile.