CN113159634A

CN113159634A - Financial product management method and device and electronic equipment

Info

Publication number: CN113159634A
Application number: CN202110526292.2A
Authority: CN
Inventors: 刘高; 王杰明; 张鹏; 吴婧琦; 迟骋; 王磊
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-05-14
Filing date: 2021-05-14
Publication date: 2021-07-23

Abstract

The application provides a financial product management method and device and electronic equipment. The method comprises the following steps: collecting financial product data; constructing a product evaluation index library, and determining a weight coefficient of an evaluation index by using a fuzzy analytic hierarchy process; and summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result. The invention combines the advantages of the fuzzy evaluation method and the analytic hierarchy process, and can well solve the problem that the thinking of the hierarchical evaluation method cannot be consistent when a plurality of indexes are evaluated; in addition, the invention realizes online quantitative automatic product evaluation, grades the clustered products according to the evaluation results, realizes annual dynamic management of the grading results, and provides decision basis for product operation management departments and resource allocation departments in product assessment and resource allocation.

Description

Financial product management method and device and electronic equipment

Technical Field

The application relates to the field of data mining, in particular to a financial product management method and device and electronic equipment.

Background

With the deep development of financial markets, financial products are increasingly complex and diverse, and the cross connection is continuously deepened. How to objectively analyze and evaluate the existing products, how to establish a product grading and appropriateness matching mechanism, how to utilize and manage rich product structures, and how to create a high-quality product library are important topics. At present, financial products have the problems of repeated development of similar products, long time consumption of communication cooperation of different products among departments, various evaluation indexes, incapability of comprehensive utilization and the like, so that a quantitative evaluation model needs to be applied to comprehensively consider evaluation dimensions such as market performance, development potential, value contribution degree and the like, and the products are evaluated and classified and managed in a grading way.

The research on the evaluation of the existing products is mostly the result of the research provided by some financial service institutions or the report of research and research. The technical methods for evaluating the current products comprise the following steps:

1) analytic hierarchy process

The analytic hierarchy process was first proposed by the operational research institute, saty, and is a systematic analysis method. The method comprises the steps of firstly layering a system, decomposing the system into different composition factors according to the property and the total target of the system, dividing the system into different layers of combinations according to the correlation among the factors and the membership relation to form a layer of system analysis result model, then calculating the relative importance weight of the factors at the bottommost layer relative to the total target of the system at the highest layer, and then determining the priority sequence of the schemes. According to the method, a structural model or a target tree is established, reasonable combination weight is calculated, and finally a comprehensive index is obtained. This method is susceptible to information overlap between the indices.

2) Principal component analysis method

Principal component analysis is a multivariate statistical method which converts a plurality of indexes into a few comprehensive indexes and keeps a large amount of information of the original indexes. The principal components not only retain most of information of original variables, but also are mutually independent, and some overlapped information is removed, so that the problems are optimally and comprehensively simplified. But this method sometimes loses the information of the original variables; in addition, different statistical analysis software may be used to determine the result of inconsistent feature vectors, which may lead to inconsistent analysis results or the opposite.

3) Artificial neural network

The neural network is a model for simulating the structure of a biological nervous system, wherein a special nonlinear relation model between a weight description variable and a target is established, and the judgment and analysis of objects must be performed through a learning or training process. The nonlinear adaptive dynamic system composed of a large number of processing units has learning ability, memory ability, computing ability and intelligent processing function, simulates information processing mechanism of brain in different degrees and levels, and has the characteristics of large-scale information processing, distributed associative storage, adaptive learning and self-organization. But the required index parameters are more, and no effective method is available for selecting the parameters; the method has the defects of high requirement on sample representativeness, large dependence on samples and the like.

4) Fuzzy evaluation method

The method is a method for comprehensively evaluating or judging things or phenomena which are comprehensively influenced by various factors by using a fuzzy mathematical principle. The fuzzy comprehensive evaluation is an application mode of fuzzy mathematics in actual work, is a comprehensive evaluation method of an object system related to fuzzy factors, and can better solve the problems of fuzziness in the comprehensive evaluation, such as the unsharpness of transaction attributes, the fuzziness of the knowledge of evaluation experts and the like. The basic idea of risk evaluation by adopting a fuzzy comprehensive evaluation method is to comprehensively consider the influence degrees of all risk factors and set the importance of weight for distinguishing all the factors.

5) Expert scoring method

The expert scoring method belongs to a qualitative research method, firstly, influence factors possibly related to a certain specific product are obtained, influence factor questionnaires are listed, secondly, the importance of the influence factors is evaluated by using an expert system, and the evaluation result is synthesized into a whole evaluation result. But the subjectivity is too strong mainly depending on human judgment.

Therefore, the above technical methods have respective limitations.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a financial product management method, a financial product management device and electronic equipment, which combine an analytic hierarchy process and a fuzzy evaluation process and solve the problem that thinking of the financial product management method cannot be consistent when a plurality of evaluation indexes are provided.

In a first aspect, the present application provides a financial product management method, comprising: collecting financial product data; constructing a product evaluation index library, and determining a weight coefficient of an evaluation index by using a fuzzy analytic hierarchy process; and summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

Further, in some embodiments, the collecting financial product data comprises: collecting existing data in a data warehouse; and collecting financial product data of each component and each system in the whole field.

Further, in some embodiments, the building a product evaluation index library comprises: and dividing the evaluation field of the financial product according to the set evaluation standard and principle, and constructing an evaluation index library of the saleable product.

Further, in some embodiments, the determining a weight coefficient of the evaluation index by the fuzzy analytic hierarchy process includes: classifying the evaluation indexes, and establishing a hierarchical model, wherein the hierarchical model comprises a corresponding hierarchical relation between the evaluation field and the evaluation indexes; comparing every two relative importance of each factor in the same layer relative to the previous layer according to the hierarchical model to establish a judgment matrix; calculating the maximum eigenvalue and the eigenvector of the judgment matrix, and carrying out consistency check on the judgment matrix according to the maximum eigenvalue; and if the consistency check is passed, obtaining the weight coefficient of each corresponding evaluation index according to the characteristic vector, otherwise, adjusting the initial value of the judgment matrix and then carrying out consistency check.

Further, in some embodiments, the aggregating the quantified evaluation index values from the marketable product granularity to the clustered product granularity, and calculating the composite score of the single clustered product comprises: the method comprises the steps of obtaining processing rules of all evaluation indexes of the granularity of a saleable product through butt joint with different business components, and quantizing the processing rules to obtain quantized evaluation index values; and carrying out normalization calculation according to the quantized evaluation index value and the weight coefficient of the evaluation index to obtain the comprehensive score of the single-item clustered product.

Further, in some embodiments, the performing normalization calculation according to the quantized evaluation index value and the weighting coefficient of the evaluation index to obtain a comprehensive score of the single-item clustered product includes:

wherein S is the comprehensive score of the single-item clustering product, T_iWeight coefficient, Q, for evaluation index_jAnd the quantified evaluation index value is used.

Further, in some embodiments, the managing the clustered products according to the composite scoring result includes: and grading the clustered products according to the comprehensive scores of the clustered products, and formulating a coping strategy corresponding to the clustered products by combining a five-level theory of the products.

In a second aspect, the present application provides a financial product management apparatus comprising: the acquisition module is used for acquiring financial product data; the weight determining module is used for constructing a product evaluation index library and determining the weight coefficient of the evaluation index by using a fuzzy analytic hierarchy process; and the scoring module is used for summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

In a third aspect, the present application provides an electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the financial product management method of the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon at least one instruction which, when executed by a processor, implements a method of financial product management as described in the first aspect.

The embodiment of the invention provides a financial product management method, a financial product management device and electronic equipment, wherein the advantages of a fuzzy evaluation method and an analytic hierarchy process are combined, so that the problem that thinking cannot be consistent when a plurality of hierarchical evaluation indexes are provided can be well solved; in addition, the invention realizes online quantitative automatic product evaluation, grades the clustered products according to the evaluation results, realizes annual dynamic management of the grading results, and provides decision basis for product operation management departments and resource allocation departments in product assessment and resource allocation.

Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.

Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.

It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

FIG. 1 is a process flow diagram of a method of financial product management in accordance with an embodiment of the present invention;

FIG. 2 is a product evaluation index library constructed from financial products in accordance with an embodiment of the present invention;

FIG. 3 is a flowchart of a method for determining a weight coefficient of an evaluation index by a fuzzy analytic hierarchy process in step S102 of the embodiment shown in FIG. 1;

FIG. 4 is a schematic diagram of a three-level model according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of determining correspondence between various evaluation domains, clustered products, saleable products, and indicators according to an embodiment of the present invention;

FIG. 6 is a diagram illustrating a weight coefficient table according to an embodiment of the present invention;

FIG. 7 is a flowchart of a method for implementing step S103 according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of a five-level theory of a product according to an embodiment of the present invention;

FIG. 9 is a schematic structural diagram of a financial product administrator apparatus according to an embodiment of the present invention;

fig. 10 is a schematic block diagram of a system configuration of an electronic apparatus according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Before further detailed description of the embodiments of the present application, terms and expressions referred to in the embodiments of the present application will be described, and the terms and expressions referred to in the embodiments of the present application will be used for the following explanation.

Data mining: refers to a non-trivial process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in a database. Data mining is a decision support process, and is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization technologies and the like, the data of enterprises are analyzed in a highly automated manner, inductive reasoning is made, potential patterns are mined out from the data, decision makers are helped to adjust market strategies, risks are reduced, and correct decisions are made.

Clustering products: the system is an enterprise-level overall management object formed by clustering the saleable products according to the similar or similar product characteristics according to the product business logic, has a mapping relation with the saleable products with clear logic and real-time linkage, and has the characteristic of consistent saleable product risk under the same clustered product item.

Granularity: the granularity is the thickness degree of data statistics under the same dimension, and refers to the level of the refinement or integration degree of the data stored in the data unit of the data warehouse. The higher the refinement degree is, the smaller the granularity level is; conversely, the lower the degree of refinement, the larger the granularity level. Granularity is a major design issue in a data warehouse environment because it profoundly affects the size of the amount of data stored in the data warehouse, as well as the type of queries that the data warehouse can answer. The size of the granularity requires the data warehouse to make a trade-off between the size of the data volume and the level of detail of the query at design time.

The embodiment of the invention provides a financial product management evaluation method, which selects a comprehensive evaluation method combining a fuzzy comprehensive evaluation method and an analytic hierarchy process. The analytic hierarchy process can compare and judge the importance of the factors on a certain layer relative to the factors on the previous layer to form a judgment matrix, and then single sequencing and consistency inspection are carried out, so that a user can obtain the relative weight between the parameters in an intuitive mode, and the analytic hierarchy process is high in reliability and small in error. However, when a certain level evaluation index is many (for example, more than four), the consistency of thinking is difficult to guarantee, and in order to make up for the defect, the fuzzy analytic hierarchy process formed by combining the advantages of the fuzzy analytic process and the analytic hierarchy process can well solve the problem that the thinking cannot be consistent when the level evaluation index is many.

FIG. 1 is a flowchart illustrating a method for managing financial products according to an embodiment of the present invention. As shown in fig. 1, includes:

step S101, collecting financial product data;

step S102, constructing a product evaluation index library, and determining a weight coefficient of an evaluation index by using a fuzzy analytic hierarchy process;

and S103, summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

In specific implementation, in step S101 of this embodiment, the financial product data is collected, including the collection of the existing data of the data warehouse and the collection of the data of each component and system, so that the product classification is more comprehensive and accurate, specifically:

1) data warehouse data collection

Analyzing the existing data of the data warehouse, classifying the products in grades, collecting the data sheets into a product analysis and evaluation database, and processing indexes and labels.

2) Component product data collection

In order to more comprehensively and accurately carry out hierarchical management on products and further carry out data acquisition on each component and each system so as to meet the data collection work of the hierarchical classification indexes of the marketable products in the whole field.

In step S102 of this embodiment, a product evaluation index library is constructed, specifically: and dividing the evaluation field of the financial product according to the set evaluation standard and principle, and constructing an evaluation index library of the saleable product.

In some embodiments, 22 evaluation domains including deposit, credit, debit card, receipt, staging, deposit and custody, agency sales, proxy settlement, proxy loan, financial market, customer asset management, cash management, trade financing, investment bank, consultant, payment settlement, peer business, channel, customer service platform, socialized service platform, etc. are constructed for the financial product in the practice of the present application according to the criteria and principles of evaluation. And moreover, the evaluation indexes which can show the financial products most comprehensively are selected, repeated or similar indexes are removed, and finally, a product evaluation index library (as shown in fig. 2) is constructed according to five dimensions of performance, market and customers, traffic, risk, innovation capacity and the like and according to the total 41 product evaluation indexes included in the five dimensions.

In the product evaluation index library of the embodiment shown in fig. 2, the primary indexes include 5 performance, market and customer, traffic, risk and innovation capability, and the secondary indexes include 41 profitability, number of merchants, card distribution, bad loan rate, product association degree and the like.

Fig. 3 is a flowchart of a method for determining a weight coefficient of an evaluation index by a fuzzy analytic hierarchy process in step S102 according to an embodiment of the present invention. As shown in fig. 3, the method may specifically include:

step S301, classifying the evaluation indexes, and establishing a hierarchical model, wherein the hierarchical model comprises a corresponding hierarchical relation between the evaluation field and the evaluation indexes;

step S302, comparing every two relative importance of each factor in the same layer relative to the previous layer according to the hierarchical model, and establishing a judgment matrix;

step S303, calculating the maximum eigenvalue and the eigenvector of the judgment matrix, and carrying out consistency check on the judgment matrix according to the maximum eigenvalue;

and step S304, if the consistency check is passed, obtaining the weight coefficient of each corresponding evaluation index according to the characteristic vector, otherwise, adjusting the initial value of the judgment matrix and then carrying out the consistency check.

The fuzzy comprehensive evaluation method is a common method for comprehensively integrating qualitative analysis and quantitative analysis, is widely applied in social life, and how to reasonably determine the weight of each evaluation index in practical application is a difficult point at present. Among the methods for determining the weight, the analytic hierarchy process is the most prominent one, and is an effective method for carrying out quantitative analysis on non-quantitative events, which is used for mathematics of thinking process of a complex system by people, carrying out quantitative analysis on qualitative analysis mainly based on subjective judgment of people, and quantifying differences among various judgment elements to help people to keep consistency of the thinking process.

Due to the large number of financial products and evaluation factors, the evaluation indexes can be classified and combined, as shown in fig. 4, to form a hierarchical structure, which can be divided into three layers, for example. In order to establish the relationship between each evaluation area, clustered products, saleable products, and indexes (as shown in fig. 5), it should be noted that the evaluation indexes of the saleable products in the same evaluation area are identical, and the selection of the evaluation index is determined according to the characteristics of the actual area, so that the evaluation indexes of the saleable products in different evaluation areas are not necessarily identical. One saleable product belongs to only one clustered product, and one clustered product belongs to only one evaluation field. Therefore, when the weights are actually established, only the weights of the evaluation indexes of the evaluation areas and different levels need to be established.

In step S302, each factor in the same layer is compared with each other in pairs with respect to the previous layer according to the hierarchical model, a certain judgment is given to the relative importance of each factor in each layer, the two factors are compared, and a judgment matrix of a certain factor in a certain layer with respect to the previous layer is constructed as follows:

in the judgment matrix (B)_kin-C), C_ijIndicates a secondary index C_iAnd a second level index C_jCompared with each other, and the index B of the previous layer is_kWhichever is more important, to reduce the occurrence of misjudgmentIn some embodiments, a ratio of 1 to 9 (as shown in Table 1 below) may be used.

TABLE 1

Scale	Means of
		1	The two factors are compared and have the same importance
3	Comparison of two factors, one being slightly more important than the other
		5	Comparison of two factors, one being significantly more important than the other
7	Comparison of two factors, one being of greater importance than the other
		9	Comparison of two factors, one being extremely important over the other
2、4、6、8	Median between two adjacent judgments
		Reciprocal of the	Judgment a of factor i compared with j_ijA judgment a comparing the factor j with the factor i_ij＝1/a_ji

In some embodiments, there is a decision matrix (B)_kAfter C), its maximum eigenvalue and corresponding eigenvector are calculated, i.e.:

(B_k-C)V＝λ_maxV

in the above formula, λ_maxIs a judgment matrix (B)_kMaximum eigenvalue of-C), V being a number corresponding to λ_maxThe feature vector of (2). In some embodiments, the weighting coefficients of the corresponding evaluation indexes are obtained according to the feature vector V.

In practical analysis, due to the complexity of objective objects, it is impossible that each judgment matrix has complete consistency, and in order to investigate whether the judgment matrix is suitable for hierarchical analysis, consistency check is carried out on the judgment matrix. To check the consistency of the decision matrix, the consistency index needs to be calculated:

in the above formula, n is the order of the judgment matrix. When the order number of the matrix is less than 3, the consistency check algorithm is better, and when the order number of the matrix is higher, the consistency needs to be corrected. The algorithm is as follows:

wherein, RI is a correction factor, and the values are shown in the following table 2:

TABLE 2

Order of the scale	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
																RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49	1.52	1.54	1.56	1.58	1.59

In general, when CR is greater than 0.1, the decision matrix is considered to satisfy the requirement of consistency, otherwise, the initial value of the decision matrix needs to be adjusted.

In some embodiments, the algorithm for determining the weight by combining the above analytic hierarchy process is applied to determine the product index weight of the financial product, and a weight coefficient table as shown in fig. 6 can be obtained. As can be seen from the weight coefficient table shown in fig. 6, the primary index and the secondary index respectively correspond to weight coefficient values for different evaluation areas.

Fig. 7 is a flowchart of a method for calculating a composite score of a single clustered product by summarizing quantized evaluation index values from marketable product particle sizes to clustered product particle sizes in step S103 according to an embodiment of the present invention. As shown in fig. 7, the method may specifically include:

step S701, acquiring processing rules of each evaluation index of the marketable product granularity by butting with different service components, and quantizing the processing rules to obtain quantized evaluation index values;

and step S702, carrying out normalization calculation according to the quantized evaluation index value and the weight coefficient of the evaluation index to obtain the comprehensive score of the single-item clustered product.

In some embodiments, the processing rules of each evaluation index of the marketable granularity are acquired and quantized by interfacing with different business components, so as to obtain quantized evaluation index values. Then, according to the quantized evaluation index value and the weighting coefficient of the evaluation index, normalization calculation is performed to form a normalized value, namely, the comprehensive score value of the single item clustering product, namely:

In the embodiment of the invention, after the comprehensive score of the single clustering product is obtained, the clustering product is subjected to hierarchical classification management. In some embodiments, all clustered products in the same field may be ranked according to the ranking percentage of the final composite score according to the composite score result, and the clustered products may be subjected to hierarchical classification management following the ranking coping path of the "score-percentage ratio-ranking-coping strategy", for example, the top 20% may be taken as a second-level product, the top 20% to the top 80% may be taken as a third-level product, the rest may be taken as a fourth-level product, and the products that are not sold may be individually judged as a fifth-level product.

The key to the competitive win of modern products is not what products the enterprises produce but what added value they can give to the products. In the present stage, the attention and expectation of customers to products are improved along with the development of economy, and besides some basic factors such as price and income, the extended benefits of various matching services, wind control capability and the like also directly influence the satisfaction degree of customers. Therefore, according to the grading result, the product coping strategy is formulated by referring to the five-level theory of the product (as shown in figure 8).

On the basis of the visual angle of a producer, the visual angle of the consumer is introduced, all psychological processes of selecting and consuming products by the consumer are completely explained, the formation of the utility and the value of the product has bidirectional interaction, and the visual angle of the product is simultaneously and better guided by two parties of a market main body for research and development and marketing of the product. Wherein:

core products, i.e., the basic services or benefits that a customer receives from the use or consumption of bank products. The financial market environment is variable, and the banking core business keeps relatively stable characteristics. For banks, the core products are mainly the deposit and loan business, and also include the traditional intermediate business such as payment settlement and the supporting services thereof.

The basic product is a carrier for the core product to play a role, and comprises positioning and design of the form, quality, brand and the like of the basic product, and generally refers to a real object with different time limit, use and income.

It is desirable for a product, i.e., a set of conditions or attributes that are closely related to the product itself, as desired by a customer, to provide a high level of service awareness for the underlying product.

The expansion products, also called add-on products, are value-added services and benefits that customers obtain from bank products.

The potential products represent the evolution direction or trend of enterprise products, and for commercial banks, the potential products are product innovation oriented by mixed operation thinking.

By the financial product management evaluation and grading classification method, online quantitative automatic product evaluation can be realized, the clustered products are graded according to the evaluation results, annual dynamic management of the grading results is realized, and decision basis is provided for product management departments and resource allocation departments in product assessment and resource allocation. In the implementation process, the advantages of a fuzzy evaluation method and an analytic hierarchy process are combined, so that the problem that thinking cannot be consistent when a plurality of hierarchical evaluation indexes exist can be well solved.

Based on the same inventive concept, the embodiment of the present application further provides a financial product management apparatus, which can be used to implement the methods described in the above embodiments, as described in the following embodiments. Because the principle of solving the problems of the financial product management device is similar to that of the financial product management method, the implementation of the financial product management device can refer to the implementation of the software performance reference determination method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Referring to fig. 9, an embodiment of the present application further provides a financial product management apparatus, including: an acquisition module 901, configured to acquire financial product data; the weight determination module 902 is configured to construct a product evaluation index library, and determine a weight coefficient of an evaluation index by using a fuzzy analytic hierarchy process; and the scoring module 903 is used for summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

From a hardware aspect, the present application provides an embodiment of an electronic device for implementing all or part of the content in the financial product management method, where the electronic device specifically includes the following content:

a Processor (Processor), a Memory (Memory), a communication Interface (Communications Interface) and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the financial product management device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiments of the financial product management method and the financial product management apparatus in the embodiments, and the contents thereof are incorporated herein, and repeated descriptions thereof are omitted.

In practical applications, part of the financial product management method may be performed on the electronic device side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be in communication connection with a remote server to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

Fig. 10 is a schematic block diagram of a system configuration of an electronic apparatus according to an embodiment of the present application. As shown in fig. 10, the electronic device may include a processor 1 and a memory 2; a memory 2 is coupled to the processor 1. Notably, this fig. 10 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

The present application further provides a computer-readable storage medium capable of implementing all the steps of the financial product management method in which the execution subject is the server or the client in the above embodiments, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps of the financial product management method in which the execution subject is the server or the client in the above embodiments.

In summary, the financial product management method and apparatus, the electronic device, and the storage medium according to the embodiments of the present invention can implement online quantitative automated product evaluation, classify the clustered products according to the evaluation results, implement annual dynamic management of the classification results, and provide decision basis for product management and management departments and resource allocation departments during product assessment and resource allocation. In addition, the invention combines the advantages of the fuzzy evaluation method and the analytic hierarchy process, and can well solve the problem that the thinking of the hierarchical evaluation method cannot be consistent when a plurality of hierarchical evaluation indexes exist.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A financial product management method, comprising:

collecting financial product data;

constructing a product evaluation index library, and determining a weight coefficient of an evaluation index by using a fuzzy analytic hierarchy process;

and summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

2. The financial product management method of claim 1 wherein said collecting financial product data comprises:

collecting existing data in a data warehouse; and

financial product data for each component and each system in the whole field is collected.

3. The financial product management method of claim 1, wherein said building a product evaluation index library comprises:

and dividing the evaluation field of the financial product according to the set evaluation standard and principle, and constructing an evaluation index library of the saleable product.

4. The financial product management method of claim 3, wherein said determining a weight coefficient of an evaluation index by a fuzzy analytic hierarchy process comprises:

classifying the evaluation indexes, and establishing a hierarchical model, wherein the hierarchical model comprises a corresponding hierarchical relation between the evaluation field and the evaluation indexes;

comparing every two relative importance of each factor in the same layer relative to the previous layer according to the hierarchical model to establish a judgment matrix;

calculating the maximum eigenvalue and the eigenvector of the judgment matrix, and carrying out consistency check on the judgment matrix according to the maximum eigenvalue;

and if the consistency check is passed, obtaining the weight coefficient of each corresponding evaluation index according to the characteristic vector, otherwise, adjusting the initial value of the judgment matrix and then carrying out consistency check.

5. The financial product management method of claim 1 wherein said aggregating the quantified evaluation index values from marketable product granularity to clustered product granularity and calculating a composite score for a single clustered product comprises:

the method comprises the steps of obtaining processing rules of all evaluation indexes of the granularity of a saleable product through butt joint with different business components, and quantizing the processing rules to obtain quantized evaluation index values;

and carrying out normalization calculation according to the quantized evaluation index value and the weight coefficient of the evaluation index to obtain the comprehensive score of the single-item clustered product.

6. The financial product management method of claim 5, wherein said performing a normalization calculation according to the quantized evaluation index value and the weighting factor of the evaluation index to obtain a composite score of the individual clustered products comprises:

7. The financial product management method of any one of claims 1-6 wherein managing clustered products based on the composite scoring results comprises:

and grading the clustered products according to the comprehensive scores of the clustered products, and formulating a coping strategy corresponding to the clustered products by combining a five-level theory of the products.

8. A financial product management apparatus, comprising:

the acquisition module is used for acquiring financial product data;

the weight determining module is used for constructing a product evaluation index library and determining the weight coefficient of the evaluation index by using a fuzzy analytic hierarchy process;

and the scoring module is used for summarizing the quantized evaluation index values from the marketable product granularity to the clustered product granularity, calculating the comprehensive score of the single clustered product, and managing the clustered product according to the comprehensive score result.

9. An electronic device, comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the financial product management method of any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one instruction which, when executed by a processor, implements the financial product management method of any one of claims 1 to 7.