CN110032607A - A kind of auditing method based on big data - Google Patents
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Abstract
The invention discloses a kind of auditing methods based on big data comprising: acquire the transaction data over the years of each object unit, make out an invoice over the years data and audit result data over the years；The transaction data over the years of each object unit is classified, multi-exchange classification data is obtained, the data of making out an invoice over the years of each object unit are classified, a variety of classification data of making out an invoice are obtained；The audit result data over the years of each object unit is classified, a variety of audit conclusion classification data are obtained；All Activity classification data, classification data of making out an invoice and audit conclusion classification data are trained as training sample, and using NB Algorithm, obtain classifier；Classification and Detection is carried out to the transaction data of new collected target unit and data of making out an invoice by classifier, obtains the audit conclusion classification results of target unit.The present invention can break through dependence of the auditing department to the quality of data, and effectively promote success rate, reliability and the efficiency of auditing department's detection problem unit.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an auditing method based on big data.
Background
In the prior art, the traditional SQL database technology is mainly adopted to audit unit data, and SQL statements are used for correlation query, and the problems of the method are mainly expressed in two aspects:
firstly, depending on the quality of collected unit data, different data need to be identified by a certain special main key when being associated, and only when the identifications are completely consistent, the association can be performed, and the actual situation is that the data usually come from a plurality of different data systems, the field definition and the type of each data system can have differences, so that partial data can be missed when the SQL statement is used for strong association, and finally, the obtained audit conclusion is not strict.
And secondly, the judgment can be usually carried out only on the basis of financial data provided by units, and other evidences are lacked.
Disclosure of Invention
The invention aims to: aiming at the existing problems, the method for generating the live-action map is provided, so that the dependence of an auditing department on data quality can be broken through, and the success rate, reliability and efficiency of detecting problem units by the auditing department can be effectively improved.
In order to solve the technical problems, the invention adopts a technical scheme that: providing an auditing method based on big data, S1: acquiring historical trade data, historical billing data and historical audit result data of each object unit, and importing the historical trade data, the historical billing data and the historical audit result data into a data warehouse; s2: classifying the historical transaction data of each object unit according to a first preset economic classification mode to obtain various transaction classification data, and classifying the historical invoicing data of each object unit according to a second preset economic classification mode to obtain various invoicing classification data; s3: classifying the historical audit result data of each object unit according to a preset audit conclusion classification mode to obtain various audit conclusion classification data; s4: taking all transaction classification data, invoicing classification data and audit conclusion classification data as training samples, and training the training samples by using a naive Bayes algorithm to obtain a classifier; s5: and carrying the newly acquired transaction data and invoicing data of the target unit into the classifier, and carrying out classification detection on the transaction data and the invoicing data of the target unit through the classifier to obtain an audit conclusion classification result of the target unit.
Preferably, the auditing method further comprises: s6: and marking the target unit with abnormal audit conclusion classification result as the key unit to be checked.
Preferably, the transaction classification data includes budget expenditure total, actual expenditure total and actual expenditure detail, the billing classification data includes expenditure billing total, and the audit conclusion classification data includes normal, actual inconsistency, account inconsistency and account inconsistency, wherein the actual inconsistency represents the budget expenditure total and the actual expenditure total, the account inconsistency represents the actual expenditure total and the actual expenditure detail, and the account inconsistency represents the actual expenditure total and the expenditure billing total.
Preferably, the step of training the training samples by using a naive bayes algorithm to obtain the classifier specifically comprises:
s41: let x be { a ═ a1，a2，...，amThe item to be classified is represented by x, a is used as the characteristic attribute of x and is transaction classification data and invoicing classification data of the object unit, and m is a natural number;
s42: let C ═ y1，y2，...，ynThe method comprises the steps of (1) setting a category set, wherein y is an audit conclusion category of C, and n is a natural number;
s43: computing the conditional probability P (y) using a naive Bayes algorithm1|x)，P(y2|x)，...，P(yn| x), wherein the calculation formula of the naive bayes algorithm is as follows:p (B | a) represents the probability of the occurrence of feature a at the time of occurrence of feature B. P (A) represents the probability of occurrence of feature A;
s44: obtaining a classifier according to the calculation result of the conditional probability, wherein the classifier is expressed as:
if P (y)k|x)＝max{P(y1|x)，P(y2|x)，...，P(yn| x) }, thenk is less than or equal to n.
Preferably, the step S43 specifically includes:
step S431: selecting an appointed item to be classified and an appointed class set as a training set;
step S432: and counting the conditional probability of each characteristic attribute under each audit conclusion classification, namely:
P(a1y1)，P(a2|y1)，...，P(am|y1)；P(a1|y2)，P(a2|y2)，...，P(am|y2)；...；P(a1|yn)，P(a2|yn)，...，P(an|yn)；
step S433: assuming that each characteristic attribute is independent, calculating conditional probability according to Bayes theorem, wherein the calculation formula is as follows:
wherein,
preferably, the first preset economic classification mode is one or more of a budget expenditure total classification mode, an actual expenditure total classification mode and an actual expenditure detail classification mode, the second preset economic classification mode is an expenditure billing total classification mode, and the preset audit conclusion classification mode is one or more of a normal classification mode, an account real non-compliance classification mode, an account non-compliance classification mode and an account non-compliance classification mode.
In conclusion, by adopting the technical scheme, the big data technology is used for replacing the traditional SQL database to process the audit data, the weak association is used for replacing the traditional strong association to analyze unit data, and the machine learning method is used for solving the problem of reasonability of the audit conclusion, so that the dependence of the audit department on the data quality can be broken through, and the success rate, the reliability and the efficiency of detecting problem units by the audit department can be effectively improved.
Drawings
FIG. 1 is a flow chart of a big data based auditing method according to an embodiment of the invention.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
As shown in fig. 1, in the embodiment of the present invention, the big data-based auditing method includes the following steps:
s1: and acquiring the historical trade data, the historical billing data and the historical audit result data of each object unit, and importing the historical trade data, the historical billing data and the historical audit result data into a data warehouse.
The object units are already audited units, and the historical trade data, the historical billing data and the historical audit result data of each object can be conveniently extracted and analyzed in real time after being imported into a data warehouse, wherein the data warehouse is a common Hive which is a data warehouse tool based on Hadoop.
S2: classifying the historical transaction data of each object unit according to a first preset economic classification mode to obtain various transaction classification data, and classifying the historical invoicing data of each object unit according to a second preset economic classification mode to obtain various invoicing classification data.
S3: and classifying the past year audit result data of each object unit according to a preset audit conclusion classification mode to obtain various audit conclusion classification data.
The historical trade data, the historical billing data and the historical audit result data can be classified by utilizing Spark, and Spark is a rapid and universal calculation engine specially designed for large-scale data processing. In this embodiment, the transaction classification data includes a budget expenditure total, an actual expenditure total and an actual expenditure detailed amount, the billing classification data includes an expenditure billing total, the audit conclusion classification data includes a normal state, an actual state, an account inconsistency and an account inconsistency, wherein the actual state indicates that the budget expenditure total and the actual expenditure total do not agree, the account inconsistency indicates that the actual expenditure total and the actual expenditure detailed amount do not agree, and the account inconsistency indicates that the actual expenditure total and the expenditure billing total do not agree. The budget expenditure total amount can be further divided into payroll expenditure budget total amount, product expenditure budget total amount and other expenditure budget total amounts according to actual conditions, the actual expenditure total amount can be further divided into payroll actual expenditure total amount, product actual expenditure total amount and other actual expenditure total amounts according to actual conditions, the actual expenditure detailed amount can also be further divided into payroll actual expenditure detailed amount, product actual expenditure detailed amount and other actual expenditure detailed amount, and the payroll actual expenditure detailed amount comprises payroll number and payroll issued by each person. Accordingly, the total amount of the expenditure invoices can be further divided into the total amount of payroll expenditure invoices, the total amount of product expenditure invoices and the total amount of other expenditure invoices.
The first preset economic classification mode is one or more of a budget expenditure total classification mode, an actual expenditure total classification mode and an actual expenditure detailed classification mode, the second preset economic classification mode is an expenditure billing total classification mode, and the preset audit conclusion classification mode is one or more of a normal classification mode, an account real non-conformity classification mode, an account non-conformity classification mode and an account non-conformity classification mode.
S4: and taking all transaction classification data, invoicing classification data and audit conclusion classification data as training samples, and training the training samples by using a naive Bayes algorithm to obtain the classifier.
S5: and carrying the newly acquired transaction data and invoicing data of the target unit into a classifier, and carrying out classification detection on the transaction data and the invoicing data of the target unit through the classifier to obtain an audit conclusion classification result of the target unit.
After the classifier performs classification detection on the transaction data and the billing data of the target unit, the audit conclusion classification of the target unit can be obtained, for example, the target unit is inconsistent with the financial account.
In this embodiment, the auditing method further includes:
s6: and marking the target unit with abnormal audit conclusion classification result as the key unit to be checked.
The classification result of the audit conclusion is abnormal, the audit conclusion of the target unit is abnormal, the abnormal condition comprises three conditions of actual account inconsistency, account and bill inconsistency, no matter which condition is the condition, the unit is indicated to have a problem, and after the unit is marked as a key unit to be checked, the audit department can be prompted to perform key checking.
In this embodiment, the training of the training samples by using the naive bayes algorithm to obtain the classifier specifically includes:
s41: let x be { a ═ a1，a2，...，amThe item to be classified is represented by x, a is used as the characteristic attribute of x and is transaction classification data and invoicing classification data of the object unit, and m is a natural number;
s42: let C ═ y1，y2，...，ynThe method comprises the steps of (1) setting a category set, wherein y is an audit conclusion category of C, and n is a natural number;
s43: computing the conditional probability P (y) using a naive Bayes algorithm1|x)，P(y2|x)，...，P(yn| x), wherein the calculation formula of the naive bayes algorithm is as follows:p (B | a) represents the probability of the occurrence of feature a at the time of occurrence of feature B. P (A) represents the probability of occurrence of feature A;
s44: obtaining a classifier according to the calculation result of the conditional probability, wherein the classifier is expressed as:
if P (y)k|x)＝max{P(y1|x)，P(y2|x)，...，P(yn| x) }, then x ∈ ykK is less than or equal to n.
In the above steps, there are various methods for calculating each conditional probability in step S43, and in this embodiment, step S43 specifically includes:
step S431: selecting an appointed item to be classified and an appointed class set as a training set;
step S432: and counting the conditional probability of each characteristic attribute under each audit conclusion classification, namely:
P(a1y1)，P(a2|y1)，...，P(am|y1)；P(a1|y2)，P(a2|y2)，...，P(am|y2)；...；P(a1|yn)，P(a2|yn)，...，P(an|yn)；
step S433: assuming that each characteristic attribute is independent, calculating conditional probability according to Bayes theorem, wherein the calculation formula is as follows:
wherein,
in the formulaIn (3), since the denominator is classified as a constant for all conclusions, it is only necessary to maximize the numerator, and since each feature attribute is conditionally independent, it can be obtained:
the invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (6)
1. An auditing method based on big data is characterized by comprising the following steps:
s1: acquiring historical trade data, historical billing data and historical audit result data of each object unit, and importing the historical trade data, the historical billing data and the historical audit result data into a data warehouse;
s2: classifying the historical transaction data of each object unit according to a first preset economic classification mode to obtain various transaction classification data, and classifying the historical invoicing data of each object unit according to a second preset economic classification mode to obtain various invoicing classification data;
s3: classifying the historical audit result data of each object unit according to a preset audit conclusion classification mode to obtain various audit conclusion classification data;
s4: taking all transaction classification data, invoicing classification data and audit conclusion classification data as training samples, and training the training samples by using a naive Bayes algorithm to obtain a classifier;
s5: and carrying the newly acquired transaction data and invoicing data of the target unit into the classifier, and carrying out classification detection on the transaction data and the invoicing data of the target unit through the classifier to obtain an audit conclusion classification result of the target unit.
2. The auditing method of claim 1, further comprising:
s6: and marking the target unit with abnormal audit conclusion classification result as the key unit to be checked.
3. The auditing method of claim 2, wherein the transaction classification data includes a budget expenditure total, an actual expenditure total and an actual expenditure detail, the invoicing classification data includes an expenditure invoicing total, and the audit conclusion classification data includes normal, financial, billing and billing non-compliance, wherein financial non-compliance indicates a budget expenditure total and an actual expenditure total, billing non-compliance indicates an actual expenditure total and an actual expenditure detail, and billing non-compliance indicates an actual expenditure total and an expenditure total.
4. The auditing method of claim 3, wherein the step of training the training samples using a naive Bayesian algorithm to obtain a classifier specifically comprises:
s41: let x be { a ═ a1，a2，...，amIs an item to be classified, wherein x represents an object unit, a is taken as the characteristic attribute of x and is taken as the object unitTransaction classification data and billing classification data, wherein m is a natural number;
s42: let C ═ y1，y2，...，ynThe method comprises the steps of (1) setting a category set, wherein y is an audit conclusion category of C, and n is a natural number;
s43: computing the conditional probability P (y) using a naive Bayes algorithm1|x)，P(y2|x)，...，P(yn| x), wherein the calculation formula of the naive bayes algorithm is as follows:p (B | a) represents the probability of the occurrence of feature a at the time of occurrence of feature B. P (A) represents the probability of occurrence of feature A;
s44: obtaining a classifier according to the calculation result of the conditional probability, wherein the classifier is expressed as: if P (y)k|x)＝max{P(y1|x)，P(y2|x)，...，P(yn| x) }, then x ∈ yiK is less than or equal to n.
5. The auditing method of claim 4, wherein step S43 specifically includes:
step S431: selecting an appointed item to be classified and an appointed class set as a training set;
step S432: and counting the conditional probability of each characteristic attribute under each audit conclusion classification, namely:
step S433: assuming that each characteristic attribute is independent, calculating conditional probability according to Bayes theorem, wherein the calculation formula is as follows:
wherein,
6. the auditing method of claim 3, wherein the first preset economic classification is one or more of a budget expenditure gross classification, an actual expenditure gross classification and an actual expenditure detail classification, the second preset economic classification is an expenditure billing gross classification, and the preset auditing conclusion classification is one or more of a normal classification, an actual non-compliance classification, a non-compliance classification and a non-compliance classification.
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