CN116975717A - Method, device and equipment for identifying violation audit - Google Patents
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Abstract
The application provides a method, a device and equipment for identifying violation auditing, which can be used in the technical field of computers in the financial field. And identifying the violation audit through a pre-trained audit prediction model so as to ensure the safety of the client account, further, storing target data to be identified in a blockchain, inputting the target data on the blockchain into the audit prediction model for identification, and finally storing an identification result in the blockchain to ensure the data safety so as to avoid that malicious tampered data influence the identification accuracy of the violation audit.
Description
Technical Field
The application relates to the technical field of computers in the financial field, in particular to a method, a device and equipment for identifying violation auditing.
Background
Currently, customers may perform business operations, such as deposit, withdrawal, information upload, printing vouchers, etc., through terminal devices provided by banking outlets, such as intelligent counters. Some of these business operations require auditing by bank personnel. However, in the process of auditing, illegal operations may be performed on the account of the client, which brings potential safety hazards to the account of the client. Therefore, how to identify the illegal audit so as to ensure the safety of the customer account is a current urgent problem to be solved.
Disclosure of Invention
The application provides a method, a device and equipment for identifying violation audit, which are used for identifying the violation audit so as to ensure the safety of a customer account.
In a first aspect, the present application provides a method of identifying a violation audit, comprising: when a target client initiates a service request of a target service at a banking website, acquiring a service risk entropy of the target service, wherein the service risk entropy is used for representing information quantity of related risks when the target client handles the target service; when the business risk entropy of the target business is larger than the business risk threshold of the business type to which the target business belongs, selecting auditing staff from a plurality of banking staff of a banking website; pushing information of auditing staff; uploading target data into a block chain, wherein the target data is data obtained after auditing and authorizing the service data of the target service by auditing staff; inputting target data stored in the blockchain into an audit prediction model to obtain a recognition result, wherein the recognition result is also used for indicating the type and the probability of the violation when the recognition result indicates that the target business has audit violations; the auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist or not, and when the auditing violations exist, the risk identification data also comprises violation types; storing the identification result in a blockchain; determining whether the rule violation probability indicated by the identification result is greater than a probability threshold value through intelligent contracts arranged on the blockchain, wherein the probability threshold value corresponds to the rule violation type indicated by the identification result; and if the rule violation probability indicated by the identification result is larger than the probability threshold value, sending risk prompt information.
In a second aspect, the present application provides an identification device comprising: the acquisition module is used for acquiring service risk entropy of the target service when the target client initiates a service request of the target service at a banking website, wherein the service risk entropy is used for representing information quantity of related risks when the target client handles the target service; the processing module is used for selecting auditing staff from a plurality of banking staff at a banking website when the business risk entropy of the target business is greater than the business risk threshold of the business type to which the target business belongs; the processing module is also used for uploading target data to the blockchain, wherein the target data is data obtained after auditing and authorizing the service data of the service request by auditing staff; the processing module is also used for inputting target data stored on the blockchain into an audit prediction model to obtain a recognition result, and the recognition result is also used for indicating the type and the probability of the violation when the recognition result indicates that the target service has audit violations; the processing module is also used for storing the identification result in the blockchain; the auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist or not, and when the auditing violations exist, the risk identification data also comprises violation types; the processing module is also used for determining whether the violation probability indicated by the identification result is greater than a probability threshold value through intelligent contracts arranged on the blockchain, and the probability threshold value corresponds to the violation type indicated by the identification result; the receiving-transmitting module is also used for transmitting risk prompt information if the rule violation probability indicated by the identification result is larger than the probability threshold.
In a third aspect, the present application provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored by the memory to implement a method as in the first aspect or in each possible implementation.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein computer-executable instructions for performing a method as in the first aspect or each possible implementation manner when executed by a processor.
According to the method, the device and the equipment for identifying the violation audit, the violation audit is identified through the pre-trained audit prediction model so as to ensure the safety of a customer account, further, target data to be identified is stored in the blockchain, the target data on the blockchain is input into the audit prediction model for identification, and finally, an identification result is stored in the blockchain so as to ensure the safety of the data, and the problem that malicious tampered data influences the identification accuracy of the violation audit is avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for identifying violation audits according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of an identification device according to an embodiment of the present application;
fig. 4 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
It should be noted that, the identification scheme of the violation audit provided by the application can be used in the computer technical field of the financial field, but the application is not limited to the above.
Aiming at the problem that potential hazards are brought to account safety of a client due to illegal operation of auditors when the client performs business operation through terminal equipment provided by banking outlets, such as an intelligent counter. According to the embodiment of the application, the illegal audit is identified through the audit prediction model, so that the safety of the client account is ensured. Further, the target data to be identified is stored in the blockchain, and the identified result is stored in the blockchain, so that the data security can be ensured, and the data can be prevented from being modified maliciously.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present application. Referring to fig. 1, terminal device 110 may be deployed at a banking outlet. For example, it may be a smart counter, other self-service device, or teller machine, etc., and terminal device 110 may be deployed with a client of a banking outlet. Of course, the application is not limited thereto, and the terminal device may be a personal computer PC, a notebook computer, a mobile phone, a mining machine, a server, or the like
The end devices 110 may be nodes in the blockchain network 100, the blockchain network 100 being comprised of a plurality of blockchain link points, the blockchain nodes may be any type of end device. The plurality of blockchain nodes in the blockchain network 100 may be terminal devices of different banking outlets, respectively.
It should be understood that, the blockchain node 110 logs in with an account of the user, so as to complete a corresponding operation according to an instruction of the user, in the embodiment of the present application, the account logged in on the blockchain node 110 may be an account corresponding to a banking website, or an account corresponding to a client, optionally, the same account may have a data reading authority or a data writing authority, or both a data reading authority and a data writing authority.
Illustratively, operations that the blockchain node 110 can perform include, but are not limited to, invoking a smart contract, making a transaction record in a block of the blockchain, determining the order of transactions and whether the transactions were successful based on a consensus algorithm.
In embodiments of the present application, "at least one" includes "one" or "a plurality of"; "plurality" or "at least two" means "two" or "more than two". "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
It should be noted that, the execution body of the embodiment of the present application may be a terminal device or a component in the terminal device, such as a chip or a chip system in an electronic device or other functional modules capable of calling a program and executing the program. The terminal device may be the terminal device 110 of fig. 1 described above.
Fig. 2 is a flow chart of a method 200 for identifying offence auditing according to an embodiment of the present application. As shown in connection with fig. 2, the method 200 includes the following S210 to S240.
S210, when a target client initiates a service request of a target service at a banking website, acquiring a service risk entropy of the target service, wherein the service risk entropy is used for representing information quantity of related risks when the target client handles the target service;
s220, selecting auditing staff from a plurality of banking staff of a banking website when the business risk entropy of the target business is greater than the business risk threshold of the business type to which the target business belongs;
s230, pushing information to auditing staff;
s240, uploading target data into a blockchain, wherein the target data is data obtained after auditing and authorizing the business data of the target business by auditing staff;
s250, inputting target data stored in the blockchain into an audit prediction model to obtain a recognition result, wherein the recognition result is also used for indicating the type and the probability of the violation when the recognition result indicates that the target business has audit violations;
s260, storing the identification result in a block chain;
s270, determining whether the rule breaking probability indicated by the identification result is larger than a probability threshold value or not through intelligent contracts arranged on the blockchain, wherein the probability threshold value corresponds to the rule breaking type indicated by the identification result;
And S280, if the rule violation probability indicated by the identification result is greater than the probability threshold, sending risk prompt information.
The target client initiates a service request of the target service on the terminal equipment of the banking website, and correspondingly, the terminal equipment can generate the service request according to the operation of the user. By way of example, the target business may be a deposit business, a withdrawal business, a financial business, a document extraction business, and the like. It should be noted that different service requests have different service risks, and the service risks may also be different when different clients initiate the same target service. In the embodiment of the application, the business risk can be embodied through the business risk entropy.
In S220, the terminal device may determine whether to audit the service data of the target service according to the service risk entropy of the target service. For example, the terminal device may determine to audit the service data of the target service when the service risk entropy is greater than the service risk threshold of the service type to which the target service belongs. In this case, the terminal device may select an audit staff member for auditing the service data of the target service from a plurality of bank staff members of the banking website.
In S230, the terminal device may push information of the auditing staff, for example, at least one of the work number, the job title, and the name of the auditing staff. The terminal device can display information of the auditing staff through a display of the terminal device or a connected external display; or the terminal device may send information of the auditing staff to other devices, for example, push information of the auditing staff to a terminal of the auditing staff, so as to remind the auditing staff to audit service data of the target service.
After auditing the business data of the target business, if the business data are normal, the auditing staff can operate to authorize the business data, and if the business data are abnormal, the auditing staff can operate to not authorize (or prohibit or reject) the target business requested by the business request. After auditing and authorizing the business data of the target business, the auditing staff obtains the target data, wherein the target data can comprise the business data and related data of auditing and authorization.
In S240, the terminal device uploads the target data to the blockchain; in S250, the target data stored on the blockchain is input into the audit prediction model to obtain the recognition result. Writing and reading data on the block chain are realized, and the data can be prevented from being tampered maliciously.
The auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist, and when auditing violations exist, the risk identification data also comprises a violation type. The risk identification data can be identified after the corresponding business data after the audit and the authorization find the illegal audit event in the business implementation process, namely the risk identification data can be understood as a true value for expressing whether the audit and the authorization exist in the business data after the audit and the authorization, in the model training process, the audit prediction model outputs a predicted identification result according to the historical business data after the staff audit and the authorization, and the predicted identification result is compared with the true value for iterative training to obtain a trained audit prediction model.
Therefore, in S250, the terminal device inputs the target data stored in the blockchain into the audit prediction model obtained by training, so as to obtain an accurate recognition result. The identification result can indicate whether the target service has audit violations or not, and when the identification result indicates that the target service has audit violations, the identification result can also indicate the violation type and the violation probability of the target service; or the recognition result can directly indicate the violation type and the violation probability, the indication type can be null when the target service has no audit violation, the violation probability can be zero, and the indication type is not null and the violation probability is not zero when the target service has audit violation.
In S260, the terminal device stores the identification result in the blockchain, so that malicious tampering of the identification result can be avoided.
It should be appreciated that, in order to ensure account security of the target customer, when the recognition result indicates that the target service has a high probability of violation, an audit violation of the target service may be alerted.
The terminal device determines whether the rule violation probability indicated by the identification result is greater than a probability threshold value through an intelligent contract arranged on the blockchain, and sends risk prompt information when the rule violation probability indicated by the identification result is greater than the probability threshold value. Alternatively, the probability threshold may be a preset value.
Alternatively, the probability threshold may be that the types of violations correspond, i.e., different types of violations may correspond to different probability thresholds. For example, the probability threshold may be 50% when the risk of violation is counterfeited by an auditing worker.
Optionally, in S280 above, the terminal device may send the risk prompt information to a terminal of the bank risk controller, for example, send a message to the terminal of the bank risk controller through a wireless network communication technology.
In the embodiment of the application, the terminal equipment identifies the illegal audit through the pre-trained audit prediction model so as to ensure the safety of the client account, further, the target data to be identified is stored in the blockchain, the target data on the blockchain is input into the audit prediction model for identification, and finally, the identification result is stored in the blockchain, so that the data safety is ensured, and the identification accuracy of the illegal audit is prevented from being influenced by malicious tampered data.
In some possible implementations of S210 above, obtaining the service risk entropy of the target service may include:
s211, acquiring historical service data of a target client under the target service type of the target service.
The historical business data may include risk business data and non-risk business data, wherein the risk business data refers to business data related to risks, such as business data generated when the risk entropy of business handled by a target client is greater than a business risk threshold, and the non-risk business data refers to business data generated when the risk entropy of business handled by the target client is less than the business risk threshold.
Optionally, if the historical service data of the target client is less, for example, when the historical service data is less than a preset service quantity threshold, the historical service data of other clients under the target service type under the client type to which the target client belongs may be obtained.
S212, dividing the risk business data into data sets of corresponding business dimensions according to the business dimensions of the risk business data to obtain at least one risk business data set, wherein the business dimensions can be business channels, business time or business areas. Optionally, if at least two risk service data sets are obtained, any two risk service data sets in the at least two risk service data sets are mutually exclusive.
S213, regarding each risk service data set in at least one risk service data set, taking the ratio of the service quantity in the risk service data set to the service quantity of the historical service data as a first risk ratio.
In the above S212 and S213, the terminal device may determine, for the historical service data in each service dimension, the respective first risk ratio P, and further determine the service risk entropy P in each service dimension in S214 as follows.
S214, determining a business risk entropy P= -plog corresponding to the first risk ratio 2 p。
S215, determining residual risk entropy as Q= - (1-Q) log 2 (1-q), wherein q is the sum of at least one first risk ratio value respectively corresponding to at least one risk service data set.
S216, taking the sum of at least one business risk entropy corresponding to the residual risk entropy Q and the at least one first risk ratio as the business risk entropy of the target business. For example, the business risk entropy W of the target business is equal to the business risk entropy P of the data set of the remaining risk entropy Q and business dimension 1 1 Business risk entropy P for data set of business dimension 2 2 Business risk entropy P of data set of business dimension 3 3 And (3) summing.
In some possible implementations of S220, the terminal device selects the auditing staff member from a plurality of banking staff members of the banking website, including:
s221-1, acquiring the biological characteristic data of the target client. The biometric data may include facial data and/or fingerprint data, among other things. The biometric data of the target customer may be pre-entered or may be currently acquired, as the application is not limited in this regard.
S221-2, determining a biological matching value corresponding to each of a plurality of banking staff at a banking website, wherein the biological matching value is used for representing the matching degree of the biological characteristic data of the target client and the biological characteristic data of the banking staff.
It should be appreciated that a higher degree of match between a banking staff member and a target customer indicates a higher association of the banking staff member with the target customer, e.g., a higher frequency of operation of the banking staff member with an account of the target customer, a higher frequency of operation of the banking staff member with an account of an associated customer of the target customer, etc. Thus, consider that the higher the degree of match between the bank staff and the target customer, the higher the probability of a violation audit.
S221-3, determining corresponding relation risk entropy of the plurality of bank staff in the first corresponding relation according to biological matching values corresponding to the plurality of bank staff, wherein the relation risk entropy is used for representing information quantity of related risks when the bank staff reviews the target business of the client.
The first correspondence may be a correspondence between a biological matching value and a relationship risk entropy. The terminal device may obtain a plurality of historical audit data of the banking website, determine, according to the historical audit data, a biological matching value and a relationship risk entropy corresponding to the historical audit data, and combine the biological matching value and the relationship risk entropy corresponding to the historical audit data to obtain the first corresponding relationship. The biological matching value and the relation risk entropy corresponding to the historical auditing data are the biological matching value and the relation risk entropy corresponding to the bank staff and the clients contained in the historical auditing data.
The manner of obtaining the relationship risk entropy may be similar to that of the business risk entropy, and will not be described in detail for brevity.
S221-4, taking the bank staff with the minimum relation risk entropy as the auditing staff in the plurality of bank staff according to the relation risk entropy corresponding to the plurality of bank staff.
Generally, the higher the biological matching value corresponding to the bank staff, the higher the relation risk entropy, namely the higher the relation risk entropy, and the higher the probability that the bank staff has illegal auditing on the target business. Therefore, the bank staff with the minimum relation risk entropy is considered as the auditing staff, so that the risk of illegal auditing of the target business can be reduced.
In other possible implementations of S220, the terminal device selects the auditing staff member from a plurality of banking staff members of the banking website, including:
s222-1, determining the relevant entropy of each of a plurality of banking staff and a target client, wherein the relevant entropy is used for representing the information quantity of the relevance of the banking staff and the target client.
For example, the terminal device may determine, for each of a plurality of banking staff members of a banking website and each of a plurality of clients that the banking staff members audit, the data amounts of the banking staff members and the clients respectively corresponding in at least one common dimension, and further determine, for each common dimension in at least one common dimension, the relevant entropy s= -slog of the banking staff members and the clients in the common dimension according to the ratio S of the data amounts of the banking staff members and the clients corresponding in the common dimension to a preset value 2 s-(1-s)log 2 (1-s). Further, in each public dimension of the at least one public dimension, the terminal device takes the maximum correlation entropy of the correlation entropies between each of the plurality of bank staff and the plurality of clients audited by the bank staff as the maximum correlation entropies of the public dimension, and the terminal device takes the ratio of the correlation entropies between each of the plurality of bank staff and one of the plurality of clients audited by the bank staff and the maximum correlation entropies as the correction correlation entropies between the bank staff and the clients, and further takes the maximum value of the correction correlation entropies between the bank staff and the clients in the at least one public dimension as the correlation entropies between the bank staff and the target clients.
Optionally, the public dimension may include at least one of a public customer dimension, a public transaction dimension, and a related audit dimension, the data quantity in the public customer dimension includes a quantity of public related customers of the bank staff and the customers, the data quantity in the public transaction dimension includes a quantity of public transaction data of the bank staff and the customers, and the data quantity in the related audit dimension is a quantity of audit data of the bank staff for the customers.
The preset value may be greater than twice the number of data in the common dimension.
S222-2, selecting a bank staff member with the minimum relevant entropy from the plurality of bank staff members as an auditing staff member according to the relevant entropy between the plurality of bank staff members and the target client.
Similar to the relation risk entropy, the higher the relation entropy, the higher the probability that the bank staff has illegal auditing on the target business. Therefore, the bank staff with the minimum relevant entropy is considered as the auditing staff, so that the risk of illegal auditing of the target business can be reduced.
In the step S222-2, the terminal device may determine, according to the relative entropy between the banking staff and the target client, a relationship risk entropy corresponding to the banking staff in a second corresponding relationship, where the second corresponding relationship is a corresponding relationship between the relative entropy and the relationship risk entropy, and further, the terminal device uses, as the auditing staff, a banking staff with the minimum relationship risk entropy among the banking staff according to the relationship risk entropy corresponding to the banking staff.
The terminal device may obtain a plurality of historical audit data of the banking website, and for each historical audit data in the plurality of historical audit data, the terminal device may determine a relevant entropy and a relationship risk entropy of the historical audit data according to the historical audit data, and further combine the relevant entropy and the relationship risk entropy respectively corresponding to the plurality of historical audit data to obtain the second corresponding relationship. The related entropy and the relationship risk entropy corresponding to the historical auditing data can be related entropy and relationship risk entropy corresponding to a bank staff and a client contained in the historical auditing data.
In some embodiments, the terminal device determines a business risk threshold for the banking website as follows:
the method comprises the steps that terminal equipment obtains a plurality of website relation models, wherein the website relation models are used for determining the size relation of auditing risks of two banking websites; determining the auditing risk size relation between the current banking website and each other banking website according to the website relation models; determining first-class network points and second-class network points according to the size relation of the auditing risks of the current banking network points and each other banking network point, wherein the auditing risks of the first-class network points are greater than those of the current banking network points, and the auditing risks of the second-class network points are less than those of the current banking network points; according to the historical service data of the target service type corresponding to the first type of website, determining an upper bound of the service risk entropy corresponding to the current banking website, and the historical service data of the target service type corresponding to the second type of website, and determining a lower bound of the service risk entropy corresponding to the current banking website; and determining a business risk threshold of the current banking website according to the upper bound of the business risk entropy corresponding to the current banking website and the lower bound of the corresponding business risk entropy, wherein the business risk threshold is smaller than the upper bound of the business risk entropy and larger than the lower bound of the business risk entropy.
The website relation model can be a rule of a rule learning method in machine learning, each rule corresponds to two variables, each variable is a banking website, the corresponding rule body is a binary relation of values of characteristics of the two variables corresponding to the banking website, and the corresponding rule head is a size relation of auditing risks of the two variables. Each banking point feature corresponds to a binary relationship that is used to determine the relationship of the two values of the banking point feature.
In some embodiments, the determining, by the terminal device, an upper bound of a business risk entropy corresponding to the target banking node according to historical business data of the target business type corresponding to the first type node includes: when the number of the businesses contained in the historical business data of the first type of network points corresponding to the target business type is smaller than the set number value, the terminal equipment executes the following steps:
constructing a bank website queue, and sequentially adding the bank website contained in the first type website into the bank website queue; the steps of circularly executing are carried out until the bank network point queue is empty or the number of the businesses contained in the historical business data of the first type network point corresponding to the target business type is larger than or equal to the set number value: taking out the banking outlets at the opposite first position from the banking outlet queue; according to the multiple website relation models, sequentially adding corresponding banking websites with the auditing risks being greater than those of the retrieved banking websites into a banking website queue and adding the banking websites into first-class websites; when the bank network point queue is empty or the number of the businesses contained in the historical business data of the first network point corresponding to the target business type is larger than or equal to a set number value, determining the upper bound of the business risk entropy corresponding to the target bank network point according to the historical business data of the first network point corresponding to the target business type.
It should be noted that, according to the historical service data of the second type website corresponding to the target service type, the lower bound of the service risk entropy corresponding to the current banking website is determined, which has been described in the foregoing embodiment, and is not repeated for brevity.
Fig. 3 is a schematic block diagram of an identification device for violation audit according to an embodiment of the present application. As shown in fig. 3, the apparatus 300 may include: an acquisition module 310, a processing module 320 and a transceiver module 330.
The obtaining module 310 may be configured to obtain, when the target client initiates a service request of the target service at a banking website, a service risk entropy of the target service, where the service risk entropy is used to represent an information amount of a related risk of the target client when the target client handles the target service; the processing module 320 may be configured to select an audit staff from a plurality of banking staff at a banking website when a business risk entropy of the target business is greater than a business risk threshold of a business type to which the target business belongs; the processing module 320 is further configured to upload target data to the blockchain, where the target data is data obtained after the auditing worker performs auditing and authorization on the service data of the service request; the processing module 320 is further configured to input the target data stored in the blockchain into an audit prediction model to obtain a recognition result, where the recognition result is further used to indicate a violation type and a violation probability when the recognition result indicates that the target service has audit violations; the processing module 320 is further configured to store the identification result in the blockchain; the auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist or not, and when the auditing violations exist, the risk identification data also comprises violation types; the processing module 320 is further configured to determine, through an intelligent contract set on the blockchain, whether a rule violation probability indicated by the recognition result is greater than a probability threshold, where the probability threshold corresponds to the rule violation type indicated by the recognition result; the transceiver module 330 is further configured to send risk prompting information if the probability of the violation indicated by the recognition result is greater than the probability threshold.
In some embodiments, the acquisition module 310 is specifically configured to: acquiring historical service data of a target client under a target service type to which a target service belongs, wherein the historical service data comprises risk service data, and the risk service data is service data related to risks; dividing the risk business data into data sets of corresponding business dimensions according to the business dimensions of the risk business data to obtain at least one risk business data set, wherein the business dimensions comprise at least one of business channels, business time and business areas; taking the ratio of the number of services in the risk service data sets to the number of services of the historical service data as a first risk ratio for each risk service data set in at least one risk service data set; determining a business risk entropy P= -plog corresponding to the first risk ratio 2 p, wherein p is a first risk ratio; determining the residual risk entropy as Q= - (1-Q) log 2 (1-q), wherein q is the sum of at least one first risk ratio value corresponding to at least one risk service data set, respectively; and taking the sum of at least one business risk entropy corresponding to the residual risk entropy Q and the at least one first risk ratio as the business risk entropy of the target business.
In some embodiments, the processing module 320 is specifically configured to: acquiring biological characteristic data of a target client; determining a biological matching value corresponding to each of a plurality of banking staff at a banking website, wherein the biological matching value is used for representing the matching degree of the biological characteristic data of a target client and the biological characteristic data of the banking staff; determining a corresponding relation risk entropy of the plurality of bank staff respectively according to the biological matching values respectively corresponding to the plurality of bank staff in a first corresponding relation, wherein the first corresponding relation is a corresponding relation between the biological matching values and the relation risk entropy, and the relation risk entropy is used for representing information quantity of related risks when the bank staff inspects target business of clients; and taking the bank staff with the minimum relation risk entropy as an auditing staff in the plurality of bank staff according to the relation risk entropy corresponding to the plurality of bank staff.
In some embodiments, the processing module 320 is further to: acquiring a plurality of historical audit data of banking outlets; aiming at each historical auditing data in a plurality of historical auditing data, determining a biological matching value and a relation risk entropy corresponding to the historical auditing data according to the historical auditing data; and combining the biological matching values and the relation risk entropy corresponding to the historical auditing data respectively to obtain a first corresponding relation.
In some embodiments, the processing module 320 is specifically configured to: determining, for each of a plurality of banking staff members, a correlation entropy of the banking staff member with the target client, wherein the correlation entropy is used for characterizing an information amount of correlation of the banking staff member with the target client; and selecting the bank staff with the minimum relevant entropy from the plurality of bank staff as the auditing staff according to the relevant entropy of the plurality of bank staff and the target client respectively.
In some embodiments, the processing module 320 is specifically configured to: for each of a plurality of banking staff members of a banking website and each of a plurality of clients audited by the banking staff members, determining data amounts respectively corresponding to the banking staff members and the clients in at least one public dimension, wherein the public dimension comprises at least one of a public client dimension, a public transaction dimension and a related audit dimension, the data amounts in the public client dimension comprise the data amounts of the banking staff members and the clients in public related clients, the data amounts in the public transaction dimension comprise the data amounts of the banking staff members and the clients in public transaction, and the data amounts in the related audit dimension are the audit data amounts of the banking staff members for clients; for each public dimension in at least one public dimension, determining the related entropy S= -slog of the banking staff and the client in the public dimension according to the ratio S of the data quantity corresponding to the banking staff and the client in the public dimension to the preset value 2 s-(1-s)log 2 (1-s) the preset value is two times greater than the data quantity of the common dimension; taking the maximum correlation entropy in the correlation entropy between each of a plurality of banking staff and a plurality of clients for auditing as the maximum correlation entropy of the common dimension under each of at least one common dimension;taking the ratio of the correlation entropy between each of a plurality of banking staff and one of a plurality of clients audited by the banking staff and the maximum correlation entropy as the correction correlation entropy of the banking staff and the clients; and taking the maximum value of the correction correlation entropy of the bank staff and the client in at least one common dimension as the correlation entropy of the bank staff and the target client.
In some embodiments, the processing module 320 is specifically configured to: according to the relative entropy of the bank staff and the target customer, determining a corresponding relation risk entropy of the bank staff in a second corresponding relation, wherein the second corresponding relation is a corresponding relation between the relative entropy and the relation risk entropy, and the relation risk entropy is used for representing information quantity of related risks when the bank staff reviews the target business of the customer; and taking the bank staff with the minimum relation risk entropy as an auditing staff in the plurality of bank staff according to the relation risk entropy corresponding to the plurality of bank staff.
In some embodiments, the processing module 322 is further to: acquiring a plurality of historical audit data of banking outlets; for each historical auditing data in the plurality of historical auditing data, determining the related entropy and the relation risk entropy of the historical auditing data according to the historical auditing data; and combining the related entropy and the relation risk entropy corresponding to the historical auditing data respectively to obtain a second corresponding relation.
The specific process of executing the corresponding steps by each module is described in detail in the above method embodiments, and for brevity, will not be described in detail herein.
The division of each unit/module in the above device is only a division of a logic function, and may be fully or partially integrated into one physical entity or may be physically separated when actually implemented.
Fig. 4 is a schematic block diagram of an electronic device 400 according to an embodiment of the present application. The apparatus 400 may include a processor 410 and a memory 420, the processor 410 and the memory 420 communicating with each other through an internal connection path, the memory 420 for storing instructions, the processor 410 for executing the instructions stored by the memory 420.
Alternatively, the memory 420 may include read-only memory and random access memory, and provide instructions and data to the processor 410. The memory 420 may be a separate device or may be integrated into the processor 410.
In some embodiments, the device 400 may also include an input interface 430. The processor 410 may control the input interface 430 to communicate with other devices or chips, and in particular, may acquire information or data sent by the other devices or chips.
In some embodiments, the device 400 may also include an output interface 440. Wherein the processor 410 may control the output interface 440 to communicate with other devices or chips, and in particular, may output information or data to other devices or chips.
In some embodiments, the apparatus 400 may implement respective flows of the methods in the embodiments of the present application, which are not described herein for brevity.
It should be appreciated that the processor of an embodiment of the present application may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It will be appreciated that the memory in embodiments of the application may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
The embodiment of the application also provides a computer readable storage medium for storing a computer program. In some embodiments, the computer program makes a computer execute corresponding processes in the methods of the embodiments of the present application, which are not described herein for brevity.
The embodiment of the application also provides a computer program product comprising computer program instructions. In some embodiments, the computer program instructions cause a computer to execute corresponding processes in the methods of the embodiments of the present application, which are not described herein for brevity.
The embodiment of the application also provides a computer program. In some embodiments, when the computer program runs on a computer, the computer is caused to execute corresponding processes in the methods of the embodiments of the present application, and for brevity, a detailed description is omitted herein.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method of identifying a violation audit, comprising:
when a target client initiates a service request of a target service at a banking website, acquiring a service risk entropy of the target service, wherein the service risk entropy is used for representing information quantity of related risks when the target client handles the target service;
when the business risk entropy of the target business is larger than the business risk threshold of the business type to which the target business belongs, selecting auditing staff from a plurality of banking staff of the banking website;
pushing information of the auditing staff;
uploading target data into a block chain, wherein the target data is data obtained after auditing and authorizing the service data of the target service by the auditing staff;
inputting the target data stored in the blockchain into an audit prediction model to obtain a recognition result, wherein the recognition result is also used for indicating the type and the probability of the violation when the recognition result indicates that the target service has audit violations;
The auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist or not, and when auditing violations exist, the risk identification data also comprises violation types;
storing the identification result in a blockchain;
determining whether the violation probability indicated by the identification result is greater than a probability threshold value through an intelligent contract arranged on a blockchain, wherein the probability threshold value corresponds to the violation type indicated by the identification result;
and if the rule violation probability indicated by the identification result is larger than the probability threshold, sending risk prompt information.
2. The method of claim 1, wherein the obtaining the business risk entropy of the target business comprises:
acquiring historical service data of the target client under the target service type of the target service, wherein the historical service data comprises risk service data, and the risk service data is service data related to risks;
dividing the risk service data into data sets of corresponding service dimensions according to the service dimensions of the risk service data to obtain at least one risk service data set;
Taking the ratio of the number of services in the risk service data set to the number of services of the historical service data as a first risk ratio for each risk service data set in at least one risk service data set;
determining a business risk entropy P= -plog corresponding to the first risk ratio 2 p, wherein p is the first risk ratio;
determining the residual risk entropy as Q= - (1-Q) log 2 (1-q), wherein q is a sum of at least one first risk ratio value respectively corresponding to the at least one risk service data set;
and taking the sum of at least one business risk entropy corresponding to the residual risk entropy Q and the at least one first risk ratio as the business risk entropy of the target business.
3. The method of claim 1 or 2, wherein said selecting an audit staff member from a plurality of banking staff members of said banking outlet comprises:
acquiring biometric data of the target client;
determining a biological matching value corresponding to each of a plurality of banking staff at the banking website, wherein the biological matching value is used for representing the matching degree of the biological characteristic data of the target client and the biological characteristic data of the banking staff;
Determining corresponding relation risk entropy of the plurality of bank staff respectively in a first corresponding relation according to the biological matching values respectively corresponding to the plurality of bank staff, wherein the first corresponding relation is a corresponding relation between the biological matching values and the relation risk entropy, and the relation risk entropy is used for representing information quantity of related risks when the bank staff inspects target business of clients;
and taking the bank staff with the minimum relation risk entropy as the auditing staff according to the relation risk entropy corresponding to the bank staff respectively.
4. A method as recited in claim 3, further comprising:
acquiring a plurality of historical audit data of the banking website;
determining a biological matching value and a relationship risk entropy corresponding to the historical auditing data according to the historical auditing data aiming at each of the plurality of historical auditing data;
and combining the biological matching values and the relation risk entropy corresponding to the historical auditing data respectively to obtain the first corresponding relation.
5. The method of claim 1 or 2, wherein said selecting an audit staff member from a plurality of banking staff members of said banking outlet comprises:
Determining, for each of the plurality of banking staff members, a correlation entropy of the banking staff member with the target customer, wherein the correlation entropy is used to characterize an amount of information of a correlation of the banking staff member with the target customer;
and selecting the bank staff with the minimum relevant entropy from the plurality of bank staff as the auditing staff according to the relevant entropy of the plurality of bank staff and the target client respectively.
6. The method of claim 5, wherein said determining the entropy associated with the banking staff member and the target customer comprises:
determining, for each of a plurality of banking staff members of the banking website and each of a plurality of customers that the banking staff members audit, a data quantity of the banking staff members that corresponds to the customers in at least one common dimension, respectively, the common dimension including at least one of a common customer dimension, a common transaction dimension, and a related audit dimension, the data quantity in the common customer dimension including a quantity of common related customers of the banking staff members and the customers, the data quantity in the common transaction dimension including a quantity of common transaction data of the banking staff members and the customers, the data quantity in the related audit dimension being a quantity of audit data that the banking staff members audit for the customers;
For each public dimension of the at least one public dimension, determining the related entropy S= -slog of the banking staff and the client in the public dimension according to the ratio S of the data quantity corresponding to the banking staff and the client in the public dimension to a preset value 2 s-(1-s)log 2 (1-s) the preset value is greater than twice the number of data in the common dimension;
under each public dimension of the at least one public dimension, taking the maximum correlation entropy in the correlation entropy between each bank staff of the plurality of bank staff and the plurality of clients for auditing as the maximum correlation entropy of the public dimension;
taking the ratio of the correlation entropy between each of the plurality of bank staff and one of the plurality of clients audited by the bank staff and the maximum correlation entropy as the corrected correlation entropy of the bank staff and the client;
and taking the maximum value of the correction correlation entropy of the bank staff and the client in the at least one common dimension as the correlation entropy of the bank staff and the target client.
7. The method of claim 5, wherein selecting the banking staff member with the smallest associated entropy among the banking staff members as the auditing staff member based on the associated entropy of the banking staff member with the target client, respectively, comprises:
According to the related entropy of the bank staff and the target client, determining a corresponding relationship risk entropy of the bank staff in a second corresponding relationship, wherein the second corresponding relationship is a corresponding relationship between the related entropy and the relationship risk entropy, and the relationship risk entropy is used for representing information quantity of related risks when the bank staff examines the target business of the client;
and taking the bank staff with the minimum relation risk entropy as the auditing staff according to the relation risk entropy corresponding to the bank staff respectively.
8. The method as recited in claim 7, further comprising:
acquiring a plurality of historical audit data of the banking website;
for each history audit data in the plurality of history audit data, determining a related entropy and a relationship risk entropy of the history audit data according to the history audit data;
and combining the related entropy and the relation risk entropy corresponding to the historical auditing data respectively to obtain the second corresponding relation.
9. An identification device for violation auditing, comprising:
the system comprises an acquisition module, a target client and a storage module, wherein the acquisition module is used for acquiring service risk entropy of a target service when the target client initiates a service request of the target service at a banking website, wherein the service risk entropy is used for representing information quantity of related risks when the target client handles the target service;
The processing module is used for selecting auditing staff from a plurality of banking staff of the banking website when the business risk entropy of the target business is larger than the business risk threshold of the business type to which the target business belongs;
the processing module is also used for uploading target data to a blockchain, wherein the target data is data obtained after the auditing staff audits and authorizes the service data of the service request;
the processing module is further used for inputting the target data stored on the blockchain into an audit prediction model to obtain a recognition result, and the recognition result is further used for indicating the type and the probability of the violation when the recognition result indicates that the target service has audit violations;
the processing module is further used for storing the identification result in a blockchain;
the auditing prediction model is obtained by training according to historical business data after auditing and authorization of staff and risk identification data, wherein the risk identification data comprises an identification of whether auditing violations exist or not, and when auditing violations exist, the risk identification data also comprises violation types;
the processing module is further used for determining whether the violation probability indicated by the identification result is larger than a probability threshold value through an intelligent contract arranged on the blockchain, and the probability threshold value corresponds to the violation type indicated by the identification result;
The receiving-transmitting module is further used for sending risk prompt information if the rule violation probability indicated by the identification result is larger than the probability threshold.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 8.
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