CN114926041A

CN114926041A - Data risk analysis method and device, computer equipment and storage medium

Info

Publication number: CN114926041A
Application number: CN202210587029.9A
Authority: CN
Inventors: 杨先吉
Original assignee: Ping An Bank Co Ltd
Current assignee: Ping An Bank Co Ltd
Priority date: 2022-05-27
Filing date: 2022-05-27
Publication date: 2022-08-19

Abstract

The application is applicable to the technical field of risk management and control, and provides a data risk analysis method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: performing data extraction on system loading data based on a plurality of set risk control dimensions to obtain attribute data and additional data of the system loading data in different risk control dimensions; respectively arranging and combining the attribute data with different additional data to form risk assessment data combination items under different analysis granularities; screening out target risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the target risk analysis granularity from the risk assessment data combination items on the basis of the attribute data and the additional data; and performing risk analysis and processing based on the target risk assessment data combination item to obtain a target risk analysis result. According to the scheme, the original data are effectively utilized, and the accuracy and the comprehensiveness of the data risk analysis result are improved.

Description

Data risk analysis method and device, computer equipment and storage medium

Technical Field

The application belongs to the technical field of risk management and control, and particularly relates to a data risk analysis method and device, computer equipment and a storage medium.

Background

The risk management refers to a process of performing risk control around a total operation target in each link of enterprise management and an operation process to achieve risk avoidance, and becomes an important link of enterprise operation.

In the enterprise operation process, especially for some large enterprises, massive operation data can be generated. The current common risk management is usually system development according to definite fixed requirements or data management by using reports, so as to realize risk prediction based on data analysis.

In specific application, when data analysis is performed on a risk management object, the data to be faced is often massive data, so that sufficient utilization of information resources is difficult to effectively achieve, a database stores massive data, but the risk analysis result is lack of accuracy and comprehensiveness, and the analysis efficiency is low.

Disclosure of Invention

The embodiment of the application provides a data risk analysis method and device, computer equipment and a storage medium, and aims to solve the problems that in the prior art, massive information resources are difficult to effectively utilize, so that risk analysis results lack accuracy and comprehensiveness, and analysis efficiency is low.

A first aspect of an embodiment of the present application provides a data risk analysis method, including:

performing data extraction on system loading data based on a plurality of set risk control dimensions to obtain attribute data and additional data except the attribute data of the system loading data in different risk control dimensions;

for each risk management and control dimension, the attribute data and different additional data are arranged and combined to form risk assessment data combination items under different analysis granularities; acquiring a target risk analysis object and a target risk analysis granularity;

screening target risk assessment data combination items which are matched with the target risk analysis object and have analysis granularity consistent with the target risk analysis granularity from the risk assessment data combination items on the basis of the attribute data and the additional data;

and performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result.

Optionally, the performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result includes:

determining a target risk control dimension to which the target risk assessment data combination item belongs;

selecting a target risk analysis model matched with the target risk control dimension from a risk analysis model pool based on the target risk control dimension;

and performing risk analysis processing on target attribute data and target additional data contained in the target risk assessment data combination item based on the target risk analysis model to obtain a target risk analysis result.

Optionally, the target attribute data includes data content of a target data type, and the target additional data includes service scene data; the risk analysis processing is performed on the target attribute data and the target additional data contained in the target risk assessment data combination item based on the target risk analysis model to obtain the target risk analysis result, and the method comprises the following steps:

based on the target risk analysis model, performing service scene classification prediction on the data content of the target data type to obtain a probability value that the data content belongs to a service scene corresponding to the service scene data;

and acquiring the authenticity analysis result of the data content of the target data type under the service scene data based on the probability value.

Optionally, before the step of screening the risk assessment data combination items to obtain target risk assessment data combination items which match the target risk analysis object and have analysis granularity consistent with the target risk analysis granularity based on the attribute data and the additional data, the method further includes:

receiving a first risk analysis instruction triggered by an option selection operation of a user terminal by a user, wherein the option selection operation comprises a data option selection operation and a data granularity option selection operation; analyzing the first risk analysis instruction to obtain an option identifier which is carried in the first risk analysis instruction and corresponds to the option selection operation;

and matching to obtain the target risk analysis object corresponding to the data option selection operation and the target risk analysis granularity corresponding to the data granularity option selection operation based on the option identifier.

Optionally, after performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result, the method further includes:

receiving a second risk analysis instruction triggered by a user through option adjustment operation of the user terminal, wherein the option adjustment operation comprises data granularity option adjustment operation;

analyzing the second risk analysis instruction to obtain an identifier of an adjusted option corresponding to the data granularity option adjustment operation, wherein the identifier is carried in the second risk analysis instruction;

matching to obtain adjusted risk analysis granularity corresponding to the option adjusting operation based on the identifier of the adjusted option;

screening out adjusted risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the adjusted risk analysis granularity from the risk assessment data combination items on the basis of the attribute data and the additional data;

and performing risk analysis processing based on the adjusted risk assessment data combination item to obtain an adjusted risk analysis result.

Optionally, after the risk analysis processing is performed based on the adjusted risk assessment data combination item and an adjusted risk analysis result is obtained, the method further includes:

outputting the adjusted risk analysis result to a first display area of a display interface in the user terminal; the display interface further comprises a second display area adjacent to the first display area, and the target risk analysis result is displayed in the second display area.

Optionally, before performing data extraction on system loading data based on a plurality of set risk control dimensions to obtain attribute data of the system loading data in different risk control dimensions and additional data except the attribute data, the method further includes:

acquiring original data from different sources from an external data system based on a data loading script;

according to the data commonality information among the original data, carrying out data integration on the original data to obtain target data;

and loading the target data to a local system to obtain the system loading data.

A second aspect of an embodiment of the present application provides a data risk analysis device, including:

the data extraction module is used for extracting data of system loading data based on a plurality of set risk control dimensions to obtain attribute data of the system loading data in different risk control dimensions and additional data except the attribute data;

the data combination module is used for arranging and combining the attribute data and different additional data respectively for each risk control dimension to form risk assessment data combination items under different analysis granularities;

the acquisition module is used for acquiring a target risk analysis object and target risk analysis granularity;

the data screening module is used for screening the risk assessment data combination items to obtain target risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the target risk analysis granularity;

and the data analysis module is used for carrying out risk analysis and processing based on the target risk assessment data combination item to obtain a target risk analysis result.

A third aspect of embodiments of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, performs the steps of the method according to the first aspect.

A fifth aspect of the present application provides a computer program product, which, when run on a terminal, causes the terminal to perform the steps of the method of the first aspect described above.

Therefore, in the embodiment of the application, after attribute data and additional data are divided from a data source based on risk management and control dimensions, the data source is arranged and combined to form risk evaluation data combination items under different analysis granularities, a basic data unit for risk evaluation is formed, data to be analyzed is sorted and screened on the basis, risk analysis processing on a risk analysis object under the risk analysis granularity is implemented, and through matching and convergence of the data, the risk evaluation data source can face users uniformly, effective utilization of system loading data is achieved, accuracy and comprehensiveness of data risk analysis results are improved, different risk evaluation requirements are met, and risk analysis efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

Fig. 1 is a first flowchart of a data risk analysis method provided in an embodiment of the present application;

fig. 2 is a second flowchart of a data risk analysis method provided in the embodiment of the present application;

fig. 3 is a structural diagram of a data risk analysis device according to an embodiment of the present application;

fig. 4 is a block diagram of a computer device according to an embodiment of the present disclosure.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

In particular implementations, the terminals described in embodiments of the present application include, but are not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers having touch sensitive surfaces (e.g., touch screen displays and/or touch pads). It should also be understood that in some embodiments, the device is not a portable communication device, but is a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or a touchpad).

In the discussion that follows, a terminal that includes a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and/or joystick.

The terminal supports various applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disc burning application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an email application, an instant messaging application, an exercise support application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, a digital music player application, and/or a digital video player application.

Various applications that may be executed on the terminal may use at least one common physical user interface device, such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and corresponding information displayed on the terminal can be adjusted and/or changed between applications and/or within respective applications. In this way, a common physical architecture (e.g., touch-sensitive surface) of the terminal can support various applications with user interfaces that are intuitive and transparent to the user.

It should be understood that, the sequence numbers of the steps in this embodiment do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of this embodiment.

In order to explain the technical means described in the present application, the following description will be given by way of specific examples.

Referring to fig. 1, fig. 1 is a first flowchart of a data risk analysis method provided in an embodiment of the present application. As shown in fig. 1, a data risk analysis method includes the steps of:

step 101, performing data extraction on system loading data based on a plurality of set risk control dimensions to obtain attribute data and additional data except the attribute data of the system loading data in different risk control dimensions.

The data risk analysis method in the embodiment may be applied to risk management processes in various fields, specifically, for example, in the fields of tax, bank, insurance, and the like.

In different application fields, the risk management objects may correspond to different risk management objects, for example, taxpayers in the tax field, insurance applicants in the insurance field, loan objects or issued bonds of banks, downstream distributors or retail enterprises in the distribution field, or the like, or different financial products such as bonds, foreign exchanges, and the like issued in the bank field, or account types such as current accounts, deposit accounts, and the like in the bank field.

And acquiring data related to the risk management objects, forming system loading data, and performing risk analysis on the system loading data.

The risk management and control dimension and the system loading data can be set according to a specific application scenario and a risk management and control requirement, and are not specifically limited here.

The risk management and control dimensions are multiple, and the risk management and control dimensions are business data authenticity management and control dimensions and business risk management and control dimensions, for example.

Under different risk control dimensions, different attribute data and additional data can be extracted from the same system loading data. The attribute data is feature data corresponding to the risk control dimension in the system loading data, and the features can be classified and counted. The additional data is data except attribute data of system loading data in different risk management and control dimensions, and can assist the attribute data to realize risk analysis in corresponding risk management and control dimensions.

For example, the system loading data is massive financial transaction data, and each financial transaction record includes: financial account to complete transaction, transaction payment snapshot, transaction time, transaction amount, transaction financial product, transaction yield.

In the data authenticity control dimension, when data extraction is carried out on the financial transaction record, extracting attribute data corresponding to the data authenticity control dimension from the financial transaction record, wherein the financial account, the transaction payment snapshot and the transaction time for completing the transaction are taken as the attribute data corresponding to the data authenticity control dimension, and the transaction amount and the transaction yield are taken as additional data.

Differently, in the business risk control dimension, when data extraction is performed on the financial transaction record, the attribute data corresponding to the business risk control dimension is extracted from the financial transaction record, wherein the financial account, the transaction amount and the transaction yield rate for completing the transaction are used as the attribute data corresponding to the business risk control dimension, the investment risk control is specifically realized, and the transaction payment snapshot and the transaction time are used as additional data.

During specific implementation, corresponding relations between different risk control dimensions and various attribute data dimensions of system loading data can be established in advance, so that rapid data matching extraction is realized on the basis, and the attribute data and the additional data in the system loading data under different risk control dimensions are extracted.

Or, in specific implementation, semantic analysis may be performed based on the control features of different risk control dimensions and the data content of each data dimension in the system loading data, so as to implement data matching extraction on the basis, and extract the attribute data and the additional data in the system loading data under different risk control dimensions. Further, the system loading data is obtained by specifically extracting data from an upstream external system and then loading the data into the risk management and control system.

Correspondingly, before performing data extraction on the system loading data based on the multiple set risk control dimensions to obtain attribute data and additional data except the attribute data of the system loading data in different risk control dimensions, the method further includes:

acquiring original data from different sources from an external data system based on a data loading script; according to data commonality information among the original data, performing data integration on the original data to obtain target data; and loading the target data to the local system to obtain system loading data.

In the process, raw data from different sources can be acquired based on an external data system. The raw data from different sources are, for example, transaction record data from different subsidiaries, transaction record data disclosed by official websites and unofficial websites, and the like.

The data extraction method specifically comprises the steps of accessing an upstream external data system through an SQOOP tool to extract data, then loading the extracted content into an MRISK system to form original data, integrating the original data, and then loading the integrated original data into an HIVE data warehouse to obtain system loading data. And data reprocessing can be carried out based on each dimension of risk control in the subsequent processing process to form an atom granularity risk data pool, and a foundation is laid for final data aggregation analysis.

The process may be based on artificial intelligence techniques to obtain and process relevant data. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

And 102, for each risk management and control dimension, respectively arranging and combining the attribute data and different additional data to form risk evaluation data combination items under different analysis granularities.

When the risk management and control dimensions are multiple, the attribute data and the additional data are arranged and combined to form risk evaluation data combination items under different analysis granularities, specifically, the attribute data and the additional data are respectively arranged and combined aiming at the attribute data and the additional data corresponding to different risk management and control dimensions to form initial granularity risk evaluation data under each risk management and control dimension, so that risk evaluation is performed on the same system loading data from different risk management and control dimensions in the subsequent risk analysis process.

The risk assessment data combination is formed as a basic data unit for risk assessment at different analysis granularities. Based on the risk assessment data combination items, a risk assessment data cluster capable of meeting different risk assessment requirements is formed, and accuracy and comprehensiveness of data risk analysis results are improved.

The permutation and combination between the attribute data and the additional data may be specifically based on the combination of the same attribute data and different additional data, or based on the association relationship between the attribute data and the additional data, selecting partial data from the attribute data and the additional data having an association relationship therewith to perform permutation and combination.

Therefore, for each risk control dimension, when different attribute data and different additional data are combined in an arrangement mode, content differences exist between each formed risk assessment data combination item group, and therefore risk assessment data combination items with different analysis granularities are formed.

Here, the analysis granularity refers to the size of the data amount included in the analysis target. The size of the analysis granularity is positively correlated with how much of the data content is contained in the risk assessment data combination item.

For example, attribute data formed by information such as the name, issuer, amount, and issue time of a financial product may be combined with additional data such as the position, transaction amount, and transaction time to form a risk assessment data combination item including the name, issuer, amount, issue time, and position of a bond, a risk assessment data combination item including the name, issuer, amount, issue time, and transaction amount of a bond, a risk assessment data combination item including the name, issuer, amount, issue time, and transaction time of a bond, a risk assessment data combination item including the name, issuer, amount, issue time, position, and transaction amount of a bond, or other more permutation and combination, which are not listed here.

Alternatively, when the association between the attribute data and the additional data is constructed, for example, the association between the issue amount and the transaction amount and the association between the transaction time and the issue time are established.

The method comprises the steps of selecting a bond name, an issuer and an issuing amount from attribute data formed by information such as the bond name, the issuer, the issuing amount and the issuing time, selecting additional information of a position holder and a transaction amount from the additional information, combining the additional information to form a risk assessment data combination item, and realizing data correctness between issuing of the issued bond and transaction amount based on the risk assessment data combination item to judge whether business data are abnormal or not so as to realize risk analysis.

The method may further include selecting a bond name, an issuer, and an issue time from attribute data formed by information such as a bond name, an issuer, an issue amount, and an issue time, selecting additional information such as a position holder and a transaction time from the additional information, combining the additional information to form a risk assessment data combination item, and performing risk analysis on whether or not the bond transaction time is abnormal based on the risk assessment data combination item.

The process carries out re-carding and content division on risk management and control dimensions and system loading data, and risk analysis data are atomized.

And 103, acquiring a target risk analysis object and a target risk analysis granularity.

The target risk analysis object and the target risk analysis granularity are specifically determined based on a setting operation of a user. Or the system analyzes the content based on the risk analysis task and the corresponding risk management and control requirement document.

The target risk analysis object indicates an analysis object of the current risk analysis task, and the target risk analysis granularity indicates an analysis data amount required by the current risk analysis task.

And 104, screening the risk assessment data combination items to obtain target risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the target risk analysis granularity based on the attribute data and the additional data.

In this step, a target risk assessment data combination item needs to be obtained from the risk assessment data combination items through matching.

Specifically, after the attribute data and the additional data are arranged and combined to form initial granularity risk assessment data, data screening and extraction are required to be performed on the initial granularity risk assessment data to obtain a target risk assessment data combination item meeting the current risk analysis requirement, so that the original data are effectively utilized, and the processing efficiency of subsequent risk analysis processing is improved.

In the implementation process, the target risk analysis object needs to be subjected to data matching by combining attribute data and additional data in risk assessment data combination items under different analysis granularities, wherein keywords may be extracted from object description information corresponding to the risk analysis object, the attribute data and the additional data in the formed risk assessment data combination items are matched with the keywords, a data combination item matched with the target risk analysis object is found, and a data combination item with the analysis granularity consistent with the target risk analysis granularity is determined from the data combination items and serves as the target risk assessment data combination item.

According to the process, after the massive data are subjected to data combination atomization according to different risk management and control dimensions, through data matching and gathering, the risk assessment data sources can face users in a unified mode, and data sorting and screening are achieved.

Further, in one possible scenario, the items of the combination of the risk assessment data correspond to the permutation and combination between the various attribute data and the additional data. Therefore, the extracted target risk assessment data combination item may have a phenomenon of data content overlapping, and may be processed, specifically:

judging whether attribute data and additional data in the first data combination item are contained in the second data combination item in the extracted target risk assessment data combination item; when the judgment result is yes, the first data combination item is removed from the target risk assessment data combination item, only one group of data combination items is reserved, data redundancy is reduced, and the speed of subsequent data analysis and the operability of data analysis are improved.

And 105, performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result.

And when the risk analysis processing based on the target risk analysis object is carried out, specifically, the risk analysis processing is carried out based on the target risk evaluation data combination item corresponding to the target risk analysis object and the target risk analysis granularity.

The risk analysis processing includes, for example, graphical processing of data, analysis processing of data distribution information, analysis processing of information such as data trend, comparison analysis processing between data and the like based on the extracted target risk assessment data combination items, and finally, an analysis result can be output in an output form such as characters, graphical contents, reports and the like, and specific analysis means can be selected according to actual analysis requirements.

Specifically, as an optional implementation manner, performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result includes:

determining a target risk control dimension to which a target risk evaluation data combination item belongs;

and performing risk analysis processing on the target attribute data and the target additional data contained in the target risk assessment data combination item based on the target risk analysis model to obtain a target risk analysis result.

And corresponding risk analysis models correspond to different risk management and control dimensions. Different risk analysis models can implement data analysis functions in corresponding risk management and control dimensions.

When the target attribute data and the target additional data included in the target risk assessment data combination item are subjected to risk analysis processing based on the target risk analysis model, the target attribute data and the target additional data may be specifically input to the target risk analysis model as input data, and data risk analysis processing of a predetermined program may be implemented based on the target risk analysis model.

Optionally, when the target risk control dimension to which the target risk assessment data combination item belongs is a data authenticity control dimension, performing data authenticity risk analysis on the target attribute data and the target additional data contained in the target risk assessment data combination item based on the risk analysis model.

In this case, as an optional implementation manner, the target attribute data includes data content of a target data type, and the target additional data includes service scene data; the risk analysis processing is performed on the target attribute data and the target additional data contained in the target risk assessment data combination item based on the target risk analysis model to obtain the target risk analysis result, and the method comprises the following steps:

Wherein the data type is used for defining the data content. The data content is not the same for different data types.

Such as text data, numerical data, code number data, character data, and the like. The target data type may be one or more.

The business scenario data is, for example, a security release scenario, a security trading scenario, a security settlement scenario, and the like in financial security trading. The different service scenario data correspond to respective service scenarios.

When the authenticity analysis of the data is realized, in the process, the target data type of the data content and the service scene corresponding to the service scene data are introduced, the service scene to which the data content belongs is judged, the attribute data is subjected to auxiliary verification by the additional data, and the authenticity analysis result of the data content is obtained.

In the embodiment of the application, after attribute data and additional data are divided from a data source based on risk management and control dimensions, the data source is arranged and combined to form risk evaluation data combination items under different analysis granularities, a basic data unit for risk evaluation is formed, data to be analyzed is sorted and screened on the basis, risk analysis processing of a risk analysis object under the risk analysis granularity is implemented, the risk evaluation data sources can be uniformly oriented to users through matching and convergence of the data, effective utilization of system loading data is achieved, accuracy and comprehensiveness of data risk analysis results are improved, different risk evaluation requirements are met, and risk analysis efficiency is improved.

Different embodiments of the data risk analysis method are also provided in the embodiments of the present application.

Referring to fig. 2, fig. 2 is a second flowchart of a data risk analysis method provided in the embodiment of the present application. As shown in fig. 2, a data risk analysis method includes the steps of:

step 201, based on a plurality of set risk control dimensions, performing data extraction on the system loading data to obtain attribute data and additional data except the attribute data of the system loading data in different risk control dimensions.

The implementation process of this step is the same as that of step 101 in the foregoing embodiment, and is not described here again.

And 202, for each risk management and control dimension, respectively arranging and combining the attribute data and different additional data to form risk evaluation data combination items under different analysis granularities.

The implementation process of this step is the same as that of step 102 in the foregoing embodiment, and is not described here again. Step 203, receiving a first risk analysis instruction triggered by the user through the option selection operation of the user terminal.

The option selection operation comprises a data option selection operation and a data granularity option selection operation.

The target risk analysis object and the target risk analysis granularity are selected by a user.

Here, it is necessary to provide a user operation interface for the user to perform triggering of the risk analysis instruction. The user operation interface comprises a data option and a data granularity option.

The data option is used for selecting a target risk analysis object, and the data granularity option is used for selecting a target risk analysis granularity.

The data option is, for example, to pick a certain bond, the data granularity option is, for example, to pick a past quarter, the triggered risk analysis instruction is, for example, to perform risk analysis for analyzing issue data of a certain bond for a past quarter, and a specific risk analysis dimension may be determined in combination with the risk management dimension or may be specified when the user triggers the risk analysis instruction.

And 204, analyzing the first risk analysis instruction to obtain an option identifier carried in the first risk analysis instruction and corresponding to the option selection operation.

And analyzing the risk analysis instruction triggered by the user to acquire the current risk management and control requirement of the user.

The analysis of the risk analysis instruction specifically comprises the steps of reading the content in a set message field from an instruction message corresponding to the risk analysis instruction, and extracting an option identifier carried in the instruction message; the option designator includes a first designator corresponding to a data option select operation and a second designator corresponding to a data granularity option select operation.

Step 205, based on the option identifier, obtaining a target risk analysis object corresponding to the data option selecting operation and a target risk analysis granularity corresponding to the data granularity option selecting operation through matching.

Different option identifiers and the content selected by the user option selection operation have preset corresponding relations, and the content selected by the user is determined through the analyzed identifiers.

According to the operation steps, an operable user interface is provided for a user, so that the user can dynamically select the risk control data, an instruction meeting most of risk analysis requirements is generated, and follow-up data analysis can be performed closer to the actual risk requirements of the user.

And step 206, screening the risk assessment data combination items to obtain target risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the target risk analysis granularity based on the attribute data and the additional data.

The implementation process of this step is the same as that of step 104 in the foregoing embodiment, and is not described here again.

And step 207, performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result.

The implementation process of this step is the same as that of step 105 in the foregoing embodiment, and is not described here again.

And step 208, receiving a second risk analysis instruction triggered by the option adjustment operation of the user terminal.

Wherein the option adjustment operation comprises a data granularity option adjustment operation.

Such as selecting the past financial year. I.e., adjusting the risk analysis granularity from the last quarter to the last year.

After the adjustment of the user on the risk analysis granularity is obtained, the adjusted parameters can be obtained through analysis based on the corresponding risk analysis instructions, the risk analysis results generated according to the parameters before adjustment are updated based on the adjusted parameters, and the risk analysis processing is carried out based on the adjusted target data to obtain the adjusted risk analysis results.

The process provides a risk analysis granularity adjustment function for a user, and carries out risk analysis granularity adjustment through a risk analysis system, so that the same risk analysis object can realize mutual skip of risk analysis among different metering granularities.

Step 209, the second risk analysis instruction is analyzed to obtain an adjusted option identifier carried in the second risk analysis instruction and corresponding to the data granularity option adjustment operation.

Step 210, based on the adjusted option identifier, matching to obtain an adjusted risk analysis granularity corresponding to the option adjustment operation;

the process of parsing the risk analysis command in steps 209 to 210 and obtaining the risk analysis granularity based on the parsed identifier is the same as the process of steps 204 to 205, and is not described herein again.

In the process, the adjusted risk analysis granularity is obtained.

Step 211, based on the attribute data and the additional data, screening the risk assessment data combination items to obtain adjusted risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the adjusted risk analysis granularity;

and step 212, performing risk analysis processing based on the adjusted risk assessment data combination item to obtain an adjusted risk analysis result.

After the adjusted risk analysis granularity is obtained, the steps 211 to 212 are executed to implement a processing procedure of executing corresponding data risk analysis on the target risk analysis object based on the adjusted risk analysis granularity.

Further, in an optional embodiment, after the performing risk analysis processing based on the adjusted risk assessment data combination item and obtaining an adjusted risk analysis result, the method further includes:

The first display region and the second display region may be two display regions juxtaposed.

The process realizes the same-screen display of the data risk analysis results under different data analysis granularities so as to visually display the risk analysis results of the same data analysis object under different risk analysis granularities.

In the embodiment of the application, after attribute data and additional data are divided from a data source based on risk management and control dimensions, the data source is arranged and combined to form risk evaluation data combination items under different analysis granularities, a basic data unit for risk evaluation is formed, data to be analyzed is sorted and screened on the basis, risk analysis processing on a risk analysis object under the risk analysis granularity is implemented, the risk evaluation data source can uniformly face users through matching and gathering of the data, effective utilization of system loading data is realized, a risk analysis granularity adjusting function is further provided for the users, risk analysis granularity adjustment is implemented through a risk analysis system, the same risk analysis object realizes mutual skip of risk analysis among different metering granularities, accuracy and comprehensiveness of data risk analysis results are improved, and different risk evaluation requirements are met, and the risk analysis efficiency is improved.

Referring to fig. 3, fig. 3 is a structural diagram of a data risk analysis device according to an embodiment of the present application, and for convenience of description, only portions related to the embodiment of the present application are shown.

The data risk analysis device 300 includes:

the data extraction module 301 is configured to perform data extraction on system loading data based on a plurality of set risk management and control dimensions, so as to obtain attribute data of the system loading data in different risk management and control dimensions and additional data except the attribute data;

the data combination module 302 is configured to, for each risk management and control dimension, arrange and combine the attribute data and the different additional data, so as to form risk assessment data combination items at different analysis granularities;

an obtaining module 303, configured to obtain a target risk analysis object and a target risk analysis granularity;

a data screening module 304, configured to screen, based on the attribute data and the additional data, a target risk assessment data combination item that matches the target risk analysis object and has an analysis granularity that is consistent with the target risk analysis granularity from the risk assessment data combination item;

and the data analysis module 305 is configured to perform risk analysis and processing based on the target risk assessment data combination item to obtain a target risk analysis result.

The data analysis module 305 is specifically configured to:

The target attribute data comprise data content of a target data type, and the target additional data comprise service scene data; the data analysis module 305 is more specifically configured to:

based on the target risk analysis model, performing service scene classification prediction on the data content of the target data type to obtain a probability value of the data content belonging to a service scene corresponding to the service scene data;

The obtaining module 303 is specifically configured to:

receiving a first risk analysis instruction triggered by a user through option selection operation of a user terminal, wherein the option selection operation comprises data option selection operation and data granularity option selection operation; analyzing the first risk analysis instruction to obtain an option identifier which is carried in the first risk analysis instruction and corresponds to the option selection operation;

Wherein, the device still includes:

an adjustment module to:

screening out adjusted risk assessment data combination items which are matched with the target risk analysis object and have the analysis granularity consistent with the adjusted risk analysis granularity from the risk assessment data combination items based on the attribute data and the additional data;

and carrying out risk analysis and processing based on the adjusted risk assessment data combination item to obtain an adjusted risk analysis result.

Wherein, the device still includes:

the display module is used for outputting the adjusted risk analysis result to a first display area of a display interface in the user terminal; the display interface further comprises a second display area adjacent to the first display area, and the target risk analysis result is displayed in the second display area. The device also includes:

a data loading module to:

and loading the target data to the local system to obtain the system loading data.

The data risk analysis device provided by the embodiment of the application can realize each process of the embodiment of the data risk analysis method, can achieve the same technical effect, and is not repeated here to avoid repetition.

Fig. 4 is a block diagram of a computer device according to an embodiment of the present disclosure. As shown in the figure, the computer device 4 of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the steps of any of the various method embodiments described above being implemented when the computer program 42 is executed by the processor 40.

The computer device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The computer device 4 may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a computer device 4 and is not intended to limit computer device 4 and may include more or fewer components than shown, or some of the components may be combined, or different components, e.g., the computer device may also include input output devices, network access devices, buses, etc.

The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. The memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the computer device 4. The memory 41 is used for storing the computer program and other programs and data required by the computer device. The memory 41 may also be used to temporarily store data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one type of logical function division, and other division manners may exist in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.

When the computer program product runs on a terminal, the steps in the method embodiments can be realized when the terminal executes.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.

Claims

1. A method of data risk analysis, comprising:

for each risk control dimension, the attribute data and different additional data are arranged and combined to form risk assessment data combination items under different analysis granularities;

acquiring a target risk analysis object and target risk analysis granularity;

and performing risk analysis and processing based on the target risk assessment data combination item to obtain a target risk analysis result.

2. The method of claim 1, wherein performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result comprises:

3. The method of claim 2, wherein the target attribute data comprises data content of a target data type, and the target additional data comprises traffic scene data; the risk analysis processing is performed on the target attribute data and the target additional data contained in the target risk assessment data combination item based on the target risk analysis model to obtain the target risk analysis result, and the method comprises the following steps:

4. The method of claim 1, wherein obtaining the target risk analysis object and the target risk analysis granularity comprises:

receiving a first risk analysis instruction triggered by an option selection operation of a user terminal by a user, wherein the option selection operation comprises a data option selection operation and a data granularity option selection operation; analyzing the first risk analysis instruction to obtain an option identifier carried in the first risk analysis instruction and corresponding to the option selection operation;

5. The method of claim 4, wherein after performing risk analysis processing based on the target risk assessment data combination item to obtain a target risk analysis result, further comprising:

6. The method of claim 5, wherein after performing risk analysis processing based on the adjusted risk assessment data combination item to obtain an adjusted risk analysis result, further comprising:

7. The method according to claim 1, wherein before performing data extraction on system loading data based on a plurality of set risk control dimensions to obtain attribute data and additional data other than the attribute data of the system loading data in different risk control dimensions, the method further includes:

8. A data risk analysis device, comprising:

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.