CN115409290A - Business data risk model verification method and device, electronic equipment and medium - Google Patents
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Abstract
The disclosure relates to the technical field of data security, and provides a business data risk model verification method, a business data risk model verification device, electronic equipment and a business data risk model verification medium. The method comprises the following steps: acquiring basic service data corresponding to a target service; constructing service data to be tested of a target service based on the basic service data; performing risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result; performing risk prediction on the business data to be tested based on a risk prediction model to obtain a test risk prediction result; and determining a target risk prediction result of the basic service data of the target service based on the initial risk prediction result and the test risk prediction result. According to the method and the system, the service data to be tested in the testing environment is constructed according to the basic service data of the target service, the two types of service data are subjected to prediction processing respectively, a data risk prediction result is obtained, the risk prediction of the basic service data is verified in real time, and the accuracy of the risk prediction of the service data is improved.
Description
Technical Field
The present disclosure relates to the field of data security technologies, and in particular, to a method, an apparatus, an electronic device, and a medium for verifying a business data risk model.
Background
Currently, the demand for predicting the risk of financial transaction data is gradually increasing. The currently widely adopted methods for preventing risks are to utilize risk modeling to realize warning and handling of risks. The risk modeling process specifically includes: extracting model feature points through case feature analysis; quantifying case characteristics to form a risk model; performing correctness verification on the risk model; executing a risk model auditing management process; and (4) issuing and commissioning the risk model. The quality of risk modeling and a verification mechanism determine the quality of the wind control effect.
At present, the common risk model of financial institution verifies the mode mainly through development, test environment goes on, utilize a small amount of sample data to simulate and verify, after test environment verifies, send the model to the production environment again, because test environment data's limitation, in addition in recent years information security all sides strict requirement, key data in the test environment all need do desensitization, avoid information leakage to cause information security to realize, under such condition, can't carry out accurate risk prediction to business data in essence, cause the model to verify inaccurate, produce a large amount of rubbish data after the input, the condition such as risk lou reports appears easily, risk wrong reports, bring very big degree of difficulty to subsequent business data analysis's applicability simultaneously.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a business data risk model verification method, device, electronic device, and medium, so as to solve the problem in the prior art that prediction of business data risk is inaccurate.
In a first aspect of the embodiments of the present disclosure, a method for verifying a business data risk model is provided, including: acquiring basic service data corresponding to a target service; constructing service data to be tested of the target service based on the basic service data; performing risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result; performing risk prediction on the to-be-tested business data based on the risk prediction model to obtain a test risk prediction result; and determining a target risk prediction result of the basic service data of the target service based on the initial risk prediction result and the test risk prediction result.
In a second aspect of the embodiments of the present disclosure, a business data risk model verification apparatus is provided, including: the service data acquisition unit is configured to acquire basic service data corresponding to a target service; a test service data construction unit configured to construct service data to be tested of the target service based on the basic service data; the initial risk prediction unit is configured to carry out risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result; the test risk prediction unit is configured to carry out risk prediction on the to-be-tested business data based on the risk prediction model to obtain a test risk prediction result; and the target risk prediction unit is configured to determine a target risk prediction result of basic service data of the target service based on the initial risk prediction result and the test risk prediction result.
In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the computer program.
In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which a computer program is stored, which when executed by a processor implements the steps of the above-mentioned method.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: firstly, acquiring basic service data of a target service; then, constructing service data to be tested of the target service according to the basic service data, and performing risk prediction processing on the two types of service data under different environments respectively based on a risk prediction model; and finally, adjusting the initial risk prediction result according to the risk prediction result in the test environment, and comprehensively evaluating the target risk prediction result of the basic service data of the target service. According to the method, risk prediction is carried out according to basic service data of a target service in a production environment to obtain an initial risk prediction result, meanwhile, testing risk prediction is carried out in a testing environment by utilizing constructed service data to be tested, and the initial risk prediction result is verified in real time according to the testing risk prediction result to obtain a relatively accurate risk prediction result; the invention realizes that the model laboratory data environment (namely the test data environment) of the risk model is simultaneously established in the production environment, the business data sharing is realized between the model laboratory data environment and the test data environment, and the definition data and the test result data of the risk model are isolated from the business data and are independently stored, thereby not influencing the normal processing of the business data.
Drawings
To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed 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 disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a schematic diagram of one application scenario of a business data risk model validation method, according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of a business data risk model validation method according to the present disclosure;
FIG. 3 is a process flow diagram of some embodiments of a business data risk model validation method according to the present disclosure;
FIG. 4 is a block diagram of some embodiments of a business data risk model validation mechanism according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 is a schematic diagram of one application scenario of a business data risk model validation method, in accordance with some embodiments of the present disclosure.
In the application scenario of fig. 1, a computing device 101 may obtain underlying business data 102 for a target business. Then, the computing device 101 may construct the service data to be tested 103 of the target service according to the basic service data 102, and perform risk prediction on the two types of service data based on a risk prediction model, respectively, to obtain an initial risk prediction result 104 and a test risk prediction result 105. Finally, computing device 101 may perform risk prediction on the underlying business data of the target business based on initial risk prediction result 104 and test risk prediction result 105, as indicated by reference numeral 106. It should be noted that the initial risk prediction result and the test risk prediction result 105 are generated separately and stored separately; the initial risk prediction result 104 is mainly used for processing the early warning result of the business data, and the test risk prediction result 105 is mainly used for testing the business data according to the verification scheme to determine whether the prediction result is accurate.
The risk of the financial industry is everywhere, the requirement on data safety is higher and higher, the risk prevention technology generally adopted at present mainly utilizes risk modeling to realize warning and processing of the risk, but the quality of the risk modeling and a verification mechanism determine the quality of the wind control effect. The current main risk model that adopts verifies the mode mainly through development, test environment goes on, utilize a small amount of sample data to simulate and verify, after test environment verifies, send the model to the production environment again, because test environment data's limitation, in addition in recent years information security all aspects strict requirement, the desensitization processing all need be done to key data in the test environment, avoid information leakage to cause information security to realize, under such condition, cause the accuracy that can't complete verification model in essence, cause the model to verify inaccurate, produce a large amount of rubbish data after putting into production, the condition such as risk is leaked and reported easily appears, risk wrong report, bring very big degree of difficulty to the validity that business personnel confirm the model.
Based on this, in the business data risk model verification method provided by the embodiment of the present disclosure, a model laboratory data environment (test data environment) is established on a production environment, and a risk model is verified in real time according to real data in the production environment, so as to verify a prediction result of the risk model; meanwhile, the sharing of basic service data of the production environment is realized, the prediction result in the test environment is isolated, real data can be shared and used in the production environment, the service data result is not influenced, and the verification and management work of the risk model is achieved.
The computing device 101 may be hardware or software. When the computing device 101 is hardware, it may be implemented as a distributed cluster composed of a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device 101 is embodied as software, it may be installed in the hardware devices listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
FIG. 2 is a flow diagram of some embodiments of a business data risk model validation method according to the present disclosure. The business data risk model validation method of FIG. 2 can be performed by the computing device 101 of FIG. 1. As shown in fig. 2, the business data risk model verification method includes:
step S201, obtaining basic service data corresponding to the target service.
In some embodiments, an executing body (e.g., the computing device 101 shown in fig. 1) of the business data risk model verification method may obtain basic business data corresponding to a target business, where the target business may be understood as an alert business requiring risk prediction on the target business, such as a financial investment risk prediction business, and the basic business data may be understood as business data requiring risk prediction, including but not limited to a business order number, a business type, a business data type, a transaction account, a transaction time, a transaction flow, and the like. As an example, when receiving an instruction for performing risk prediction on business data of a target business, an execution subject responds to the risk prediction instruction to obtain basic business data corresponding to the target business, so as to facilitate subsequent risk prediction on the basic business data.
In some optional implementation manners of some embodiments, the obtaining basic service data corresponding to the target service includes: determining a target database of a target service in a production environment; and acquiring basic service data corresponding to the target service from the target database. Optionally, the execution main body sets up a target database in a production environment, and all types of service data are stored in the target database, so that the basic service data corresponding to the target service is obtained from the target database in order to realize risk prediction on the target service data in the following step.
It should be noted that, acquiring the service data corresponding to the target service in the production environment is a process for predicting the risk of the basic service data, and the process belongs to a normal wind control process of the service data.
Step S202, constructing the service data to be tested of the target service based on the basic service data.
In some embodiments, the executing entity may construct the business data to be tested of the target business according to the basic business data, where the business data to be tested may be understood as the business data that needs risk verification in a laboratory environment, such as business data customers, accounts, transaction flow and the like which are important in a financial institution. It should be noted that, a service data processing process in a laboratory environment may be understood as a data processing process in a test environment, in order to perform real-time prediction and verification on basic service data in the embodiment of the present disclosure, an execution main body may also perform real-time verification on a basic service data prediction result in a normal service data prediction process; reference may be made in particular to the description of the examples which follows.
In some optional implementation manners of some embodiments, constructing service data to be tested of the target service based on the basic service data includes: determining a laboratory database of the target service in the test environment; and constructing the service data to be tested of the target service in the laboratory database based on the basic service data. The execution main body can establish a laboratory database in a test environment and is used for storing a basic service data table to be verified, so that subsequent risk prediction verification is facilitated. Optionally, mapping a basic service data table required by the verification environment, that is, a service data table to be tested, in a laboratory database by establishing a view mode.
And step S203, performing risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result.
In practical applications, the executing entity may perform risk prediction on the acquired basic business data of the target business based on the risk prediction model, and obtain an initial risk prediction result, where the initial risk prediction result may be understood as a result of predicting a risk event occurring in the basic business data, and as an example, a result of predicting a risk of a bond default event.
In some optional implementation manners of some embodiments, performing risk prediction on the basic service data to obtain an initial risk prediction result includes: inputting the basic service data into an initial risk prediction model to obtain an initial risk prediction result; the execution main body inputs basic business data into an initial risk prediction model, wherein the initial risk prediction model is obtained by pre-training, the model is mainly used for predicting the occurrence risk of the event corresponding to the business data, different data types and different parameter thresholds can be obtained in the specific model training process by referring to different business scenes to judge whether the event pointed by the business data belongs to a risk event in the field, and the process is not limited in the embodiment. In addition, the initial risk prediction model and the corresponding risk model definition entity table data can only be stored in the target database, so that the corresponding model parameter definition can be acquired from the database in the following process.
And step S204, performing risk prediction on the to-be-tested business data based on the risk prediction model to obtain a test risk prediction result.
In some optional implementation manners of some embodiments, in order to verify the prediction accuracy of the initial risk prediction model in real time, after the execution main body obtains the service data to be tested, the execution main body may further perform risk prediction on the service data to be tested based on the risk prediction model to obtain a test risk prediction result, including: and inputting the to-be-tested business data into a test risk prediction model to obtain a test risk prediction result. Specifically, the execution main body also performs risk prediction on the business data to be tested by using the test risk prediction model in a laboratory user environment to obtain a test risk prediction result.
In some optional implementation manners of some embodiments, before inputting the service data to be tested into the test risk prediction model and obtaining the test risk prediction result, the method further includes: obtaining model definition parameters in the initial risk prediction model; and constructing a test risk prediction model of the target service based on the model definition parameters. In practical application, an executive body can acquire model definition parameters in an initial risk prediction model from a production environment, and initialize needed risk model definition parameters into a laboratory database, but the model definition parameters are not permanently stored in the database, but are cleaned according to different verification schemes; after obtaining the model definition parameters of the initial risk prediction model, the execution subject can construct a test risk prediction model of the target service according to the required model definition parameters.
In the business data risk model verification method disclosed in this embodiment, the accuracy of the initial risk prediction model is verified by constructing the risk prediction model in a laboratory environment and predicting business data, so that the initial risk prediction model can be adjusted at any time in the follow-up process, the effectiveness evaluation of the risk model before the model is put into production is realized, a large amount of garbage data caused after the model is put into production is reduced, and the timely processing and statistical analysis of the business data are affected.
Step S205, determining a target risk prediction result of the basic service data of the target service based on the initial risk prediction result and the test risk prediction result.
In practical application, after obtaining an initial risk prediction result of a target service and a test risk prediction result, an execution main body can verify and evaluate an initial risk prediction model in the initial risk prediction result in real time according to the test risk prediction result to determine the effectiveness and the availability of the initial risk prediction model; and subsequently, comprehensively evaluating the basic service data of the target service according to the test risk prediction result to determine the target risk prediction result.
In some optional implementation manners of some embodiments, determining a target risk prediction result of basic service data of the target service based on the initial risk prediction result and the test risk prediction result includes: determining a policy to be verified of the target service; verifying the initial risk prediction result according to the strategy to be verified based on the test risk prediction result to obtain a verification result of the initial risk prediction model; and determining a target risk prediction result of the basic service data of the target service according to the verification result. In practical applications, different target services may relate to different policies to be verified, such as different basic service data corresponding to different parameter reference standards, or reference conditions, etc.; furthermore, the execution main body can verify the initial risk prediction result according to the determined strategy to be verified based on the test risk prediction result so as to determine whether the initial risk prediction result can reach the preset standard or not; and finally, the execution subject can obtain the initial risk prediction model with higher prediction accuracy and put the initial risk prediction model into production for use.
In some optional implementation manners of some embodiments, a target database in a production environment establishes a risk model early warning result layer data table for business personnel to process a risk model early warning result; and establishing an entity risk model early warning result layer data sheet according to each verification scheme in a laboratory database, so that business personnel can conveniently perform analysis ratio peer-to-peer work.
In some optional implementation manners of some embodiments, after determining the target risk prediction result of the basic business data of the target business based on the initial risk prediction result and the test risk prediction result, the method further includes: and performing data analysis on the basic service data based on the target risk prediction result. After the execution main body obtains the target risk prediction result of the target service, data analysis can be performed on the basic service data, the data analysis has more analysis types, and analysis of risk event factors of the basic service data, analysis of other risk prediction model reference values established subsequently by the basic service data and the like can be analyzed. This embodiment does not specifically limit this process.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: firstly, acquiring basic service data of a target service; then, constructing service data to be tested of the target service according to the basic service data, and respectively carrying out risk prediction processing on the two types of service data under different environments based on a risk prediction model; and finally, adjusting the initial risk prediction result according to the risk prediction result in the test environment, and comprehensively evaluating the target risk prediction result of the basic service data of the target service. According to the method, risk prediction is carried out according to basic service data of a target service in a production environment to obtain an initial risk prediction result, meanwhile, test risk prediction is carried out in a test environment by using constructed service data to be tested, and the initial risk prediction result is verified in real time according to the test risk prediction result to obtain a relatively accurate risk prediction result.
FIG. 3 is a process flow diagram of some embodiments of a business data risk model validation method according to the present disclosure.
An example of the target service data is illustrated in fig. 3. It should be noted that, in the embodiment of the present application, a business risk model calculation environment and a risk model laboratory data verification environment are simultaneously established in a production environment, laboratory data environment business risk model basic business data are shared, risk model definition data and risk model calculation result data are independently stored, and are isolated from business data, so that normal processing of the business data is not affected.
The execution main body establishes two database users in the target business data instance, a business database user and a laboratory database user, and basic business data sharing can be realized in the same database instance; mapping a basic service data table required by a risk model verification environment in a laboratory database user by establishing a view mode; business data customers, accounts, transaction pipelines and the like which are important in financial institutions; establishing a corresponding risk model definition entity table in a business database user, wherein the data of the risk model definition table is only stored in the business database, and the laboratory user environment initiates the needed risk model definition data to the laboratory database user in the process of risk model verification without permanently storing the model definition data and cleaning the model definition data according to a verification scheme; and the business database users and the laboratory database users are completely isolated in a risk model early warning result data layer. And establishing a risk model early warning result layer data sheet by the user of the service database, and processing the risk model early warning result by service personnel. And establishing an entity risk model early warning result layer data sheet according to each verification scheme in an experiment database user, so that business personnel can conveniently perform work such as analysis and comparison.
In conclusion, the business data risk model verification method can realize the risk model verification work based on the real data of the production environment, can accurately verify the correctness of the risk model, provides a data environment for risk model verification, and improves the accuracy of the risk model verification work. Meanwhile, the method and the device are mainly applied to verification links in the management process of risk models related to the financial industry, validity and usability verification of the risk models based on real business data can be achieved through the method and the device, effective assessment is conducted on the risk models before the risk models are put into production, and the problems that a large amount of garbage data are caused after the risk models are put into production, timely processing and statistical analysis of the business data are affected are reduced.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described in detail herein.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 4 is a schematic structural diagram of some embodiments of a business data risk prediction apparatus according to the present disclosure. As shown in fig. 4, the business data risk prediction apparatus includes: a business data obtaining unit 401, a test business data constructing unit 402, an initial risk predicting unit 403, a test risk predicting unit 404, and a target risk predicting unit 405. The service data acquiring unit 401 is configured to acquire basic service data corresponding to a target service; a test service data construction unit 402 configured to construct service data to be tested of the target service based on the basic service data; an initial risk prediction unit 403, configured to perform risk prediction on the basic service data based on a risk prediction model, and obtain an initial risk prediction result; a test risk prediction unit 404 configured to perform risk prediction on the service data to be tested based on the risk prediction model to obtain a test risk prediction result; and an objective risk prediction unit 405 configured to determine an objective risk prediction result of the basic business data of the objective business based on the initial risk prediction result and the test risk prediction result.
In some optional implementations of some embodiments, the initial risk prediction unit 403 of the business data risk prediction apparatus is further configured to: and inputting the basic service data into an initial risk prediction model to obtain an initial risk prediction result.
In some optional implementations of some embodiments, the test risk prediction unit 404 of the business data risk prediction apparatus is further configured to: and inputting the to-be-tested business data into a test risk prediction model to obtain a test risk prediction result.
In some optional implementations of some embodiments, the business data risk predicting device is further configured to: obtaining model definition parameters in the initial risk prediction model; and constructing a test risk prediction model of the target service based on the model definition parameters.
In some optional implementations of some embodiments, the target risk prediction unit 405 of the business data risk prediction apparatus is further configured to: determining a strategy to be verified of the target service; verifying the initial risk prediction result according to the strategy to be verified based on the test risk prediction result to obtain a verification result of the initial risk prediction model; and determining a target risk prediction result of the basic service data of the target service according to the verification result.
In some optional implementations of some embodiments, the business data obtaining unit 401 of the business data risk predicting apparatus is further configured to: determining a target database of a target service in a production environment; and acquiring basic service data corresponding to the target service from the target database.
In some optional implementations of some embodiments, the test business data constructing unit 402 of the business data risk predicting apparatus is further configured to: determining a laboratory database of the target service in the test environment; and constructing the business data to be tested of the target business in the laboratory database based on the basic business data.
In some optional implementations of some embodiments, the business data risk predicting device is further configured to: and performing data analysis on the basic service data based on the target risk prediction result.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
Fig. 5 is a schematic diagram of a computer device 5 provided by an embodiment of the present disclosure. As shown in fig. 5, the computer device 5 of this embodiment includes: a processor 501, a memory 502 and a computer program 503 stored in the memory 502 and executable on the processor 501. The steps in the various method embodiments described above are implemented when the processor 501 executes the computer program 503. Alternatively, the processor 501 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 503.
Illustratively, the computer program 503 may be partitioned into one or more modules/units, which are stored in the memory 502 and executed by the processor 501 to complete the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 503 in the computer device 5.
The computer device 5 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computer devices. Computer device 5 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that fig. 5 is merely an example of a computer device 5 and is not intended to limit the computer device 5 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 501 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, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 502 may be an internal storage unit of the computer device 5, for example, a hard disk or a memory of the computer device 5. The memory 502 may also be an external storage device of the computer device 5, such as a plug-in hard disk provided on the computer device 5, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 502 may also include both internal storage units of the computer device 5 and external storage devices. The memory 502 is used for storing computer programs and other programs and data required by the computer device. The memory 502 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, so as to perform all or part of the functions described above. 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 for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any 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 technical solution. 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 disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the above-described apparatus/computer device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, another division may be made in actual implementation, multiple 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 a plurality of 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 disclosure 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 may be implemented in the form of hardware, or may also be implemented in the 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 above embodiments may be realized by the present disclosure, and the computer program may be stored in a computer readable storage medium to instruct related hardware, and when the computer program is executed by a processor, the steps of the above method embodiments may be realized. The computer program may comprise 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 computer program code, recording medium, U.S. 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 media, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure 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 disclosure, and they should be construed as being included in the scope of the present disclosure.
Claims (10)
1. A business data risk model verification method is characterized by comprising the following steps:
acquiring basic service data corresponding to a target service;
constructing service data to be tested of the target service based on the basic service data;
performing risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result;
performing risk prediction on the to-be-tested business data based on the risk prediction model to obtain a test risk prediction result;
and determining a target risk prediction result of the basic service data of the target service based on the initial risk prediction result and the test risk prediction result.
2. The business data risk model verification method according to claim 1, wherein the risk prediction is performed on the basic business data based on a risk prediction model to obtain an initial risk prediction result, comprising:
inputting the basic service data into an initial risk prediction model to obtain an initial risk prediction result;
and, the risk prediction of the business data to be tested based on the risk prediction model to obtain a test risk prediction result includes:
and inputting the service data to be tested into a test risk prediction model to obtain a test risk prediction result.
3. The business data risk model verification method according to claim 2, wherein before inputting the business data to be tested into the test risk prediction model and obtaining the test risk prediction result, the method further comprises:
obtaining model definition parameters in the initial risk prediction model;
and constructing a test risk prediction model of the target business based on the model definition parameters.
4. The business data risk model validation method of claim 3, wherein the determining a target risk prediction result of the basic business data of the target business based on the initial risk prediction result and the test risk prediction result comprises:
determining a policy to be verified of the target service;
verifying the initial risk prediction result according to the strategy to be verified based on the test risk prediction result to obtain a verification result of the initial risk prediction model;
and determining a target risk prediction result of the basic service data of the target service according to the verification result.
5. The business data risk model verification method according to any one of claims 1 to 4, wherein the obtaining of the basic business data corresponding to the target business comprises:
determining a target database of a target service in a production environment;
and acquiring basic service data corresponding to the target service from the target database.
6. The business data risk model verification method of claim 5, wherein the constructing business data to be tested of the target business based on the basic business data comprises:
determining a laboratory database of the target service in the test environment;
and constructing the service data to be tested of the target service in the laboratory database based on the basic service data.
7. The business data risk model verification method according to claim 1, wherein after determining the target risk prediction result of the basic business data of the target business based on the initial risk prediction result and the test risk prediction result, the method further comprises:
and performing data analysis on the basic service data based on the target risk prediction result.
8. A business data risk model verification apparatus, comprising:
the service data acquisition unit is configured to acquire basic service data corresponding to a target service;
a test service data construction unit configured to construct service data to be tested of the target service based on the basic service data;
the initial risk prediction unit is configured to carry out risk prediction on the basic service data based on a risk prediction model to obtain an initial risk prediction result;
the test risk prediction unit is configured to carry out risk prediction on the to-be-tested business data based on a risk prediction model to obtain a test risk prediction result;
and the target risk prediction unit is configured to determine a target risk prediction result of basic service data of the target service based on the initial risk prediction result and the test risk prediction result.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the method according to any one 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 a method according to any one of claims 1 to 7.
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