CN115760368A - Credit business approval method and device and electronic equipment - Google Patents
Credit business approval method and device and electronic equipment Download PDFInfo
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- CN115760368A CN115760368A CN202211486425.9A CN202211486425A CN115760368A CN 115760368 A CN115760368 A CN 115760368A CN 202211486425 A CN202211486425 A CN 202211486425A CN 115760368 A CN115760368 A CN 115760368A
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Abstract
The application provides a credit business approval method, a credit business approval device and electronic equipment, wherein the credit business approval method comprises the following steps: extracting target historical business data corresponding to the identity information from the risk data mart; calling a preset third-party interface to acquire external risk data; determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data; for each preset risk model, extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed, inputting the preset risk model, and obtaining a risk prediction result corresponding to the preset risk model; and determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model. According to the method and the device, the automation circulation of the approval process is realized through the preset risk model, the manual intervention process is reduced, and the approval efficiency is improved.
Description
Technical Field
The application relates to the technical field of finance, in particular to a credit business approval method and device and electronic equipment.
Background
The credit is a credit act between different owners reflecting a certain economic relationship, is a special form of value movement with repayment as a condition, and is a credit activity that a debtor loans money, and debtors repay and pay a certain interest according to time. The bank needs to pre-determine the repayment capabilities of the customer before approving the credit, thereby avoiding that the loan cannot be reclaimed.
In the existing credit approval process, a client is required to submit application data, an approver is required to examine the application data, the integrity of the application data is checked, an applicant is required to be informed to complete the application data if the application data is not complete, and complex approval processes such as admission screening, pre-approval, credit investigation, anti-fraud investigation, risk report generation and the like are required to be performed successively after the application data is complete, that is, the existing serial approval process results in overlong approval time, low approval efficiency caused by manual examination and verification, and increased risk.
Disclosure of Invention
In view of this, an object of the present application is to provide at least a credit business approval method, a credit business approval apparatus, and an electronic device, which implement automatic circulation of an approval process through a preset risk model, reduce manual intervention processes, and improve approval efficiency.
The application mainly comprises the following aspects:
in a first aspect, an embodiment of the present application provides a credit service approval method, where the method includes:
extracting identity information corresponding to the applicant from the received credit service application data; extracting target historical business data corresponding to the identity information from a risk data mart, wherein the risk data mart comprises historical business data used for risk calculation; calling a plurality of preset third-party interfaces to acquire external risk data corresponding to the identity information; determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data; for each preset risk model, extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed, inputting the preset risk model, and obtaining a risk prediction result corresponding to the preset risk model; and determining a service auditing result corresponding to the credit service application data according to the risk prediction result corresponding to each preset risk model.
In a possible implementation, the step of determining a plurality of risk indicators to be analyzed according to the target historical business data and the external risk data comprises: determining a plurality of first risk indicators according to the target historical service data; determining a plurality of second risk indicators according to the external risk data; and processing and cleaning the plurality of first risk indicators and the plurality of second risk indicators to obtain a plurality of risk indicators to be analyzed.
In one possible embodiment, the plurality of first risk indicators is determined by: acquiring a plurality of statistical formulas associated with target historical service data; carrying out statistics and summarization on the target historical service data by using each statistical formula; and determining the statistical result corresponding to each statistical formula as the first risk index corresponding to the statistical formula.
In a possible embodiment, the step of processing and cleaning the plurality of first risk indicators and the plurality of second risk indicators to obtain the plurality of risk indicators to be analyzed includes: acquiring a plurality of preset derivative rules, wherein each preset derivative rule indicates a risk index and a calculation mode for processing calculation; performing derivative index processing calculation between the plurality of first risk indexes and the plurality of second risk indexes by using each preset derivative rule, and determining a derivative index corresponding to each preset derivative rule; and a plurality of risk indexes to be analyzed are formed by the plurality of first risk indexes, the plurality of second risk indexes and the plurality of derivative indexes.
In one possible implementation, a plurality of target risk indicators to be analyzed corresponding to each preset risk model are determined in the following manner: acquiring a preset input object set corresponding to the preset risk model, wherein the preset input object set comprises a plurality of preset input indexes; and aiming at each preset input index, extracting a risk index to be analyzed matched with the preset input index from the plurality of risk indexes to be analyzed, and determining the risk index to be analyzed as a target risk index to be analyzed corresponding to the preset risk model.
In a possible implementation manner, the step of determining a service auditing result corresponding to the credit service application data according to the risk prediction result corresponding to each preset risk model comprises the following steps: inputting the risk prediction result corresponding to each preset risk model into a preset disposal strategy model; and outputting a risk report and a service auditing result corresponding to the credit service application data according to the input multiple risk prediction results by using a preset disposal strategy model.
In one possible implementation, the preset disposal policy model determines the service auditing result corresponding to the credit service application data by: determining a target risk result set formed by the risk prediction results corresponding to the preset risk models; and determining a target business auditing result corresponding to the target risk result set according to the preset corresponding relation between the plurality of risk result sets and the plurality of business auditing results.
In a possible implementation manner, the service audit result includes audit pass, audit fail and no judgment, wherein the method further includes: if the service auditing result is that the auditing is passed, lending according to the loan amount corresponding to the credit service application data, and if the service auditing result is that the auditing is not passed, prompting that the data is abnormal and not lending; and if the service audit result is that the service audit cannot be judged, sending the credit service application data and the risk report to a manual processing node for manual audit, if the manual audit result is that the audit is passed, lending according to the loan amount corresponding to the credit service application data, and if the manual audit result is that the audit is not passed, prompting that the data is abnormal and not lending.
In a second aspect, the present application further provides a credit service approval apparatus, comprising: the first extraction module is used for extracting the identity information from the received credit business application data; the second extraction module is used for extracting target historical business data corresponding to the identity information from the risk data mart, and the risk data mart comprises historical business data used for risk calculation; the calling module is used for calling a plurality of preset third-party interfaces so as to acquire external risk data corresponding to the identity information; the determining module is used for determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data; the prediction module is used for extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed according to each preset risk model, inputting the preset risk model and obtaining a risk prediction result corresponding to the preset risk model; and the auditing module is used for determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the memory communicate with each other through the bus, and the machine-readable instructions are executed by the processor to perform the steps of the credit business approval method according to the first aspect or any one of the possible implementation manners of the first aspect.
The credit business approval method, the credit business approval device and the electronic equipment provided by the embodiment of the application comprise the following steps: extracting target historical business data corresponding to the identity information from the risk data mart; calling a preset third-party interface to acquire external risk data; determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data; for each preset risk model, extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed, inputting the preset risk model, and obtaining a risk prediction result corresponding to the preset risk model; and determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model.
Compared with the prior art, the application has the advantages that:
1. according to the method and the system, risk indexes to be analyzed are obtained according to data which are obtained in different modes and are related to the risk, the risk indexes to be analyzed are combined with a preset risk model, a credit approval process is automatically completed, a manual approval process is replaced, manual intervention is reduced, and operation risks are reduced;
2. in the prior art, data called by a plurality of third-party interfaces are required in the auditing process, and corresponding approval is required every time an interface is called, namely the auditing process and the data calling are required to be carried out in series, but the steps for obtaining the third-party data are complex, and the problems of difficult aggregation and use after the data is retrieved and overlong approval time of a serial processing flow are solved, the approval efficiency is reduced, a plurality of preset risk models are adopted in the method, the real-time data processing is realized by replacing a serial data obtaining and approval mode, and the auditing efficiency is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a first flowchart illustrating steps of a credit business approval method according to an embodiment of the present application;
FIG. 2 is a flow chart II illustrating the steps of a credit business approval method provided by an embodiment of the application;
FIG. 3 is a functional block diagram of a credit business approval apparatus provided by an embodiment of the application;
fig. 4 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not intended to limit the scope of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be performed in reverse order or concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
In the current credit approval process, firstly, a client needs to submit loan application data, after a bank or a related loan unit receives and sees the loan application data submitted by an applicant, an approver needs to examine whether the data is complete or not, and if the data is incomplete, the data needs to be supplemented, and after the application data is complete, the complicated approval processes such as admission screening, pre-approval, credit investigation report inspection, fraud prevention investigation, risk report inspection and the like are carried out in sequence.
In the credit approval process in the prior art, multiple approval links are required, and third-party interfaces such as element authentication interfaces, personnel checking interfaces, credit investigation interfaces, fraud prevention interfaces and the like are also required to be called for many times, and the manual approval mode is added to the serial data calling of the multiple links, so that the approval time is too long, the data multiplexing is difficult, the approval efficiency is low, and the approval risk and the approval cost are increased.
Based on this, the embodiment of the application provides a credit business approval method, a credit business approval device and electronic equipment, which realize the automatic circulation of an approval process through a preset risk model, reduce the manual intervention process, and improve the approval efficiency, and specifically comprises the following steps:
referring to fig. 1, fig. 1 is a flowchart illustrating a first step of a credit business approval method according to an embodiment of the present application. As shown in fig. 1, the credit business approval method provided in the embodiment of the present application is applied to a credit approval system of a loan institution, and includes the following steps:
and S100, extracting the identity information corresponding to the applicant from the received credit service application data.
The applicant can log in the credit approval system to submit corresponding credit business application data, wherein the credit business application data comprises the loan amount and personal basic information corresponding to the applicant.
After receiving credit service application data corresponding to an applicant, identity information corresponding to the applicant needs to be extracted from personal basic information, wherein the identity information is a unique identity, such as an identification number and a mobile phone number, which can indicate or authenticate the applicant.
Specifically, the method is realized through a FLINK real-time task computing framework which is constructed in advance, so that the credit business application data submitted by an applicant can be guaranteed to be processed in time.
And S110, extracting target historical business data corresponding to the identity information from the risk data mart.
Wherein, the risk data mart comprises historical business data used for risk calculation.
In a specific embodiment, a data warehouse is pre-established in a payment institution, such as a bank, for a plurality of business systems existing in the payment institution, the data warehouse includes a plurality of data marts/data lakes with different business types, each business system generates different types of historical business data, a data manager performs statistics and summarization on the historical business data in each business system according to needs in advance, and stores the summarized historical business data in a data mart corresponding to the needs, such as a risk data mart, that is, according to the predefinition of a risk assessment person, the historical business data in each business system is screened, the historical business data related to risk calculation is screened and summarized to the risk data mart, and the historical business data in the risk data mart includes, but is not limited to, at least one of the following items: historical loan data, transaction data, and loan repayment records.
And acquiring target historical service data corresponding to the identity information from the risk data mart according to the acquired identity information corresponding to the applicant.
And S120, calling a plurality of preset third-party interfaces to acquire external risk data corresponding to the identity information.
Specifically, the plurality of preset third party interfaces include, but are not limited to, at least one of the following: a basic information screening interface, a central row credit interface, an anti-fraud interface, a blacklist interface, a social software interface.
External risk data includes, but is not limited to, at least one of: the public security data, the legal data, the social data and the consumption data related to the applicant are specifically allowed to call the corresponding interface to acquire the external risk data which is allowed to be called by the open authority only under the condition of obtaining the authorization of the third-party system.
In the method, in a flink computing framework, different third-party interfaces are used for being disassembled and packaged into corresponding flink operator tasks in order to acquire external risk data, the external risk data are used from different third-party interfaces in a parallel mode, data processing efficiency is improved, and meanwhile data acquisition instantaneity can be guaranteed.
And S130, determining a plurality of risk indexes to be analyzed according to the target historical business data and the external risk data.
Specifically, the step of determining a plurality of risk indicators to be analyzed according to the target historical service data and the external risk data includes:
determining a plurality of first risk indexes according to the target historical business data, determining a plurality of second risk indexes according to the external risk data, and processing and cleaning the plurality of first risk indexes and the plurality of second risk indexes to obtain a plurality of risk indexes to be analyzed.
In another preferred embodiment, the plurality of first risk indicators is determined by:
the method comprises the steps of obtaining a plurality of statistical formulas related to target historical service data, utilizing the statistical formulas to perform statistical summarization on the target historical service data, and determining a statistical result corresponding to each statistical formula as a first risk index corresponding to the statistical formula.
In a specific embodiment, the risk indicators are determined by predefined by a risk evaluator, each risk indicator has a corresponding statistical formula, that is, a mapping relationship between a plurality of risk indicators and a plurality of statistical formulas exists, and the statistical formula is used to perform statistical calculation on the target historical service data, so as to obtain the corresponding risk indicator.
For example, the first risk indicator is the accumulated outstanding credit times, the corresponding statistical formula is Count (payload _ non _ Count), and the accumulated outstanding credit times can be obtained by executing Count (payload _ non _ Count) in the target historical service data.
The determination method of the second risk indicator and the type of the first risk indicator are not described herein again.
Preferably, the step of processing and cleaning the plurality of first risk indicators and the plurality of second risk indicators to obtain the plurality of risk indicators to be analyzed includes:
the method comprises the steps of obtaining a plurality of preset derivative rules, wherein each preset derivative rule indicates a risk index for processing calculation and a calculation mode, utilizing each preset derivative rule to process derivative index calculation among a plurality of first risk indexes and/or a plurality of second risk indexes, determining a derivative index corresponding to each preset derivative rule, and forming a plurality of risk indexes to be analyzed jointly by the plurality of first risk indexes, the plurality of second risk indexes and the plurality of derivative indexes.
The risk assessment expert presets a derivative rule corresponding to each derivative index for risk calculation, and the derivative index is formed by various logic operation processes on the basis of the risk index.
In an example, the derivative index is a credit card consumption proportion, and the risk indexes indicated by the preset derivative rule are a credit card consumption amount and a total consumption amount, wherein the credit card consumption amount is a first risk index statistically acquired from the target historical business data, the total consumption amount is a second risk index statistically acquired from external risk data acquired from a third-party interface, and the corresponding calculation mode is to calculate a ratio between the credit card consumption amount and the total consumption amount.
S140, aiming at each preset risk model, extracting a plurality of target risk indexes to be analyzed, concerned by the preset risk model, from the plurality of risk indexes to be analyzed, inputting the preset risk model, and obtaining a risk prediction result corresponding to the preset risk model.
Specifically, the preset risk model is a model expert, an algorithm expert and the like in the risk calculation field, and is a model for risk prediction acquired through pre-training based on big data analysis mining, wherein the preset risk model includes but is not limited to at least one of the following items: a scoring model, an admission rule model, a risk model, and a decision tree model.
In a specific embodiment, the input index objects corresponding to each preset risk model are different, and for each preset risk model, a target risk index to be analyzed corresponding to the preset risk model needs to be screened out from a plurality of risk indexes to be analyzed to perform corresponding input and subsequent prediction.
In a preferred embodiment, a plurality of target risk indicators to be analyzed corresponding to each preset risk model are determined in the following manner:
and acquiring a preset input object set corresponding to the preset risk model, wherein the preset input object set comprises a plurality of preset input indexes, extracting the risk index to be analyzed matched with the preset input index from the plurality of risk indexes to be analyzed aiming at each preset input index, and determining the risk index to be analyzed as the target risk index to be analyzed corresponding to the preset risk model.
The risk prediction result corresponding to each preset risk model is different, for example, the risk prediction result corresponding to the score model is a risk prediction score, and the risk prediction result corresponding to the admission rule model may be FALSE or TRUE, where FALSE indicates that admission is not passed, and TRUE indicates that admission is passed.
And S150, determining a service auditing result corresponding to the credit service application data according to the risk prediction result corresponding to each preset risk model.
In a preferred embodiment, the step of determining the service auditing result corresponding to the credit service application data according to the risk prediction result corresponding to each preset risk model comprises:
and inputting the risk prediction results corresponding to the preset risk models into a preset disposal strategy model, and outputting a risk report and a service auditing result corresponding to the credit service application data by using the preset disposal strategy model according to the input multiple risk prediction results.
The preset disposal strategy model is also obtained by model experts, algorithm experts and the like in the risk calculation field based on big data analysis mining, the preset disposal strategy model is used for carrying out overall evaluation on risk prediction results corresponding to all preset risk models to obtain risk reports and business auditing results aiming at credit business application data, the risk reports comprise the risk prediction results corresponding to all the preset disposal strategy models and all risk indexes with credit risks, and the generated risk reports can be added with identity information corresponding to an applicant and cached in a database together for being checked, called and checked according to the identity information in the following process.
In a preferred embodiment, the preset handling policy model determines a service audit result corresponding to the credit service application data by:
and determining a target risk result set formed by the risk prediction results corresponding to the preset risk models, and determining a target business auditing result corresponding to the target risk result set according to the preset corresponding relation between the multiple risk result sets and the multiple business auditing results.
And the service auditing result comprises auditing passing, auditing failing and incapability of judging.
In a specific embodiment, if the preset risk model includes a score model and an admission rule model, and the risk prediction result output by the score model is 60 minutes, and the result output by the admission rule model is FALSE, the target risk result set formed by the score model is (60, FALSE), and after the (60, FALSE) is input into the preset disposal policy model, the target service audit result corresponding to the (60, FALSE) is determined as that the audit does not pass.
In another preferred embodiment, please refer to fig. 2, and fig. 2 shows a flowchart of the steps of a credit business approval method according to an embodiment of the present application. As shown in fig. 2, after step S150, the method further comprises:
and S160, if the service auditing result is that the auditing is passed, lending according to the loan amount corresponding to the credit service application data.
And S170, if the service audit result is that the audit is not passed, prompting that the data is abnormal and not performing loan.
And S180, if the service auditing result is that the service auditing can not be judged, sending the credit service application data and the risk report to a manual processing node for manual auditing.
Specifically, if the manual review result is that the review is passed, S181 is executed to loan the loan amount corresponding to the credit service application data, and if the manual review result is that the review is not passed, step 170 is executed.
Specifically, in the manual review process, the approver can analyze and review the credit service application data by combining the risk report to obtain a final manual review result.
Based on the same application concept, the embodiment of the application further provides a credit business approval device corresponding to the credit business approval method provided by the embodiment, and as the principle of solving the problem of the device in the embodiment of the application is similar to that of the credit business approval method in the embodiment of the application, the implementation of the device can refer to the implementation of the method, and repeated parts are not repeated.
Referring to fig. 3, fig. 3 is a functional block diagram of a credit business approval apparatus according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
a first extraction module 200, configured to extract identity information from the received credit service application data.
And a second extraction module 210, configured to extract target historical business data corresponding to the identity information from a risk data mart, where the risk data mart includes historical business data used for risk calculation.
The invoking module 220 is configured to invoke a plurality of preset third-party interfaces to obtain external risk data corresponding to the identity information.
The determining module 230 is configured to determine a plurality of risk indicators to be analyzed according to the target historical business data and the external risk data.
The prediction module 240 is configured to, for each preset risk model, extract a plurality of target risk indexes to be analyzed, which are concerned by the preset risk model, from the plurality of risk indexes to be analyzed, input the preset risk model, and obtain a risk prediction result corresponding to the preset risk model.
And the auditing module 250 is used for determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model.
Based on the same application concept, please refer to fig. 3, and fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, the electronic device 300 includes: a processor 310, a memory 320, and a bus 330, wherein the memory 320 stores machine-readable instructions executable by the processor 310, wherein the processor 310 communicates with the memory 320 via the bus 330 when the electronic device 300 is operating, and wherein the machine-readable instructions are executed by the processor 310 to perform the steps of the credit transaction approval method according to any of the above embodiments.
Based on the same application concept, the embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the credit business approval method provided by the above embodiment are performed.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the system and the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and details are not described herein again. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and 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 an indirect coupling or communication connection of devices or units through some communication interfaces, 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 place, 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 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A credit transaction approval method, the method comprising:
extracting identity information corresponding to the applicant from the received credit service application data;
extracting target historical business data corresponding to the identity information from a risk data mart, wherein the risk data mart comprises historical business data used for risk calculation;
calling a plurality of preset third-party interfaces to acquire external risk data corresponding to the identity information;
determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data;
for each preset risk model, extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed, inputting the preset risk model, and obtaining a risk prediction result corresponding to the preset risk model;
and determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model.
2. The method of claim 1, wherein determining a plurality of risk indicators to be analyzed based on the target historical business data and the external risk data comprises:
determining a plurality of first risk indicators according to the target historical service data;
determining a plurality of second risk indicators according to the external risk data;
and processing and cleaning the plurality of first risk indicators and the plurality of second risk indicators to obtain a plurality of risk indicators to be analyzed.
3. The method of claim 2, wherein the plurality of first risk indicators are determined by:
acquiring a plurality of statistical formulas associated with the target historical service data;
carrying out statistics and summarization on the target historical service data by using each statistical formula;
and determining the statistical result corresponding to each statistical formula as the first risk index corresponding to the statistical formula.
4. The method of claim 2, wherein processing and cleaning the first plurality of risk indicators and the second plurality of risk indicators to obtain the plurality of risk indicators to be analyzed comprises:
acquiring a plurality of preset derivative rules, wherein each preset derivative rule indicates a risk index and a calculation mode for processing calculation;
performing derivative index processing calculation between the plurality of first risk indexes and the plurality of second risk indexes by using each preset derivative rule, and determining a derivative index corresponding to each preset derivative rule;
and jointly forming a plurality of risk indexes to be analyzed by the plurality of first risk indexes, the plurality of second risk indexes and the plurality of derivative indexes.
5. The method according to claim 2, wherein the plurality of target risk indicators to be analyzed corresponding to each preset risk model are determined by:
acquiring a preset input object set corresponding to the preset risk model, wherein the preset input object set comprises a plurality of preset input indexes;
and aiming at each preset input index, extracting a risk index to be analyzed matched with the preset input index from the plurality of risk indexes to be analyzed, and determining the risk index to be analyzed as a target risk index to be analyzed corresponding to the preset risk model.
6. The method according to claim 1, wherein the step of determining a business audit result corresponding to the credit business application data based on the risk prediction result corresponding to each preset risk model comprises:
inputting the risk prediction result corresponding to each preset risk model into a preset disposal strategy model;
and outputting a risk report and a service auditing result corresponding to the credit service application data according to a plurality of input risk prediction results by using the preset disposal strategy model.
7. The method of claim 6, wherein the preset disposal policy model determines a business audit result corresponding to credit business application data by:
determining a target risk result set formed by the risk prediction results corresponding to the preset risk models;
and determining a target business auditing result corresponding to the target risk result set according to the preset corresponding relation between the plurality of risk result sets and the plurality of business auditing results.
8. The method according to claim 6, wherein the service audit result comprises audit pass, audit fail and no determination,
wherein the method further comprises:
if the service auditing result is that the auditing is passed, performing loan placement according to the loan amount corresponding to the credit service application data;
if the service audit result is that the audit is not passed, prompting that the data is abnormal and not performing loan;
and if the service auditing result is that the service auditing can not be judged, sending the credit service application data and the risk report to a manual processing node for manual auditing, if the manual auditing result is that the auditing is passed, lending according to the loan amount corresponding to the credit service application data, and if the manual auditing result is that the auditing is not passed, prompting that the data is abnormal and not lending.
9. A credit transaction approval apparatus, the apparatus comprising:
the first extraction module is used for extracting the identity information from the received credit service application data;
the second extraction module is used for extracting target historical business data corresponding to the identity information from a risk data mart, and the risk data mart comprises historical business data used for risk calculation;
the calling module is used for calling a plurality of preset third-party interfaces so as to acquire external risk data corresponding to the identity information;
the determining module is used for determining a plurality of risk indexes to be analyzed according to the target historical service data and the external risk data;
the prediction module is used for extracting a plurality of target risk indexes to be analyzed concerned by the preset risk model from a plurality of risk indexes to be analyzed aiming at each preset risk model, inputting the preset risk model and obtaining a risk prediction result corresponding to the preset risk model;
and the auditing module is used for determining a business auditing result corresponding to the credit business application data according to the risk prediction result corresponding to each preset risk model.
10. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when an electronic device is operating, the machine-readable instructions being executable by the processor to perform the steps of the credit business approval method of any one of claims 1 to 8.
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