CN117575775A - Service risk detection method, device, equipment and storage medium - Google Patents
Service risk detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The disclosure provides a business risk detection method, a device, equipment and a storage medium, which can be applied to the technical fields of big data and financial science and technology. The method comprises the following steps: classifying initial risk business information in an initial risk business information set based on a business type of a target business and a risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business; determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and inputting the target business risk information in the target business risk information set into a target risk detection model, and outputting a risk detection result of the target business risk information.
Description
Technical Field
The present disclosure relates to the technical field of big data and financial science and technology, and in particular, to a business risk detection method, apparatus, device, storage medium and program product.
Background
The scientific risk management mechanism is a necessary guarantee of the steady operation of the financial institutions, is also a premise of the standard operation behavior of the financial institutions, and takes a very important role as an important business part of the financial institutions in credit management.
In the course of conception of the present disclosure, the inventors found that the following drawbacks exist in the related art for credit risk management: the accuracy of risk detection is poor; the risk management is relatively low in efficiency, and the requirements of actual business are difficult to meet.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a business risk detection method, apparatus, device, storage medium, and program product.
According to a first aspect of the present disclosure, there is provided a business risk detection method, including: classifying initial risk business information in an initial risk business information set based on a business type of a target business and a risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business;
Determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and
and inputting the target business risk information in the target business risk information set into the target risk detection model, and outputting a risk detection result of the target business risk information.
According to an embodiment of the present disclosure, the target business risk information set includes a risk detection information subset and a product quality index information subset;
the method for detecting the risk of the target business risk includes the steps of inputting the target business risk information set into the target risk detection model, and outputting a risk detection result of the target business risk information, wherein the method comprises the following steps:
determining the risk detection information subset, wherein the risk detection information subset comprises product information, risk type information, region information, detection frequency information, detection dimension information and a detection threshold value;
determining a subset of the product quality index information corresponding to the detection dimension information according to the detection dimension information and the product information; and;
and inputting the risk detection information subset and the product quality index information subset into the target risk detection model, and outputting a risk detection result of the target business risk information.
According to an embodiment of the present disclosure, the determining the subset of the product quality index information corresponding to the detection dimension information according to the detection dimension information and the product information includes:
determining product quality information corresponding to the mechanism information to be detected and the personnel information to be detected according to the mechanism information to be detected, the personnel information to be detected and the product information; and
generating the subset of product quality index information according to the product quality information, wherein the product quality index information comprises at least one of the following: poor, overdue, scissors difference, reject ratio, overdue ratio, scissors difference ratio.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information includes a first risk detection result;
the inputting the risk detection information subset and the product quality index information subset into the target risk detection model, and outputting a risk detection result of the target business risk information, includes:
under the condition that the target risk detection model is determined to be associated with the to-be-detected mechanism information, matching product information in the risk detection information subset with product information in the product quality index information subset to generate a first matching result, wherein the first matching result comprises product information and to-be-detected mechanism information corresponding to the product information; and
And inputting the first matching result into the target risk detection model, and outputting the first risk detection result.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information further includes a second risk detection result, and the method further includes:
under the condition that the target risk detection model is determined to be associated with the personnel information to be detected, matching product information in the risk detection information subset with product information in the product quality index information subset to generate a second matching result, wherein the second matching result comprises product information and the personnel information to be detected corresponding to the product information; and
and inputting the second matching result into the target risk detection model, and outputting the second risk detection result.
According to an embodiment of the present disclosure, after the inputting the target business risk information set into the target risk detection model and outputting the risk detection result of the target business risk information, the method further includes:
comparing the risk detection result with a preset threshold value to generate a comparison result; and
and generating alarm information under the condition that the comparison result is determined to be characterized as a risk service.
According to an embodiment of the present disclosure, the preset threshold value is associated with the detection frequency information, and the detection frequency information includes at least one of: monthly detection information and quarterly detection information.
A second aspect of the present disclosure provides a business risk detection device, including:
the classification processing module is used for classifying initial risk business information in an initial risk business information set based on a business type of a target business and the risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business;
the target risk detection model determining module is used for determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and
and the risk detection result output module is used for inputting the target business risk information in the target business risk information set into the target risk detection model and outputting a risk detection result of the target business risk information.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method described above.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.
A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.
According to the business risk detection method, the device, the equipment, the storage medium and the program product, the plurality of target business risk information sets are obtained by classifying the initial risk business information in the initial risk business information sets based on the business type of the target business and the risk type of the target business, so that the target risk detection model can be flexibly determined from the plurality of business risk detection models according to the region information and the detection dimension information in the target business risk information, and further, the risk detection result of the target business risk information can be obtained by inputting the target business risk information sets into the target risk detection model, and the efficiency and the accuracy of business risk detection are improved.
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The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of a business risk detection method, apparatus, device, storage medium, and program product according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a business risk detection method according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a structural diagram of a target risk detection model generated by a business risk detection method according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a structural diagram of a credit business risk detection device generated by a business risk detection method according to an embodiment of the present disclosure;
fig. 5 schematically illustrates a block diagram of a business risk detection device according to an embodiment of the present disclosure; and
fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a business risk detection method according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical solution of the present disclosure, the related user information (including, but not limited to, user personal information, user image information, user equipment information, such as location information, etc.) and data (including, but not limited to, data for analysis, stored data, displayed data, etc.) are information and data authorized by the user or sufficiently authorized by each party, and the related data is collected, stored, used, processed, transmitted, provided, disclosed, applied, etc. and processed, all in compliance with the related laws and regulations and standards of the related country and region, necessary security measures are taken, no prejudice to the public order, and corresponding operation entries are provided for the user to select authorization or rejection.
The inventor finds that in the related art, in terms of credit risk management, the prior art does not consider different risk indexes and risk dimensions, and lacks differentiated risk management; the efficiency and accuracy of risk management are low, and the requirements of actual business are difficult to meet. In view of this, the method, device, equipment and storage medium for detecting business risk provided in the present disclosure, the method for detecting business risk includes: classifying initial risk business information in an initial risk business information set based on a business type of a target business and a risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business; determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and inputting the target business risk information in the target business risk information set into a target risk detection model, and outputting a risk detection result of the target business risk information.
Fig. 1 schematically illustrates an application scenario diagram of a business risk detection method, apparatus, device, storage medium and program product according to an embodiment of the present disclosure.
As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the business risk detection method provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the business risk detection device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The service risk detection method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the service risk detection apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically illustrates a flow chart of a business risk detection method according to an embodiment of the present disclosure.
As shown in fig. 2, the business risk detection method of this embodiment includes operations S210 to S230.
In operation S210, the initial risk service information in the initial risk service information set is classified based on the service type of the target service and the risk type of the target service to obtain a plurality of target service risk information sets, wherein the target service risk information in the target service risk information sets includes region information, detection frequency information and detection dimension information, the risk type of the target service includes a result risk type and a process risk type, and the risk type of the target service is associated with the service type of the target service.
According to an embodiment of the present disclosure, the initial risk service information may be risk service information before the classification process is not performed. The business types of the target business may be classified by the loan object into a personal loan business, a company loan business, a short-term loan, a medium-term loan, a long-term loan, etc., and it is understood that the personal loan business and the company loan business may be classified into different business types, for example, the personal loan business may be classified into: a housing loan, etc. It should be noted that the service type may be set according to the actual situation, and is not limited herein. The risk types of the target business may include a result risk type and a process risk type, the result risk type may represent a risk type of a final result of the loan, the process risk type may represent a risk type of the loan measured before the occurrence of the result risk, it may be understood that the process risk may affect the result risk, and a single process risk may not reflect whether the result risk is ultimately generated, but may affect the probability of occurrence of the result risk.
In operation S220, a target risk detection model is determined from a plurality of business risk detection models based on the region information and the detection dimension information in the target business risk information.
According to embodiments of the present disclosure, the determination of the target risk detection model may be based on the region information and the detection dimension information in the target risk information. Each branch institution of the financial institution can select different target risk detection models according to the management requirement of the area, and the product information, the area information, the detection frequency information, the detection dimension information and the detection threshold value corresponding to each target risk detection model are different. Meanwhile, the financial institutions can select different target risk detection models according to detection dimension information, wherein the detection dimension information can comprise information of institutions to be detected and information of personnel to be detected.
In operation S230, the target business risk information in the target business risk information set is input into the target risk detection model, and a risk detection result of the target business risk information is output.
According to embodiments of the present disclosure, the resulting risk type information may characterize whether a business is at risk, the duty cycle of the risk business, etc., and may include a failure rate, e.g., the "failure rate" may calculate the specific gravity of a loan that has failed (risk has occurred) to the total loan balance, which may be a core indicator that measures the quality of a financial institution's credit assets. The quality of the result risk type target service may be affected by the process risk type target service, and when the process risk type target service is not ideal, the result risk type target service may be caused to be not ideal, so that it is also necessary to detect the process risk type target service. The process risk type information may include: the rate of overdue and the rate of shear difference, for example, may characterize the specific gravity of computing the excess loan (which may not have formed a bad loan) over the total loan balance, with the higher the rate of overdue, the higher the probability of bad loan occurrence.
In a possible embodiment, the risk control degree and the control service scene corresponding to the result risk type information and the process risk type information can be set for risk management and control after subsequent triggering and early warning. For example, the process risk type information such as "overdue rate" may set a flexible prompt, act on the borrowing and paying scene, the result risk type information such as "reject rate" may set a rigid control, act on a plurality of business scenes such as business application, borrowing and paying, etc., block at key nodes of loan handling, and omnidirectionally prevent and control expansion of business risk.
In one possible embodiment, each financial institution may set different risk type information and its corresponding management measures according to its own business risk management method. When the risk type information is set, a user needs to input a risk name corresponding to the risk type information, a statistical caliber of the risk, a risk type, a risk control degree and a risk control scene, and once the risk type information is set, the system can generate a set result record and provide a query function, and then the risk type information can be selected for setting a monitoring model in classification management and control. In particular, in the present disclosure, table 1 is illustrated as an example, and table 1 is what is included in risk type information according to an embodiment of the present disclosure.
TABLE 1
According to the embodiment of the disclosure, the initial risk business information in the initial risk business information set is classified based on the business type of the target business and the risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business; determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and inputting the target business risk information in the target business risk information set into a target risk detection model, and outputting a risk detection result of the target business risk information.
According to an embodiment of the present disclosure, the target business risk information set includes a risk detection information subset and a product quality index information subset.
According to an embodiment of the present disclosure, inputting a target business risk information set into a target risk detection model, outputting a risk detection result of target business risk information, includes: determining a risk detection information subset, wherein the risk detection information subset comprises product information, risk type information, region information, detection frequency information, detection dimension information and a detection threshold value; determining a product quality index information subset corresponding to the detection dimension information according to the detection dimension information and the product information; and; and inputting the risk detection information subset and the product quality index information subset into a target risk detection model, and outputting a risk detection result of target business risk information.
According to an embodiment of the present disclosure, the detection frequency information may include monthly detection information and quarterly detection information, and the detection threshold may be a threshold set by a primary branch in the financial institution according to the regional information and the actual condition of the service, and the specific threshold is not limited herein, for example, a certain branch set a target risk detection model in which the expiration rate of a newly issued personal hand housing loan exceeds 1.4% in the last year of a customer manager (person to be detected) in the monthly detection jurisdiction, where 1.4% is the detection threshold. The detection dimension information may include information of an organization to be detected and information of a person to be detected, which may be an organization person responsible for a business, such as a customer manager.
According to an embodiment of the present disclosure, the risk detection information subset may be characterized as a risk detection information list, specifically, in the present disclosure, table 2 is illustrated by way of example, and table 2 is a schematic diagram of contents included in the risk detection information subset according to an embodiment of the present disclosure.
TABLE 2
| Fields |
| Setting mechanism code |
| Detecting product codes |
| Risk type (Process class/outcome class) |
| Risk encoding |
| Risk name |
| Detecting frequency |
| Dimension of detection (mechanism/person to be detected) |
| Detecting a threshold value |
| Degree of risk control |
| Risk control scenario |
| Setting time |
The subset of product quality indicator information may be characterized as a list of product quality indicator information, and in particular, in this disclosure, table 3 is illustrated by way of example, and table 3 is what the subset of product quality indicator information includes in accordance with an embodiment of the present disclosure.
TABLE 3 Table 3
According to an embodiment of the present disclosure, determining a subset of product quality index information corresponding to detection dimension information from the detection dimension information, the product information, includes: determining product quality information corresponding to the mechanism information to be detected and the personnel information to be detected according to the mechanism information to be detected, the personnel information to be detected and the product information; and generating a subset of product quality index information based on the product quality information, wherein the product quality index information comprises at least one of: poor, overdue, scissors difference, reject ratio, overdue ratio, scissors difference ratio.
According to embodiments of the present disclosure, the business information may contain batches of data, which may be processed using a distributed system framework, for example, using HDFS for processing and computing of the batches of data. In particular, in the present disclosure, a data lake may be generated according to batch data, and in the data lake, product quality information may be generated periodically according to various product information, mechanism information to be detected, and personnel information to be detected.
In one possible embodiment, the loan balance, the bad amount (five-level classified as bad, i.e. secondary, suspicious, lost, etc.) of the same product at the same business organization, the overdue amount (the loan balance with the overdue days greater than 0), the scissors difference (the overdue amount-bad amount) and the calculated bad rate, overdue rate and scissors difference rate of the same product at the same business organization can be calculated according to the first investigator (i.e. the client manager), the business organization and the product number of the loan business.
According to the embodiment of the disclosure, product quality information corresponding to the mechanism information to be detected and the personnel information to be detected is determined according to the mechanism information to be detected, the personnel information to be detected and the product information; and generating a product quality index information subset according to the product quality information, so that the blank of service risk detection for service institutions and service sponsors in the prior art is made up, and the accuracy of service risk detection is improved.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information includes a first risk detection result; the risk detection information subset and the product quality index information subset are input into a target risk detection model, and a risk detection result of target business risk information is output, and the method comprises the following steps: under the condition that the target risk detection model is determined to be associated with the to-be-detected mechanism information, matching product information in the risk detection information subset with product information in the product quality index information subset to generate a first matching result, wherein the first matching result comprises the product information and the to-be-detected mechanism information corresponding to the product information; and inputting the first matching result into the target risk detection model, and outputting the first risk detection result.
According to the embodiment of the disclosure, under the condition that the target risk detection model is determined to be associated with the mechanism information to be detected, product codes in a product quality index information list and the codes of the primary mechanism to which the product codes belong can be matched by using product information and mechanism information (a setting mechanism of the target risk detection model) in the target risk detection model, all records of the products under the primary mechanism are obtained, and then the risk detection results of the mechanisms to which the same target service belongs are calculated by means of arithmetic average, so that the risk detection results of all branch mechanisms in the next stage are obtained.
According to the embodiment of the disclosure, differential risk detection is performed on the mechanism to be detected, and then the early warning result is applied to financial risk system control, so that the risk of the prevention service is further enlarged, and intelligent, efficient and standardized risk detection and risk control are realized.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information further includes a second risk detection result, and the method further includes: under the condition that the target risk detection model is determined to be associated with the personnel information to be detected, matching the product information in the risk detection information subset with the product information in the product quality index information subset to generate a second matching result, wherein the second matching result comprises the product information and the personnel information to be detected corresponding to the product information; and inputting the second matching result into the target risk detection model, and outputting a second risk detection result.
According to the embodiment of the disclosure, under the condition that the association of the target risk detection model and the personnel information to be detected (client manager) is determined, product information and mechanism information (a setting mechanism of the target risk detection model) in the target risk detection model can be utilized to match product codes in a product quality index information list and codes of a primary mechanism to which the product codes belong, all records of the product under the primary mechanism are obtained, index data of the same personnel to be detected (client manager) are arithmetically averaged, and index results of all the personnel to be detected (client manager) in the jurisdiction are calculated.
According to the embodiment of the disclosure, differentiated risk detection is performed on the personnel to be detected (client manager), and then the early warning result is applied to financial risk system control, so that the risk of the prevention service is further enlarged, and intelligent, efficient and normalized risk detection and risk control are realized.
According to an embodiment of the present disclosure, the input of the target business risk information set into the target risk detection model, after outputting the risk detection result of the target business risk information, further includes: comparing the risk detection result with a preset threshold value to generate a comparison result; and generating alarm information under the condition that the comparison result is determined to be characterized as risk service.
According to the embodiment of the disclosure, the preset threshold value can be determined according to the user requirement and the actual service condition, and is not limited herein. When the comparison result is determined to be characterized as a risk service, relevant alarm information receiving personnel can be notified to take corresponding measures based on the user type and the user information in the alarm information, for example, corresponding measures are taken for the risk user, wherein the risk alarm notification mode includes but is not limited to short messages, mails, telephone messages, mobile phone App message pushing, weChat public numbers and the like, and the specific mode of risk alarm can be determined based on the alarm content or the actual application scene and is not limited specifically.
According to an embodiment of the present disclosure, the preset threshold is associated with detection frequency information including at least one of: monthly detection information and quarterly detection information.
According to the embodiment of the disclosure, in the case that the detection frequency information is monthly detection, a risk detection result of the last day of the month may be taken and compared with a preset threshold. Under the condition that the detection frequency information is detected according to seasons, risk detection results of last day of the seasons are compared with a preset threshold value, and for institutions to be detected or people to be detected (client manager) exceeding the preset threshold value, the hit risk detection results can be written into a risk detection result list, and early warning notification is issued, so that all levels of lines can be queried. In particular, in the present disclosure, table 4 is illustrated as an example, and table 4 is the content of a risk detection result list according to an embodiment of the present disclosure.
TABLE 4 Table 4
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In a feasible embodiment, the comparison is performed according to the risk detection result and a preset threshold value, and under the condition that the comparison result is characterized as a risk service, alarm information is generated, so that corresponding risk management and control measures can be performed on a mechanism to be detected or a person to be detected (a client manager) triggering an alarm in the system, and subsequent risks are timely prevented. Control measures can be added in a risk management and control scene, for example, in a paying business stage, a risk management and control service is added and invoked, parameters are set to be scene codes (such as 01-business application and 02-borrowing and paying), product codes, business mechanism numbers and numbers of people to be detected (client manager), whether effective risk records of the products exist in a risk detection result list of the people to be detected (client manager) or the institutions to be detected are checked, if so, a risk control degree field of the risk records can be returned, and business handling control is performed strictly. The card stopping and repeating management function can be newly built in the system, and each mechanism to be detected or person to be detected can inquire and stop card complaints or card repeating application can be carried out aiming at certain early warning information. If the detection result is disagreeed by the mechanism to be detected or the personnel to be detected (client manager), the card stopping complaint can be carried out, and after the application and the material proof are submitted, the card stopping can be released by checking and approval by the upper-level mechanism. Or after meeting the card-returning requirement specified by the upper-level organization, submitting a card-returning application, and returning cards by checking and approval of the upper-level row to remove the management and control of business risks.
Fig. 3 schematically illustrates a structural diagram of a target risk detection model generated by a business risk detection method according to an embodiment of the present disclosure.
As shown in fig. 3, the target risk detection model may include product information, risk type, risk name, detection frequency, detection dimension and preset threshold, and the user may select different loan products (product information), risk names, detection frequency (monthly or quarterly), detection dimension (dimension of a mechanism to be detected or dimension of a person to be detected), and preset threshold to set a differentiated target risk detection model according to the management requirement of the local area.
Fig. 4 schematically illustrates a structural diagram of a credit business risk detection apparatus generated by a business risk detection method according to an embodiment of the present disclosure.
As shown in fig. 4, the credit business risk detection device may include a risk management and control function module, an operation monitoring function module, and a data index function module, where the risk management and control function module may include parameter setting, management and control service, and management and control release; the operation detection function module can comprise detection early warning, and the data index function module can comprise a reject rate index, a personal loan litigation rate index, a overdue rate index, a scissors difference rate index and a mortgage registration office rate index. The individual loan business processes may include loan application, loan approval, loan release, lifetime management, loan settlement, and the like.
The present disclosure also provides a business risk detection device, as shown in fig. 5, based on the above business risk detection method. The device will be described in detail below in connection with fig. 5.
Fig. 5 schematically illustrates a block diagram of a business risk detection device according to an embodiment of the present disclosure.
As shown in fig. 5, the business risk detection device 500 of this embodiment includes a classification processing module 510, a target risk detection model determining module 520, and a risk detection result outputting module 530.
The classification processing module 510 is configured to classify initial risk service information in an initial risk service information set based on a service type of a target service and a risk type of the target service to obtain a plurality of target service risk information sets, where the target service risk information in the target service risk information sets includes region information, detection frequency information, and detection dimension information, the risk type of the service risk includes a result risk type and a process risk type, and the risk type of the target service is associated with the service type of the target service. In an embodiment, the classification processing module 510 may be configured to perform the operation S210 described above, which is not described herein.
The target risk detection model determining module 520 is configured to determine a target risk detection model from a plurality of business risk detection models based on the region information and the detection dimension information in the target business risk information. In an embodiment, the target risk detection model determining module 520 may be configured to perform the operation S220 described above, which is not described herein.
The risk detection result output module 530 is configured to input target business risk information in the target business risk information set into the target risk detection model, and output a risk detection result of the target business risk information. In an embodiment, the risk detection result output module 530 may be used to perform the operation S230 described above, which is not described herein.
According to an embodiment of the present disclosure, the target business risk information set includes a risk detection information subset and a product quality index information subset; wherein, risk detection result output module includes: the risk detection information subset determining sub-module, the product quality index information subset determining sub-module and the risk detection result outputting sub-module.
The risk detection information subset determining sub-module is used for determining a risk detection information subset, wherein the risk detection information subset comprises product information, risk type information, region information, detection frequency information, detection dimension information and a detection threshold value.
And the product quality index information subset determining sub-module is used for determining a product quality index information subset corresponding to the detection dimension information according to the detection dimension information and the product information.
And the risk detection result output sub-module is used for inputting the risk detection information subset and the product quality index information subset into the target risk detection model and outputting a risk detection result of target business risk information.
According to an embodiment of the present disclosure, a product quality index information subset determination submodule includes: the product quality information determining unit and the product quality index information subset generating unit.
The product quality information determining unit is used for determining product quality information corresponding to the to-be-detected mechanism information and the to-be-detected personnel information according to the to-be-detected mechanism information, the to-be-detected personnel information and the product information.
A product quality index information subset generating unit, configured to generate a product quality index information subset according to product quality information, where the product quality index information includes at least one of: poor, overdue, scissors difference, reject ratio, overdue ratio, scissors difference ratio.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information includes a first risk detection result; wherein, risk detection result output submodule includes: a first matching result generating unit and a first risk detection result output unit.
The first matching result generating unit is used for matching the product information in the risk detection information subset with the product information in the product quality index information subset under the condition that the target risk detection model is determined to be associated with the to-be-detected mechanism information, and generating a first matching result, wherein the first matching result comprises the product information and the to-be-detected mechanism information corresponding to the product information.
And the first risk detection result output unit is used for inputting the first matching result into the target risk detection model and outputting the first risk detection result.
According to an embodiment of the present disclosure, the risk detection result of the target business risk information further includes a second risk detection result, and the risk detection result output sub-module further includes: a second matching result generating unit and a second risk detection result output unit.
And the second matching result generating unit is used for matching the product information in the risk detection information subset with the product information in the product quality index information subset under the condition that the target risk detection model is determined to be associated with the personnel information to be detected, and generating a second matching result, wherein the second matching result comprises the product information and the personnel information to be detected corresponding to the product information.
And the second risk detection result output unit is used for inputting the second matching result into the target risk detection model and outputting a second risk detection result.
According to an embodiment of the present disclosure, the business risk detection device further includes: the device comprises a comparison result generation module and an alarm information generation module.
The comparison result generation module is used for inputting the target business risk information set into the target risk detection model, outputting a risk detection result of the target business risk information, and comparing the risk detection result with a preset threshold value to generate a comparison result.
The alarm information generation module is used for inputting the target business risk information set into the target risk detection model, outputting a risk detection result of the target business risk information, and generating alarm information under the condition that the comparison result is determined to be characterized as the risk business.
Any of the multiple modules of the classification processing module 510, the target risk detection model determination module 520, and the risk detection result output module 530 may be combined in one module, or any of the multiple modules may be split into multiple modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. At least one of the classification processing module 510, the target risk detection model determination module 520, and the risk detection result output module 530 may be implemented, at least in part, as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware, such as any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware, in accordance with embodiments of the present disclosure. Alternatively, at least one of the classification processing module 510, the target risk detection model determination module 520, and the risk detection result output module 530 may be at least partially implemented as a computer program module that, when executed, may perform the corresponding functions.
Fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a business risk detection method according to an embodiment of the disclosure.
As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to an input/output (I/O) interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to an input/output (I/O) interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code means for causing a computer system to carry out the business risk detection method provided by the embodiments of the present disclosure when the computer program product is run on the computer system.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (11)
1. A business risk detection method, comprising:
classifying initial risk business information in an initial risk business information set based on a business type of a target business and a risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the target business comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business;
Determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and
inputting the target business risk information in the target business risk information set into the target risk detection model, and outputting a risk detection result of the target business risk information.
2. The method of claim 1, wherein the set of target business risk information comprises a subset of risk detection information and a subset of product quality indicator information;
the target business risk information set is input into the target risk detection model, and a risk detection result of the target business risk information is output, including:
determining the risk detection information subset, wherein the risk detection information subset comprises product information, risk type information, region information, detection frequency information, detection dimension information and a detection threshold value;
determining a subset of the product quality index information corresponding to the detection dimension information according to the detection dimension information and the product information; and
and inputting the risk detection information subset and the product quality index information subset into the target risk detection model, and outputting a risk detection result of the target business risk information.
3. The method of claim 2, wherein the determining the subset of product quality indicator information corresponding to the detection dimension information from the detection dimension information and the product information comprises:
determining product quality information corresponding to the mechanism information to be detected and the personnel information to be detected according to the mechanism information to be detected, the personnel information to be detected and the product information; and
generating the product quality index information subset according to the product quality information, wherein the product quality index information comprises at least one of the following: poor, overdue, scissors difference, reject ratio, overdue ratio, scissors difference ratio.
4. The method of claim 2, wherein the risk detection result of the target business risk information comprises a first risk detection result;
the inputting the risk detection information subset and the product quality index information subset into the target risk detection model, and outputting the risk detection result of the target business risk information, includes:
under the condition that the target risk detection model is determined to be associated with the to-be-detected mechanism information, matching product information in the risk detection information subset with product information in the product quality index information subset to generate a first matching result, wherein the first matching result comprises product information and to-be-detected mechanism information corresponding to the product information; and
And inputting the first matching result into the target risk detection model, and outputting the first risk detection result.
5. The method of claim 4, the risk detection result of the target business risk information further comprising a second risk detection result, the method further comprising:
under the condition that the target risk detection model is determined to be associated with the personnel information to be detected, matching product information in the risk detection information subset with product information in the product quality index information subset to generate a second matching result, wherein the second matching result comprises product information and the personnel information to be detected corresponding to the product information; and
and inputting the second matching result into the target risk detection model, and outputting the second risk detection result.
6. The method of claim 1, wherein the inputting the target business risk information set into the target risk detection model, after outputting the risk detection result of the target business risk information, further comprises:
comparing the risk detection result with a preset threshold value to generate a comparison result; and
And generating alarm information under the condition that the comparison result is determined to be characterized as a risk service.
7. The method of claim 6, wherein the preset threshold is associated with the detection frequency information, the detection frequency information comprising at least one of: monthly detection information and quarterly detection information.
8. A business risk detection device, comprising:
the classification processing module is used for classifying initial risk business information in an initial risk business information set based on a business type of a target business and a risk type of the target business to obtain a plurality of target business risk information sets, wherein the target business risk information in the target business risk information sets comprises region information, detection frequency information and detection dimension information, the risk type of the business risk comprises a result risk type and a process risk type, and the risk type of the target business is related to the business type of the target business;
the target risk detection model determining module is used for determining a target risk detection model from a plurality of service risk detection models based on region information and detection dimension information in the target service risk information; and
And the risk detection result output module is used for inputting the target business risk information in the target business risk information set into the target risk detection model and outputting a risk detection result of the target business risk information.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-7.
11. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.
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