A risk control management method and system based on group multi-enterprise customers

Abstract

The present invention provides a risk control management method and system based on group-based multiple enterprise customers, which belongs to the field of risk control management technology, and specifically includes: determining the associated data sources of credit application associated data of different dimensions of enterprise customers based on the update status of enterprise customers in the group, and utilizing the association status of different associated data sources with the credit application associated data of different enterprise customers to determine whether it is necessary to perform verification processing on the credit application associated data of the group's enterprise customers based on the deviation status of the credit application associated data of different enterprise customers between different associated data sources when the data processing difficulty of the associated data source meets the requirements, thereby improving the timeliness of the control and processing of credit risks.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk control management, and in particular, relates to a risk control management method and system based on group-based multi-enterprise customers. Background Art

[0002] In order to achieve group risk rating, the influence weight of each target enterprise on the group to be evaluated is determined in the invention patent application CN202011572800.2 "Group rating method, device and electronic equipment based on knowledge graph", and the default probability of the group to be evaluated is obtained according to each influence weight and the default probability of the target enterprise to determine the rating of the group to be evaluated, which achieves the technical effect of improving group customer risk management. However, it is not difficult to find the following technical problems through analysis:

[0003] When conducting risk control management for group customers, there are deviations in the credibility of risk-related data, such as sales data, contract data, and financial data, submitted by different target companies when evaluating the probability of default. Therefore, if risk control management for group multi-enterprise customers cannot be conducted based on the credibility of risk-related data, timely management of credit risks cannot be achieved effectively and accurately.

[0004] In response to the above technical problems, the present application specifically provides a risk control management method and system based on group-based multi-enterprise customers. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a risk control management method based on group-based multi-enterprise customers is provided.

[0007] A risk control management method based on group-based multi-enterprise customers, specifically including:

[0008] S1 determines the associated data sources of the credit application associated data of different dimensions of the corporate customers based on the update status of the corporate customers in the group, and uses the association status of different associated data sources with the credit application associated data of different corporate customers to determine that the data processing difficulty of the associated data source does not meet the requirements, then proceeds to the next step;

[0009] S2 determines the historical data update status of different associated data sources, and determines the data source of interest in the associated data sources in combination with the association status of different associated data sources with the credit application associated data of different corporate customers;

[0010] S3 determines the deviation of the credit application-related data of different corporate customers between different data sources of interest based on the data update status of different data sources of interest, and uses the deviation to determine that there are corporate customers whose credit application-related data does not meet the requirements, and then proceeds to the next step;

[0011] S4: Based on the deviation of the credit application-related data of different corporate customers between different related data sources, it is determined whether it is necessary to perform verification processing on the credit application-related data of the corporate customers of the group.

[0012] The beneficial effects of the present invention are:

[0013] By utilizing the association between different associated data sources and the credit application associated data of different corporate customers, it is determined that the data processing difficulty of the associated data source does not meet the requirements, thereby avoiding the need to verify the credibility of the credit application associated data of all enterprises in the group from different associated data sources, which leads to technical problems such as slow processing efficiency in scenarios with greater data processing difficulty. Through the assessment of data processing difficulty, differentiated verification and processing methods are implemented in different business scenarios, which improves the efficiency of verification processing while ensuring the credibility of credit application associated data.

[0014] Based on the deviation of credit application-related data of different corporate customers between different associated data sources, it is determined whether it is necessary to verify and process the credit application-related data of the group's corporate customers. Full consideration is given to the deviation of credit application-related data of different corporate customers in the group between different associated data sources, and the credit application-related data of corporate customers in the group with large changes and low credibility due to deviations between different associated data sources are screened out, thereby achieving effective control of credit risks of the group's corporate customers.

[0015] A further technical solution is that the credit application-related data includes financial statement data, contract data, business registration data, enterprise fixed asset data and employee data.

[0016] A further technical solution is that the associated data source is a data source containing the credit application associated data.

[0017] A further technical solution is to determine that the data processing difficulty of the associated data source does not meet the requirement, specifically including:

[0018] Determine the corresponding associated data sources for different corporate customers based on the association between different associated data sources and the associated data of credit applications of different corporate customers;

[0019] Determine complex processing customers among the enterprise customers based on the associated data sources corresponding to different enterprise customers;

[0020] According to the number of the complex processing customers, it is determined whether the data processing difficulty of the associated data source meets the requirements.

[0021] A further technical solution is that the complex processing customers among the enterprise customers are enterprise customers whose corresponding number of associated data sources is greater than a preset number of data sources.

[0022] A further technical solution is to determine whether the data processing difficulty of the associated data source meets the requirements when the number of the complex processing customers is greater than the preset number of complex customers.

[0023] A further technical solution is that when the data processing difficulty of the associated data source meets the requirements, the deviation of the corporate customer's credit application associated data between different associated data sources is analyzed according to a preset time period to determine whether it is necessary to verify the credit application associated data of the group's corporate customers.

[0024] A further technical solution is to determine whether it is necessary to verify the credit application-related data of the corporate customers of the group, which specifically includes:

[0025] Based on the deviation of the credit application related data of different corporate customers in the group between different related data sources, determining the credit application related data of different corporate customers that have deviations between different related data sources, and using the deviation related data as the deviation related data;

[0026] Determine data deviation customers among the enterprise customers according to the amount of deviation-related data of different enterprise customers;

[0027] Whether it is necessary to verify the credit application-related data of the corporate customers of the group is determined by the proportion of the number of customers with data deviation in the group.

[0028] A further technical solution is that when the proportion of data-deviant customers in the group is greater than the proportion of preset deviation customers, it is determined that the credit application-related data of the corporate customers of the group needs to be verified.

[0029] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned risk control management method based on group multi-enterprise customers when running the computer program.

[0030] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0033] Figure 1 It is a flow chart of a risk control management method based on group-based multi-enterprise customers;

[0034] Figure 2 It is a flowchart to determine that the data processing difficulty of the associated data source does not meet the requirements;

[0035] Figure 3 is a flow chart of a method for determining a data source of interest in an associated data source;

[0036] Figure 4 It is a flow chart of a method for determining a corporate customer whose credit application-related data does not meet the requirements for credibility;

[0037] Figure 5 is a flow chart for determining whether it is necessary to conduct a verification process on the credit application-related data of the corporate customers of the group;

[0038] Figure 6 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0040] In this application, based on the consistency of data between existing online data sources among corporate customers in the group, screening of corporate customers whose credit application-related data in the group is not highly credible is achieved, thereby determining whether verification and processing of the credit application-related data in the group is required, ensuring the credibility of credit application-related data of different corporate customers in the group, and achieving timely and effective control of credit risks.

[0041] When the number of associated data sources of the credit application associated data of the corporate customers in the group is greater than 10, it is determined that the data processing difficulty of the associated data sources does not meet the requirement.

[0042] The data source of interest is an associated data source whose historical data has been updated more than 5 times in the most recent preset time period and whose number of corporate customers associated with the credit application-related data accounts for more than 0.7.

[0043] When the dimensions of the credit application-related data of the corporate customer with data deviations between different data sources of interest are greater than three, it is determined that the credibility of the credit application-related data of the corporate customer does not meet the requirements.

[0044] When a corporate customer whose credit application-related data has deviations between different related data sources is regarded as a basic deviation corporate customer, and when the number of basic deviation corporate customers in the group accounts for more than 0.6, it is determined that the credit application-related data of the group's corporate customers need to be verified.

[0045] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a first aspect is provided. The present invention proposes a risk control management method based on group multi-enterprise customers, specifically including:

[0046] S1 determines the associated data sources of the credit application associated data of different dimensions of the corporate customers based on the update status of the corporate customers in the group, and uses the association status of different associated data sources with the credit application associated data of different corporate customers to determine that the data processing difficulty of the associated data source does not meet the requirements, then proceeds to the next step;

[0047] Furthermore, the credit application-related data includes financial statement data, contract data, business registration data, enterprise fixed asset data, and employee data.

[0048] Specifically, the associated data source is a data source containing the credit application associated data.

[0049] Specifically, Figure 2 As shown, determining that the data processing difficulty of the associated data source does not meet the requirements specifically includes:

[0050] Determine the corresponding associated data sources for different corporate customers based on the association between different associated data sources and the associated data of credit applications of different corporate customers;

[0051] Determine complex processing customers among the enterprise customers based on the associated data sources corresponding to different enterprise customers;

[0052] According to the number of the complex processing customers, it is determined whether the data processing difficulty of the associated data source meets the requirements.

[0053] Furthermore, the complex processing customers among the enterprise customers are enterprise customers whose corresponding number of associated data sources is greater than the preset number of data sources.

[0054] It should be noted that, when the number of complex processing customers is greater than the preset number of complex customers, it is determined whether the data processing difficulty of the associated data source meets the requirement.

[0055] It is understandable that when the data processing difficulty of the associated data source meets the requirements, the deviation of the corporate customer's credit application associated data between different associated data sources is analyzed according to a preset time period to determine whether it is necessary to verify the credit application associated data of the group's corporate customers.

[0056] In another possible embodiment, determining that the data processing difficulty of the associated data source does not meet the requirement specifically includes:

[0057] Based on the association between different associated data sources and the credit application associated data of different corporate customers, determine the corporate customers of the group corresponding to the different associated data sources, and use them as associated corporate customers;

[0058] Determining a complex processing data source in the associated data sources based on the number of associated enterprise customers of different associated data sources;

[0059] According to the number of the complex processing data sources, it is determined whether the data processing difficulty of the associated data sources meets the requirements.

[0060] Further, when the number of the complex processing data sources is greater than the preset number of processing data sources, it is determined that the data processing difficulty of the associated data sources does not meet the requirements.

[0061] Optionally, determining whether the data processing difficulty of the associated data source meets the requirement specifically includes:

[0062] The number of corporate customers in the group is obtained, and the total amount of credit application-related data in the group is determined in combination with the amount of credit application-related data of different corporate customers. When the total amount of credit application-related data in the group does not meet the requirement, it is determined that the data processing difficulty of the related data source does not meet the requirement.

[0063] When the total amount of credit application-related data in the group meets the requirements:

[0064] Determine the associated data sources corresponding to the corporate customers in the group based on the association between different associated data sources and the credit application associated data of different corporate customers; when the total number of associated data sources corresponding to the corporate customers in the group is greater than the preset number of data sources, determine that the data processing difficulty of the associated data sources does not meet the requirement;

[0065] When the total number of associated data sources corresponding to the corporate customers in the group is not greater than the preset number of data sources:

[0066] When the total amount of the credit application-related data in the group is less than a preset data amount threshold, it is determined that the data processing difficulty of the related data source meets the requirement;

[0067] When the total amount of credit application-related data in the group is not less than the preset data amount threshold:

[0068] Determine the associated data sources corresponding to different corporate customers based on the association between different associated data sources and the credit application associated data of different corporate customers, and determine that the data processing difficulty of the associated data sources meets the requirements when it is determined that there is no complex processing data source in the associated data sources based on the number of associated corporate customers of the different associated data sources;

[0069] Based on the number of associated enterprise customers of different associated data sources, it is determined that there is a complex processing data source in the associated data sources: when the number of the complex processing data sources does not meet the requirement, it is determined that the data processing difficulty of the associated data source does not meet the requirement;

[0070] When the number of complex processing data sources meets the requirement:

[0071] Determine the data processing complexity coefficients of different enterprise customers according to the number of associated data sources corresponding to different enterprise customers and the amount of associated data in the different associated data sources; when the number of enterprise customers whose data processing complexity coefficients are greater than a preset complexity coefficient threshold is greater than the preset number of enterprise customers, determine that the data processing difficulty of the associated data source does not meet the requirements;

[0072] When the number of enterprise customers whose processing complexity coefficient is greater than the preset complexity coefficient threshold is not greater than the preset number of enterprise customers:

[0073] According to the data processing complexity coefficients of different enterprise customers and the number of the associated data sources, the comprehensive processing complexity coefficients of the associated data sources are determined, and the comprehensive processing complexity coefficients are used to determine whether the data processing difficulty of the associated data sources meets the requirements.

[0074] It should be noted that, when the comprehensive processing complexity coefficient of the associated data source is greater than a preset complexity coefficient threshold, it is determined that the data processing difficulty of the associated data source does not meet the requirements.

[0075] S2 determines the historical data update status of different associated data sources, and determines the data source of interest in the associated data sources in combination with the association status of different associated data sources with the credit application associated data of different corporate customers;

[0076] Furthermore, the historical data update status of the associated data source includes the interval length between different historical data update times of the associated data source and the update data volume of different historical data update times.

[0077] It is understandable that if Figure 3 As shown, the method for determining the data source of interest in the associated data source is:

[0078] Based on the historical data update status of the associated data source, determine the number of historical data updates of the associated data source in the most recent preset period, and use it as the screening update number;

[0079] Determine the updated data amount of the associated data in different screening update times according to the updated status of the credit application associated data of the corporate customers in the group in different screening update times, and determine the updated association times in the screening update times by using the updated data amount of the associated data;

[0080] Based on the update association count, it is determined whether the associated data source is a focused data source.

[0081] Furthermore, the update association times is the number of screening updates when the update data volume of the associated data is greater than a preset update data volume threshold.

[0082] It should be noted that, when the update association times is greater than a preset association times threshold, the associated data source is determined to be a focus data source.

[0083] Optionally, the method for determining the data source of interest in the associated data source is:

[0084] Determine the number of times the historical data of the associated data source is updated in different preset periods based on the historical data update status of the associated data source, and determine the data update frequency of the associated data source based on the average value of the number of times the historical data of the associated data source is updated in different preset periods;

[0085] Based on the association between the associated data source and the credit application associated data of different corporate customers, determine the ratio of the number of associated corporate customers of the associated data source in the group, and use it as the association quantity ratio;

[0086] The data update correlation coefficient of the associated data source is determined according to the data update frequency and the proportion of the associated quantity, and the data update correlation coefficient is used to determine whether the associated data source is a data source of interest.

[0087] Furthermore, the data update correlation coefficient of the associated data source is determined according to the product of the data update frequency and the percentage of the associated quantity.

[0088] It should be noted that the data update correlation coefficient has a value range of 0 to 1, wherein when the data update correlation coefficient of the associated data source is greater than a preset correlation coefficient threshold, the associated data source is determined to be a focus data source.

[0089] Optionally, the method for determining the data source of interest in the associated data source is:

[0090] S21, based on the association between the associated data source and the credit application associated data of different corporate customers, determining the ratio of the number of associated corporate customers of the associated data source in the group, and using it as the association quantity ratio;

[0091] Optionally, the above step S21 includes the following contents:

[0092] S211 determines the associated corporate customers of the associated data source in the group based on the association between the associated data source and the credit application associated data of different corporate customers. When the number of associated corporate customers is less than the preset number of corporate customers, it is determined that the associated data source does not belong to the concerned data source. When the number of associated corporate customers is not less than the preset number of corporate customers, the process proceeds to step S212.

[0093] S212: taking the proportion of the number of associated enterprise customers of the associated data source in the group as the associated quantity proportion; when the associated quantity proportion is greater than a preset associated quantity proportion threshold, the process proceeds to step S213; when the associated quantity proportion is not greater than the preset associated quantity proportion threshold, the process proceeds to step S22;

[0094] S213: When the number of the associated enterprise customers is greater than the preset enterprise customer number threshold, it is determined that the associated data source belongs to the focus data source; when the number of the associated enterprise customers is not greater than the preset enterprise customer number threshold, the process proceeds to step S22.

[0095] S22: determining the number of historical data updates of the associated data source within a recent preset period based on the historical data update status of the associated data source, and determining the data real-time coefficient of the associated data source in combination with the intervals between different historical data update times;

[0096] Optionally, the above step S22 includes the following contents:

[0097] S221 determines the number of historical data updates of the associated data source in the most recent preset period based on the historical data update situation of the associated data source. When the number of historical data updates of the associated data source in the most recent preset period is greater than the preset update number threshold, it is determined that the associated data source belongs to the concerned data source. When the number of historical data updates of the associated data source in the most recent preset period is not greater than the preset update number threshold, the process proceeds to step S222.

[0098] When S222 determines that there is a number of historical data updates with an interval length less than a preset interval length threshold based on the interval lengths between different historical data update times, the process proceeds to step S223; when there is no number of historical data updates with an interval length less than the preset interval length threshold, the process proceeds to step S224;

[0099] S223: When the interval time of the associated data source in the most recent preset period is less than the preset interval time threshold and the number of historical data updates is greater than the preset number threshold, it is determined that the associated data source belongs to the concerned data source; when the interval time of the associated data source in the most recent preset period is less than the preset interval time threshold and the number of historical data updates is not greater than the preset number threshold, the process proceeds to step S224;

[0100] S224 determines the data real-time coefficient of the associated data source based on the number of historical data updates of the associated data source within the most recent preset period and the interval length between different historical data update times. When the data real-time coefficient of the associated data source is greater than the preset real-time coefficient threshold, it is determined that the associated data source belongs to the focus data source. When the data real-time coefficient of the associated data source is not greater than the preset real-time coefficient threshold, the process proceeds to step S23.

[0101] S23 determines data update correlation coefficients for different historical data update times based on the updated data amounts of credit application-related data of different corporate customers at different historical data update times within a recent preset period;

[0102] Optionally, the above step S23 includes the following contents:

[0103] S231 determines data update correlation coefficients for different historical data update times based on the updated data amounts of credit application-related data of different corporate customers at different historical data update times within a recent preset period;

[0104] S232: When there is a historical data update number whose data update correlation coefficient is greater than the preset update correlation coefficient threshold, the process proceeds to step S233; when there is no historical data update number whose data update correlation coefficient is greater than the preset update correlation coefficient threshold, the process proceeds to step S24;

[0105] S233 When the data update correlation coefficient within the most recent preset period is greater than the preset update correlation coefficient threshold and the number of historical data updates is greater than the preset correlation update number threshold, it is determined that the associated data source belongs to the focus data source; when the data update correlation coefficient within the most recent preset period is greater than the preset update correlation coefficient threshold and the number of historical data updates is not greater than the preset correlation update number threshold, proceed to step S24.

[0106] S24 determines the data update correlation coefficient of the associated data source by multiplying the data real-time coefficient of the associated data source, the average value of the data update correlation coefficients of different historical data update times, and the proportion of the associated quantity, and uses the data update correlation coefficient to determine whether the associated data source is a data source of interest.

[0107] S3 determines the deviation of the credit application-related data of different corporate customers between different data sources of interest based on the data update status of different data sources of interest, and uses the deviation to determine that there are corporate customers whose credit application-related data does not meet the requirements, and then proceeds to the next step;

[0108] Furthermore, the deviation of the credit application-related data of the enterprise customer between different data sources of interest includes the number of related data sources in which the credit application-related data of different dimensions have deviations.

[0109] It should be noted that if Figure 4 As shown, the method for determining the corporate customers whose credibility of the credit application associated data does not meet the requirements is:

[0110] Based on the deviation of credit application-related data of different dimensions between different data sources of interest, determining the credit application-related data of the corporate customer that has deviations between different data sources of interest, and using the deviation-related data as the deviation-related data;

[0111] Determine the deviation weight coefficient of different deviation associated data based on the proportion of the number of data sources of interest with deviations in different deviation associated data among the data sources of interest with the deviation associated data;

[0112] The data deviation value of the credit application associated data of the enterprise customer is determined by summing the deviation weight coefficients of different deviation associated data, and based on the data deviation value, it is determined whether the credibility of the credit application associated data of the enterprise customer meets the requirements.

[0113] Further, when the data deviation value of the credit application-related data of the corporate customer does not meet the requirement, it is determined that the credibility of the credit application-related data of the corporate customer does not meet the requirement.

[0114] S4: Based on the deviation of the credit application-related data of different corporate customers between different related data sources, it is determined whether it is necessary to perform verification processing on the credit application-related data of the corporate customers of the group.

[0115] In addition, it should be noted that Figure 5 As shown, determining whether it is necessary to check the credit application-related data of the corporate customers of the group includes:

[0116] Based on the deviation of the credit application related data of different corporate customers in the group between different related data sources, determining the credit application related data of different corporate customers that have deviations between different related data sources, and using the deviation related data as the deviation related data;

[0117] Determine data deviation customers among the enterprise customers according to the amount of deviation-related data of different enterprise customers;

[0118] Whether it is necessary to verify the credit application-related data of the corporate customers of the group is determined by the proportion of the number of customers with data deviation in the group.

[0119] Furthermore, when the proportion of customers with data deviation in the group is greater than the preset proportion of customers with data deviation, it is determined that the credit application-related data of the corporate customers of the group needs to be verified.

[0120] Embodiment 2 In the second aspect, as Figure 6 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned risk control management method based on group multi-enterprise customers when running the computer program.

[0121] Optionally, determining whether it is necessary to perform verification processing on the credit application-related data of the corporate customers of the group specifically includes:

[0122] Determine the credit application associated data of different corporate customers in the group that have deviations between different associated data sources based on the deviations between the different associated data sources, and use the deviation associated data as the deviation associated data; when the number of corporate customers in the group that have deviation associated data is less than a preset customer number threshold, determine that it is not necessary to perform verification processing on the credit application associated data of the corporate customers in the group;

[0123] When the number of corporate customers with deviated associated data in the group is not less than the preset customer number threshold:

[0124] The corporate customers with deviation-related data are regarded as basic deviation corporate customers. When the proportion of the basic deviation corporate customers in the group is greater than the proportion of the preset corporate customers, it is determined that the credit application-related data of the corporate customers of the group needs to be verified;

[0125] When the proportion of the number of basic deviation corporate customers in the group is not greater than the proportion of the number of preset corporate customers:

[0126] When determining that there are data deviation customers among the enterprise customers according to the number of deviation-related data of different enterprise customers,

[0127] When the proportion of data-deviant customers in the group does not meet the requirement, it is determined that the credit application-related data of the corporate customers of the group needs to be verified;

[0128] When there are no data deviation customers among the enterprise customers or the proportion of data deviation customers in the group meets the requirements:

[0129] Determine the customer data deviation coefficients of different basic deviation corporate customers based on the number of deviation-related data of different basic deviation corporate customers in the group and the deviation of different deviation-related data between different related data sources; when the customer data deviation coefficients of different basic deviation corporate customers are all less than a preset deviation coefficient threshold, determine that it is not necessary to perform verification processing on the credit application-related data of the corporate customers of the group;

[0130] When there are enterprise customers whose customer data deviation coefficient is not less than the preset deviation coefficient threshold:

[0131] When the proportion of corporate customers whose customer data deviation coefficient is not less than the preset deviation coefficient threshold is greater than the proportion of the preset deviation customers, it is determined that the credit application-related data of the corporate customers of the group needs to be verified;

[0132] When the proportion of enterprise customers whose customer data deviation coefficient is not less than the preset deviation coefficient threshold is not greater than the proportion of the preset deviation customers:

[0133] The credit data deviation value of the group is determined based on the percentage of corporate customers whose customer data deviation coefficient is not less than a preset deviation coefficient threshold and the customer data deviation coefficient of corporate customers with different basic preferences, and the credit data deviation value is used to determine whether it is necessary to verify the credit application related data of the group's corporate customers.

[0134] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0135] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0136] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A risk control management method based on group-based multi-enterprise customers, characterized in that: Specifically include: Based on the update status of the corporate customers in the group, determine the associated data sources of the credit application associated data of different dimensions of the corporate customers, and use the association status of different associated data sources with the credit application associated data of different corporate customers to determine that the data processing difficulty of the associated data source does not meet the requirements, then proceed to the next step; Determine the historical data update status of different associated data sources, and determine the data source of interest in the associated data sources in combination with the association status of different associated data sources with the credit application associated data of different corporate customers; Based on the data update status of different concerned data sources, the deviation of the credit application-related data of different corporate customers between different concerned data sources is determined, and when the corporate customers whose credit application-related data does not meet the requirements are determined by using the deviation, the next step is entered; Determine whether it is necessary to verify the credit application related data of the corporate customers of the group based on the deviation between different related data sources of the credit application related data of different corporate customers; Determining that the data processing difficulty of the associated data source does not meet the requirements specifically includes: Determine the corresponding associated data sources for different corporate customers based on the association between different associated data sources and the associated data of credit applications of different corporate customers; Determine complex processing customers among the enterprise customers based on the associated data sources corresponding to different enterprise customers; According to the number of the complex processing customers, it is determined whether the data processing difficulty of the associated data source meets the requirements; the complex processing customers among the corporate customers are corporate customers whose corresponding number of associated data sources is greater than the preset number of data sources.

2. The risk control management method based on group-based multi-enterprise customers as claimed in claim 1, characterized in that: The credit application-related data includes financial statement data, contract data, business registration data, enterprise fixed asset data and employee data.

3. The risk control management method based on group-based multi-enterprise customers as claimed in claim 1, characterized in that: The associated data source is a data source containing the credit application associated data.

4. The risk control management method based on group-based multi-enterprise customers as claimed in claim 1, characterized in that: When the data processing difficulty of the associated data source meets the requirements, the deviation of the corporate customer's credit application associated data between different associated data sources is analyzed according to a preset time period to determine whether it is necessary to verify the credit application associated data of the group's corporate customers.

5. The risk control management method based on group-based multi-enterprise customers as claimed in claim 1, characterized in that: The historical data update status of the associated data source includes the interval length between different historical data update times of the associated data source and the update data volume of different historical data update times.

6. The risk control management method based on group-based multi-enterprise customers as claimed in claim 1, characterized in that: Determine whether it is necessary to verify the credit application-related data of the group's corporate customers, including: Based on the deviation of the credit application related data of different corporate customers in the group between different related data sources, determining the credit application related data of different corporate customers that have deviations between different related data sources, and using the deviation related data as the deviation related data; Determine data deviation customers among the enterprise customers according to the amount of deviation-related data of different enterprise customers; Whether it is necessary to verify the credit application-related data of the corporate customers of the group is determined by the proportion of the number of customers with data deviation in the group.

7. The risk control management method based on group-based multi-enterprise customers as claimed in claim 6, characterized in that: When the proportion of the number of data-deviant customers in the group is greater than the preset proportion of the number of data-deviant customers, it is determined that the credit application-related data of the corporate customers of the group needs to be verified.

8. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a risk control management method based on group multi-enterprise customers as described in any one of claims 1-7 is executed.

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