Information insurance service risk control method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of risk management, and particularly discloses a risk control method and system for information insurance services, an electronic device and a storage medium, and the method comprises the steps: determining the overdue points of a target information insurance service person based on a preset overdue point rule in combination with the insurance policy overdue information of all insurance policies under the name of the target information insurance service person; when the overdue integral is greater than a preset value, based on the current level, the accumulated contribution premium, the entry time and the insurance policy overdue account type of the target information insurance service personnel, judging whether the target information insurance service personnel meets an exemption order stop rule, and obtaining a judgment result; and when the judgment result is no, outputting risk early warning information and carrying out order stop processing on the target information insurance service personnel. According to the invention, intelligent information insurance business risk control management can be realized, the management efficiency and accuracy are improved, the operation risk of a financial institution is reduced, and the high-standard requirement of the financial industry on information insurance business risk control management is met.

Description

Background Art

[0002] In the traditional risk management system, qualitative risk management is the main body, which is mainly based on subjective rules and customer ratings. It has shortcomings such as narrow data acquisition dimensions, weak quantitative analysis capabilities, and difficulty in accurately identifying user characteristics. In the context of digital transformation, the shortcomings of traditional risk management models in terms of risk management timeliness, model effectiveness, and monitoring scope are becoming increasingly prominent. For credit insurance business, how to identify and prevent overdue risks and violations of insurance sales personnel, and take timely measures such as suspension of orders to protect the interests of the company and the rights of consumers are technical issues that need to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a credit insurance business risk control method, system, electronic device and storage medium.

[0004] In a first aspect, the present invention provides a credit insurance business risk control method, and the technical solution of the method is as follows:

[0005] Based on the preset overdue points rules and in combination with the overdue information of all policies under the name of the target credit insurance business personnel, the overdue points of the target credit insurance business personnel are determined;

[0006] When the overdue points are greater than the preset value, based on the current rank, accumulated premium contribution, length of employment and type of overdue account of the target credit insurance business personnel, it is determined whether the target credit insurance business personnel meets the exemption suspension rules and a determination result is obtained;

[0007] When the judgment result is no, risk warning information is output and the target credit insurance business personnel are suspended.

[0008] The beneficial effects of the credit insurance business risk control method of the present invention are as follows:

[0009] The method of the present invention can realize intelligent credit insurance business risk control management, improve management efficiency and accuracy, reduce the operational risks of financial institutions, and meet the high standards of the financial industry for credit insurance business risk control management.

[0010] On the basis of the above scheme, a credit insurance business risk control method of the present invention can also be improved as follows.

[0011] In an optional manner, the method further includes:

[0012] Acquire original credit insurance business data of multiple credit insurance business personnel, and pre-process each original credit insurance business data to obtain multiple target credit insurance business data;

[0013] The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

[0014] In an optional manner, the original credit insurance business data includes: basic personnel information, sales performance data, customer feedback data, complaint records, and violation records.

[0015] In an optional manner, the policy overdue information includes: the age of the first overdue account, the overdue duration, the financing amount and the reason for the overdue.

[0016] In an optional manner, the method further includes:

[0017] When the judgment result is yes, risk warning information is output to the target credit insurance business personnel.

[0018] In a second aspect, the present invention provides a credit insurance business risk control system, and the technical solution of the system is as follows:

[0019] Includes: processing module, judgment module and risk control module;

[0020] The processing module is used to determine the overdue points of the target credit insurance business personnel based on the preset overdue points rule and in combination with the overdue information of all insurance policies under the name of the target credit insurance business personnel;

[0021] The judgment module is used to: when the overdue points are greater than a preset value, based on the current rank, accumulated premium contribution, length of employment and type of overdue account of the target credit insurance business personnel, judge whether the target credit insurance business personnel meets the exemption suspension rules and obtain a judgment result;

[0022] The risk control module is used to: when the judgment result is no, output risk warning information and suspend the order of the target credit insurance business personnel.

[0023] The beneficial effects of the credit insurance business risk control system of the present invention are as follows:

[0024] The system of the present invention can realize intelligent credit insurance business risk control management, improve management efficiency and accuracy, reduce the operational risks of financial institutions, and meet the high standards of the financial industry for credit insurance business risk control management.

[0025] On the basis of the above solution, the credit insurance business risk control system of the present invention can also be improved as follows.

[0026] In an optional manner, the method further includes: a building module; the building module is used to:

[0027] Acquire original credit insurance business data of multiple credit insurance business personnel, and pre-process each original credit insurance business data to obtain multiple target credit insurance business data;

[0028] The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

[0029] In an optional manner, the original credit insurance business data includes: basic personnel information, sales performance data, customer feedback data, complaint records, and violation records.

[0030] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0031] It includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the credit insurance business risk control method of the present invention are implemented.

[0032] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0033] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the credit insurance business risk control method of the present invention.

[0034] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0036] Figure 1 A schematic diagram of a flow chart of an embodiment of a credit insurance business risk control method of the present invention;

[0037] Figure 2 A schematic diagram of the structure of an embodiment of a credit insurance business risk control system of the present invention;

[0038] Figure 3 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0040] Figure 1 A flow chart of an embodiment of a credit insurance business risk control method provided by the present invention is shown, and the credit insurance business risk control method can be executed by an electronic device such as a terminal device or a server. The terminal device can be any fixed or mobile terminal such as a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the credit insurance business risk control method by calling computer-readable instructions stored in a memory through a processor. Figure 1 As shown, the following steps are included:

[0041] S1. Based on a preset overdue points rule and in combination with the overdue information of all insurance policies under the name of the target credit insurance business personnel, determine the overdue points of the target credit insurance business personnel.

[0042] Among them, the preset overdue points rules are shown in Table 1 below. The target credit insurance business personnel are the credit insurance business personnel who need to perform credit insurance business risk control in this embodiment. Each credit insurance business personnel has multiple policies corresponding to each other, and each policy corresponds to different policy overdue information. The single overdue points of the corresponding policy are determined according to the policy overdue information, and the single overdue points of all policies are accumulated to obtain the total overdue points. The policy overdue information includes: the first overdue age, overdue duration, financing amount and overdue reason.

[0043] Table 1:

[0044]

[0045] It should be noted that the first overdue age refers to the period in which the customer's first overdue occurred. For example, if the customer's first overdue occurred in the first period (one month after the loan was issued), then the first overdue age is 1. If the first overdue occurred in the third period (three months after the loan was issued), then the first overdue age is 3. For the company (creditor), the smaller the customer's (debtor's) first overdue age, the greater the risk, and thus the greater the overdue points (penalty items). The overdue duration refers to the number of days overdue. Since there are unintentional overdue situations, such as customers forgetting to repay on the due date and causing a delay of several days, the overdue points rule only penalizes overdue for more than 5 days. Since the overdue points rule is mainly used in short-term overdue scenarios, the situation of more than 30 days is not considered for the time being (determined by other models and rules), so the overdue duration is set to (5, 30).

[0046] S2. When the overdue points are greater than a preset value, based on the current rank, accumulated premium contribution, length of employment and type of overdue account of the target credit insurance business personnel, determine whether the target credit insurance business personnel meets the exemption suspension rules and obtain a determination result.

[0047] The default value is set to 5, and can also be set according to actual conditions. There is no limit here. The rules for exemption from suspension are shown in Table 2 below. The entry MOB indicates the length of time the credit insurance business personnel have been employed, in months.

[0048] Table 2:

[0049]

[0050] S3. When the judgment result is no, output risk warning information and suspend the order of the target credit insurance business personnel.

[0051] The risk warning information is output to the target terminal, which can be text-based risk warning information (such as risk warning messages) or voice-based risk warning information (such as voice prompt messages), without any restrictions. When the supervisor receives the risk warning information through the target terminal, he or she can manually or automatically stop the order of the target credit insurance business personnel.

[0052] In an optional manner, the method further includes:

[0053] The original credit insurance business data of multiple credit insurance business personnel are obtained, and each original credit insurance business data is pre-processed respectively to obtain multiple target credit insurance business data.

[0054] The original credit insurance business data of credit insurance business personnel include but are not limited to: basic information of personnel, sales performance data, customer feedback data, complaint records and violation records. The process of pre-processing the original credit insurance business data includes: data cleaning and sorting, forming a unified data format, and storing it in a data warehouse or big data platform. The target credit insurance business data is used to build a credit assessment model.

[0055] The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

[0056] Among them, according to the characteristics of the industry and the actual situation of the company, a credit assessment index system is designed, including but not limited to sales performance stability, customer complaint rate, and number of violations. The GPT big data model is used to analyze and process the collected and combined data of multiple target credit insurance businesses, and to construct a credit assessment model that includes preset overdue points rules.

[0057] It should be noted that the overdue points output by the credit assessment model need to be further weighted according to the reasons for the overdue. The classification table of the reasons for the overdue is shown in Table 3 below. The overdue points output by the credit assessment model are used to rate the risk of credit insurance business personnel and distinguish credit insurance business personnel with different risk levels. By real-time monitoring of the behavior data of credit insurance business personnel, once an abnormality or risk sign is found, the card control mechanism is immediately triggered.

[0058] Table 3:

[0059]

[0060] The red, yellow and blue colors of the overdue reasons are used to classify the severity of the overdue. Dark red indicates the worst overdue, and dark blue indicates overdue due to non-subjective or force majeure reasons. After investigation, the post-insurance personnel will label the policy (which will take effect after approval by multiple levels of leaders). When processing overdue points, the credit assessment model will weight them according to the red, yellow and blue labels, with dark red having a weight of 1.2, light red having a weight of 1.1, light yellow and dark yellow having a weight of 1, and light blue having a weight of 0.8. Dark blue (force majeure) will not be included in the overdue points.

[0061] In an optional manner, the method further includes:

[0062] When the judgment result is yes, risk warning information is output to the target credit insurance business personnel.

[0063] Among them, risk warning information is used to warn credit insurance business personnel who have abnormal overdue points but meet the exemption rules. Although such credit insurance business personnel will not be suspended, they will be warned, observed and other measures will be taken against them.

[0064] This embodiment monitors the performance of credit insurance business personnel and the short-term overdue situation of their insurance policies in real time. Once overdue behavior is found in the short term, timely suspension measures are taken, which can effectively prevent credit insurance business personnel from ignoring risks in pursuit of short-term performance and reduce the occurrence of illegal operations. The credit assessment model of this embodiment is based on a large amount of real-time data for analysis and prediction, providing accurate business data and risk warning information to the management, supporting scientific decision-making and refined management. By integrating advanced information technology, the credit assessment model of this embodiment can realize the automation of processes such as overdue identification, suspension processing, and collection follow-up, thereby improving management efficiency and accuracy. The suspension measures of this embodiment can encourage sales personnel to pay more attention to risk management and avoid excessive concentration of resources on high-risk or low-quality insurance policies, thereby optimizing the overall business structure.

[0065] The technical solution of this embodiment can realize intelligent risk control management of credit insurance business, improve management efficiency and accuracy, reduce the operational risks of financial institutions, and meet the high standards of the financial industry for risk control management of credit insurance business.

[0066] Figure 2 FIG. 2 is a schematic diagram showing a structure of an embodiment of a credit insurance business risk control system 200 provided by the present invention. Figure 2 As shown, the system 200 includes: a processing module 210, a judgment module 220 and a risk control module 230;

[0067] The processing module 210 is used to determine the overdue points of the target credit insurance business personnel based on the preset overdue points rule and in combination with the overdue information of all insurance policies under the name of the target credit insurance business personnel;

[0068] The judgment module 220 is used to: when the overdue points are greater than a preset value, based on the current rank, accumulated premium contribution, length of employment, and type of overdue account of the target credit insurance business personnel, determine whether the target credit insurance business personnel meets the exemption suspension rules and obtain a judgment result;

[0069] The risk control module 230 is used to: when the judgment result is no, output risk warning information and suspend the order of the target credit insurance business personnel.

[0070] In an optional manner, the method further includes: a building module; the building module is used to:

[0071] Acquire original credit insurance business data of multiple credit insurance business personnel, and pre-process each original credit insurance business data to obtain multiple target credit insurance business data;

[0072] The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

[0073] In an optional manner, the original credit insurance business data includes: basic personnel information, sales performance data, customer feedback data, complaint records, and violation records.

[0074] In an optional manner, the policy overdue information includes: the age of the first overdue account, the overdue duration, the financing amount and the reason for the overdue.

[0075] In an optional manner, the system further includes: a prompt module; the prompt module is used to:

[0076] When the judgment result is yes, risk warning information is output to the target credit insurance business personnel.

[0077] The technical solution of this embodiment can realize intelligent risk control management of credit insurance business, improve management efficiency and accuracy, reduce the operational risks of financial institutions, and meet the high standards of the financial industry for risk control management of credit insurance business.

[0078] The above-mentioned parameters and steps for each module to implement corresponding functions in the credit insurance business risk control system 200 of this embodiment can refer to the parameters and steps in the embodiment of the credit insurance business risk control method above, and will not be repeated here.

[0079] like Figure 3 As shown, an electronic device 300 of an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above credit insurance business risk control methods, specifically:

[0080] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements any credit insurance business risk control method provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for implementing device functions, which will not be repeated here.

[0081] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above-mentioned credit insurance business risk control methods.

[0082] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0083] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned credit insurance business risk control methods.

[0084] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0085] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0086] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0087] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A credit insurance business risk control method, characterized in that: include: Based on the preset overdue points rules and in combination with the overdue information of all policies under the name of the target credit insurance business personnel, the overdue points of the target credit insurance business personnel are determined; When the overdue points are greater than the preset value, based on the current rank, accumulated premium contribution, length of employment and type of overdue account of the target credit insurance business personnel, it is determined whether the target credit insurance business personnel meets the exemption suspension rules and a determination result is obtained; When the judgment result is no, risk warning information is output and the target credit insurance business personnel are suspended.

2. The credit insurance business risk control method according to claim 1, characterized in that: Also includes: Acquire original credit insurance business data of multiple credit insurance business personnel, and pre-process each original credit insurance business data to obtain multiple target credit insurance business data; The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

3. The credit insurance business risk control method according to claim 1, characterized in that: The original credit insurance business data includes: basic personnel information, sales performance data, customer feedback data, complaint records, and violation records.

4. The credit insurance business risk control method according to claim 1, characterized in that: The policy overdue information includes: the age of the first overdue account, the overdue duration, the financing amount and the reason for the overdue.

5. The credit insurance business risk control method according to any one of claims 1 to 4, characterized in that: Also includes: When the judgment result is yes, risk warning information is output to the target credit insurance business personnel.

6. A credit insurance business risk control system, characterized in that: include: Processing module, judgment module and risk control module; The processing module is used to determine the overdue points of the target credit insurance business personnel based on the preset overdue points rule and in combination with the overdue information of all insurance policies under the name of the target credit insurance business personnel; The judgment module is used to: when the overdue points are greater than a preset value, based on the current rank, accumulated premium contribution, length of employment and type of overdue account of the target credit insurance business personnel, judge whether the target credit insurance business personnel meets the exemption suspension rules and obtain a judgment result; The risk control module is used to: when the judgment result is no, output risk warning information and suspend the order of the target credit insurance business personnel.

7. The credit insurance business risk control system according to claim 6, characterized in that: Also includes: Building blocks; the building blocks are used to: Acquire original credit insurance business data of multiple credit insurance business personnel, and pre-process each original credit insurance business data to obtain multiple target credit insurance business data; The GPT big data model is used to analyze and process all target credit insurance business data, and a credit assessment model including the preset overdue points rule is constructed. The credit assessment model is used to calculate and generate the exemption suspension rule based on the sales fluctuation coefficient.

8. The credit insurance business risk control system according to claim 6, characterized in that: The original credit insurance business data includes: basic personnel information, sales performance data, customer feedback data, complaint records, and violation records.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the credit insurance business risk control method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the credit insurance business risk control method according to any one of claims 1 to 5.

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