CN111353901A - Risk identification monitoring method and device and electronic equipment - Google Patents
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Abstract
The application provides a risk identification monitoring method and device and electronic equipment, relates to the technical field of data processing, and solves the technical problem that effective risk control is influenced due to the fact that accuracy of risk identification is low. The method comprises the following steps: based on the first customer information, carrying out fraud recognition by using a pre-trained anti-fraud model, and determining a non-fraudulent second customer; based on the behavior information of the second customer, carrying out risk identification by using a pre-trained application admission scoring model to obtain a risk value of the second customer; pre-checking by using a pre-trained credit line model based on the asset information of the third client to determine the guarantee amount granted to the third client; predicting the default risk probability of the fourth customer based on the first insurance application period data of the fourth customer, and performing early warning according to the prediction result; and predicting the repayment condition of the fifth client by utilizing a pre-trained collection risk model based on the second insurance application period data of the fifth client, and carrying out overdue prompt according to the prediction result.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a risk identification monitoring method and apparatus, and an electronic device.
Background
Risk control refers to the risk manager taking various measures and methods to eliminate or reduce the various possibilities of occurrence of a risk event, or the risk controller reducing the losses incurred when a risk event occurs. While more effective risk control requires a more accurate risk identification process.
At present, the credit insurance business is carried by a 'bank-like' passenger group which does not meet a bank credit release threshold, and the 'secondary' characteristic of the qualification of the passenger group leads the credit insurance to bear higher risk than the bank; the business mode of 'credit insurance + bank loan' is that the insurance product is not the traditional insurance product, but the composite product of insurance and finance. The income of insurance companies comes from the premium to be paid, and the information and insurance bear the risk of default claim of the applicant (principal + interest) and the loss risk of the premium once the applicant defaults; in addition, the credit insurance business is a heavy asset business, the scale development and the asset quality of the credit insurance business are greatly influenced by the scale and the quality of an offline sales team and the professional literacy of an examination and approval team, and the complexity of the risk of the credit insurance business puts higher requirements on the wind control capability; combined with the cost of the capital itself, the composite factor becomes the root cause for the higher price of the trust guarantee than the bank.
The existing risk identification monitoring is generally aimed at defense risks of customers, and credit risk detection is carried out before the customers keep in reserve. However, the risk identified by this method is low in accuracy and easily affects the effectiveness of risk control.
Disclosure of Invention
The invention aims to provide a risk identification monitoring method, a risk identification monitoring device and electronic equipment, so as to solve the technical problem that effective risk control is influenced due to low accuracy of risk identification.
In a first aspect, an embodiment of the present application provides a risk identification monitoring method, where the method includes:
based on the first customer information, carrying out fraud recognition by utilizing a pre-trained anti-fraud model, and determining a non-fraudulent second customer according to a recognition result;
based on the behavior information of the second customer, carrying out risk identification by using a pre-trained application admission scoring model to obtain a risk value of the second customer;
pre-checking by using a pre-trained credit line model based on the asset information of a third client, and determining the guarantee amount granted to the third client; the third client is the client of which the risk value is smaller than a preset value in the second client;
predicting the default risk probability of a fourth customer based on first insurance application period data of the fourth customer to obtain a first prediction result, and performing early warning according to the first prediction result; the fourth client is a client without overdue condition in the third clients;
predicting the repayment condition of a fifth client by utilizing a pre-trained acceptance risk model based on second insurance application period data of the fifth client to obtain a second prediction result, and carrying out overdue prompt according to the second prediction result; the fifth client is a client with overdue condition in the third clients.
In one possible implementation, the first customer information includes any one or more of:
the application information, credit investigation information and financial attributes of the first client.
In one possible implementation, the algorithm of the anti-fraud model includes any one or more of:
logistic algorithm, LightGBM algorithm and XGBOOST algorithm.
In one possible implementation, the behavior information includes loan information and/or credit investigation dimension information of the second client.
In one possible implementation, the application admission scoring model is generated by a machine learning algorithm.
In one possible implementation, the first application period data includes historical data and/or behavioral characteristic variables of the fourth customer during the application period.
In one possible implementation, the second application period data includes historical data of the fifth client during the application period, behavior characteristic variables, commitment and repayment information, and application and backup note information.
In a second aspect, a risk identification monitoring apparatus is provided, the apparatus comprising:
the first identification module is used for carrying out fraud identification by utilizing a pre-trained anti-fraud model based on the first customer information and determining a non-fraudulent second customer according to an identification result;
the second identification module is used for carrying out risk identification by utilizing a pre-trained application admission scoring model based on the behavior information of the second customer to obtain a risk value of the second customer;
the pre-checking module is used for pre-checking by utilizing a pre-trained credit line model based on the asset information of a third client and determining the guarantee amount granted to the third client; the third client is the client of which the risk value is smaller than a preset value in the second client;
the first prediction module is used for predicting the default risk probability of a fourth customer based on first insurance application period data of the fourth customer to obtain a first prediction result, and early warning is carried out according to the first prediction result; the fourth client is a client without overdue condition in the third clients;
the second prediction module is used for predicting the repayment condition of a fifth client by using a pre-trained revenue acceleration risk model based on second insurance application period data of the fifth client to obtain a second prediction result, and performing overdue prompt according to the second prediction result; the fifth client is a client with overdue condition in the third clients.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the method of the first aspect when executing the computer program.
In a fourth aspect, this embodiment of the present application further provides a computer-readable storage medium storing machine executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect.
The embodiment of the application brings the following beneficial effects:
the risk identification monitoring method, the risk identification monitoring device and the electronic equipment provided by the embodiment of the application can perform fraud identification by utilizing a pre-trained anti-fraud model based on first customer information, determine a non-fraudulent second customer according to an identification result, perform risk identification by utilizing a pre-trained application admission scoring model based on behavior information of the second customer so as to obtain a risk value of the second customer, perform pre-check by utilizing a pre-trained credit line model based on asset information of a third customer so as to determine a guarantee amount granted to a third customer, wherein the risk value of the third customer is a customer of which the risk value is smaller than a preset value in the second customer, predict a default risk probability of the fourth customer based on first guarantee period data of the fourth customer so as to obtain a first prediction result, perform early warning according to the first prediction result, wherein, the fourth client is a client who has no overdue condition in the third client, and the repayment condition of the fifth client is predicted by utilizing a pre-trained collection risk model based on the second insurance period data of the fifth client so as to obtain a second prediction result, and overdue prompt is carried out according to the second prediction result, wherein the fifth client is a client who has the overdue condition in the third client, in the scheme, the data of the clients are applied to all life cycle processes of the information and security service before, during and after the insurance, a plurality of recognition models are utilized to form an intelligent wind control closed loop, the intelligent wind control closed loop is suitable for risk control of each link before, during and after the insurance, not only can the standardized risk prediction monitoring be carried out objectively, the early warning can be tracked, the risk exposure of different types, periods and sources can be effectively dealt with, and the whole risk recognition and monitoring of the life cycle of the clients are realized, the technical problem that the accuracy of risk identification is low and effective risk control is influenced is solved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart diagram of a risk identification monitoring method provided in an embodiment of the present application;
FIG. 2 is a schematic view of another flowchart of a risk identification monitoring method provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a risk identification monitoring apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram illustrating an electronic device provided in an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "comprising" and "having," and any variations thereof, as referred to in the embodiments of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
At present, the credit insurance business is carried by a 'bank-like' passenger group which does not meet a bank credit release threshold, and the 'secondary' characteristic of the qualification of the passenger group leads the credit insurance to bear higher risk than the bank; the business mode of 'credit insurance + bank loan' is that the insurance product is not the traditional insurance product, but the composite product of insurance and finance. The credit insurance company is another 'fund party' except the bank, the loan fund of the insurance company is the premium to be paid, and once the borrower generates default, the credit insurance carries the default settlement risk (principal, interest and penalty) of the borrower and the loss risk of the premium; in addition, the credit insurance business is a heavy asset business, the scale development and the asset quality of the credit insurance business are greatly influenced by the scale and the quality of an offline sales team and the professional literacy of an examination and approval team, the complexity of risks of the credit insurance business puts higher requirements on wind control capacity, and the credit insurance business is also a root cause that the fixed price of the credit insurance business is higher than that of a bank.
Due to the characteristics of the information and insurance service, the borrower has many misreads of the information and insurance, such as 'compliance of charging in a questioning premium', 'interest' is too high, and 'high interest loan', and the like, the information and insurance is a regular customer of customer complaints, and the customer cancellation rate is higher; meanwhile, the products of the information security are also restricted by related cooperative mechanisms, and need to bear a large amount of operation and maintenance work and risks; in addition, the double attributes of the credit insurance service 'finance' + 'insurance' lead to unclear commercial positioning and serious brand construction difficulty.
Traditional information and insurance service deployment mode: the credit guarantee insurance business is the organic combination of insurance and credit, and the risk management is the core. The traditional personal credit single pen has small amount and large quantity, needs a large amount of manpower and time investment in the aspect of risk management, and is difficult to achieve the breakthrough of exponential level in effect promotion and cost control.
With the continuous standard development of credit industry, a relatively single isolated wind control means cannot adapt to the current development situation of the industry. The traditional credit insurance business process generally has defense risks to customers, such as forever anti-fraud and forever credit risk investigation, and does not control the whole risk of the life cycle of the customers.
Based on this, the embodiment of the application provides a risk identification monitoring method, a risk identification monitoring device and electronic equipment, and the technical problem that the accuracy of risk identification is low and effective risk control is affected can be solved through the method.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a risk identification monitoring method according to an embodiment of the present application. As shown in fig. 1, the method includes:
and step S110, based on the first customer information, carrying out fraud recognition by using a pre-trained anti-fraud model, and determining a non-fraudulent second customer according to a recognition result.
In the step, a complete intelligent fraud detection and prevention system is formed, and the construction direction of an anti-fraud system is determined. As shown in fig. 2, the anti-fraud model is used to accurately identify the fraudulent client, and provides a strong support for automatic process. The model is combined with manual examination and approval, so that fraudulent customer groups are identified, bad account rate is effectively reduced, and asset quality is improved.
And step S120, based on the behavior information of the second customer, performing risk identification by using a pre-trained application admission scoring model to obtain a risk value of the second customer.
As shown in fig. 2, a series of variables reflecting the default risk are derived based on the basic financial attributes of the customer data and combined with the customer behavior information in the big data database. The grading result has good stability and discrimination, the expected goal of improving the risk identification capability is achieved, the examination and approval cost can be saved, the examination and approval timeliness can be improved, and the purposes of improving the wind control capability and promoting the benign development of the service by applying big data are realized. In the step, a big data wind control model is developed and generated by combining financial attribute information in the industry and applying a model algorithm technology, risk identification and control are carried out on the client, and finally the insurance acceptance limit is given.
And step S130, pre-checking by using a pre-trained credit line model based on the asset information of the third client, and determining the guarantee amount granted to the third client.
It should be noted that the third client is the client whose risk value is smaller than the preset value in the second client.
As shown in fig. 2, the client payment willingness and payment capability are comprehensively considered, so that differentiated credit granting with the client as a core is realized, and an optimal balance point is reached in the aspects of optimizing asset quality, promoting service scale growth and meeting the client fund demand. The credit granting model is adopted to assist manual decision making, so that the approval time is obviously shortened, the influence of artificial subjective factors of an approver is reduced, and the implementation of credit granting strategies is more scientific and efficient.
Step S140, predicting the default risk probability of the fourth customer based on the first insurance application period data of the fourth customer to obtain a first prediction result, and performing early warning according to the first prediction result.
It should be noted that the fourth client is a client that does not have a overdue condition among the third clients.
As shown in fig. 2, predicting the probability of a default risk for a future period of time for a customer who has not yet presented a performance risk is typically used for early warning management decisions for existing customers. Because the customer groups occupy most of the proportion, even if the customers do not have the performance risk, the combination of the model can reduce the labor cost, reduce the rolling indexes such as C-M1 and C-M2 and reduce the loss of companies; meanwhile, a scientific decision basis is provided for early warning personnel.
And S150, predicting the repayment condition of the fifth client by utilizing a pre-trained collection risk model based on the second insurance application period data of the fifth client to obtain a second prediction result, and performing overdue prompt according to the second prediction result.
It should be noted that the fifth client is a client that has an overdue condition among the third clients.
As shown in fig. 2, the client who is overdue is predicted to pay willingness or a payment proportion. Post-guarantee reminders are one of the main means to reduce claims. With the increasing severity of the risk situation, the pressure for postwarranty reminders is increasing, and therefore, optimization by means of analysis is required. By means of the collection risk model, the intelligent case distribution efficiency can be improved, the collection efficiency can be improved, the loss of a company is reduced, and the profitability of the company is improved.
By providing the risk identification monitoring method, a five-level model underwriting system is established, and risks in the whole process of the life cycle of a client are found, early warned and treated early, so that the client can be managed more accurately. The five-level model underwriting based on the big data can apply the big data to the pre-insurance, mid-insurance and post-insurance full credit life cycles of the information and security service. The method is characterized in that a one-stop intelligent wind control closed loop based on data, a model and a platform is established, the one-stop wind control closed loop is suitable for risk control of all links before, during and after the insurance, standardized risk assessment can be objectively carried out, early warning can be tracked, bad accounts can be effectively controlled by flexibly executing a differentiated risk strategy, and a foundation is provided for meeting the compliance requirements of an supervision layer. And a set of complete and comprehensive wind control system which runs through the credit life cycle of the customer is established, and the risk exposure of different types, different periods and different sources is effectively responded, so that the whole risk control of the life cycle of the customer is realized.
In the embodiment of the application, the five-level model underwriting system of the credit insurance business can be realized, and the system scientifically predicts the client risks from three stages of pre-insurance, insurance and post-insurance, and provides a scientific decision basis for systematized and refined management of the client risks. By establishing a one-stop service closed loop based on data, a model and a platform, the intelligent technology is applied to each link of wind control, so that the client can be evaluated and early warned in a standardized and scientific manner, a differential risk strategy can be flexibly executed, and overdue and bad accounts can be effectively controlled.
The above steps are described in detail below.
In some embodiments, the first customer information includes any one or more of: application information, credit investigation information and financial attributes of the first client.
It should be noted that the anti-fraud model adopts the customer application, credit investigation condition and external financial attribute information as input, the credit risk model of the customer is mainly based on credit investigation variables, the big data risk model is established by adopting external three-party data, the big data and the credit investigation variables are combined to establish the model, and the problem of information asymmetry is solved.
In some embodiments, the algorithm of the anti-fraud model includes any one or more of: logistic algorithm, LightGBM algorithm and XGBOOST algorithm.
In the embodiment of the application, Logistic, LightGBM and XGBOOST algorithms are adopted to establish an anti-fraud model, the localization evolution is carried out on the basis of a Logistic regression model, big data is combined, credit investigation records are taken as basic points, dimensions such as customer behavior information, performance capability and common loan information are added, machine algorithm technologies such as Logistic regression and XGBOOST are used for developing and generating a scoring model, the scoring card is continuously adjusted according to actual conditions before and after insurance performance and model monitoring indexes, risks caused by auditing experience are avoided, the approval efficiency is effectively improved, and a basis is provided for risk pricing.
In some embodiments, the behavioral information includes loan information and/or credit investigation dimension information for the second customer. In practical application, credit investigation variables are mainly used for a client credit risk model, a big data risk model is established by adopting external three-party data, and the big data and credit investigation variables are combined to establish the direction of the model, so that the problem of information asymmetry is solved.
In some embodiments, the application admission scoring model is generated by a machine learning algorithm.
In the embodiment of the application, a machine learning algorithm is adopted to generate an application admission scoring model for risk identification and control of a client in an underwriting stage. The model makes up for a short board in the existing service which can only make a decision by referring to application form information and pedestrian credit dimension information, and enriches the risk portrait of the user.
In some embodiments, the first application period data includes historical data and/or behavioral characteristic variables of the fourth customer during the application period.
In some embodiments, the second application period data includes historical data of the fifth customer during the application period, behavioral characteristic variables, commitment and repayment information, and application backlog information.
In practical application, historical data and behavior characteristics of the client during the application period, and information such as the client's promised payment and post-insurance reminding remarks can be used as characteristic variables.
Fig. 3 provides a schematic structural diagram of a risk identification monitoring device. As shown in fig. 3, the risk identification monitoring apparatus 300 includes:
the first identification module 301 is configured to perform fraud identification by using a pre-trained anti-fraud model based on first customer information, and determine a non-fraudulent second customer according to an identification result;
a second identification module 302, configured to perform risk identification by using a pre-trained application admission scoring model based on behavior information of a second customer to obtain a risk value of the second customer;
the pre-checking module 303 is configured to perform pre-checking by using a pre-trained credit line model based on asset information of the third client, and determine a guarantee amount granted to the third client; the third client is the client with the risk value smaller than the preset value in the second client;
the first prediction module 304 is configured to predict the default risk probability of the fourth customer based on the first insurance application period data of the fourth customer, obtain a first prediction result, and perform early warning according to the first prediction result; the fourth client is a client without overdue condition in the third clients;
the second prediction module 305 is configured to predict the repayment situation of the fifth customer by using a pre-trained collection risk model based on second insurance application period data of the fifth customer, obtain a second prediction result, and perform overdue prompting according to the second prediction result; the fifth client is one of the third clients in which the overdue condition occurs.
In some embodiments, the first customer information includes any one or more of:
application information, credit investigation information and financial attributes of the first client.
In some embodiments, the algorithm of the anti-fraud model includes any one or more of:
logistic algorithm, LightGBM algorithm and 3GBOOST algorithm.
In some embodiments, the behavioral information includes loan information and/or credit investigation dimension information for the second customer.
In some embodiments, the application admission scoring model is generated by a machine learning algorithm.
In some embodiments, the first application period data includes historical data and/or behavioral characteristic variables of the fourth customer during the application period.
In some embodiments, the second application period data includes historical data of the fifth customer during the application period, behavioral characteristic variables, commitment and repayment information, and application backlog information.
The risk identification monitoring device provided by the embodiment of the application has the same technical characteristics as the risk identification monitoring method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
As shown in fig. 4, an electronic device 400 provided in an embodiment of the present application includes a memory 401 and a processor 402, where the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method provided in the foregoing embodiment.
Referring to fig. 4, the electronic device further includes: a bus 403 and a communication interface 404, the processor 402, the communication interface 404 and the memory 401 being connected by the bus 403; the processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.
The Memory 401 may include a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 404 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
Bus 403 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The memory 401 is used for storing a program, and the processor 402 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present application may be applied to the processor 402, or implemented by the processor 402.
The processor 402 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 402. The Processor 402 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 and completes the steps of the method in combination with the hardware.
Corresponding to the risk identification monitoring method, the embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores machine executable instructions, and when the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to execute the steps of the risk identification monitoring method.
The risk identification monitoring device provided by the embodiment of the application can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
As another example, in the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the risk identification monitoring method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the scope of the embodiments of the present application. Are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A risk identification monitoring method, the method comprising:
based on the first customer information, carrying out fraud recognition by utilizing a pre-trained anti-fraud model, and determining a non-fraudulent second customer according to a recognition result;
based on the behavior information of the second customer, carrying out risk identification by using a pre-trained application admission scoring model to obtain a risk value of the second customer;
pre-checking by using a pre-trained credit line model based on the asset information of a third client, and determining the guarantee amount granted to the third client; the third client is the client of which the risk value is smaller than a preset value in the second client;
predicting the default risk probability of a fourth customer based on first insurance application period data of the fourth customer to obtain a first prediction result, and performing early warning according to the first prediction result; the fourth client is a client without overdue condition in the third clients;
predicting the repayment condition of a fifth client by utilizing a pre-trained acceptance risk model based on second insurance application period data of the fifth client to obtain a second prediction result, and carrying out overdue prompt according to the second prediction result; the fifth client is a client with overdue condition in the third clients.
2. The method of claim 1, wherein the first customer information comprises any one or more of:
the application information, credit investigation information and financial attributes of the first client.
3. The method of claim 1, wherein the anti-fraud model algorithm comprises any one or more of:
logistic algorithm, LightGBM algorithm and XGBOOST algorithm.
4. The method of claim 1, wherein the behavior information comprises loan information and/or credit investigation dimension information of the second customer.
5. The method of claim 1, wherein the application admission scoring model is generated by a machine learning algorithm.
6. The method of claim 1, wherein the first application period data comprises historical data and/or behavioral characteristic variables of the fourth customer during an application period.
7. The method of claim 1, wherein the second application period data comprises historical data of the fifth customer during the application period, behavioral characteristic variables, commitment and repayment information, and backup note information.
8. A risk identification monitoring device, the device comprising:
the first identification module is used for carrying out fraud identification by utilizing a pre-trained anti-fraud model based on the first customer information and determining a non-fraudulent second customer according to an identification result;
the second identification module is used for carrying out risk identification by utilizing a pre-trained application admission scoring model based on the behavior information of the second customer to obtain a risk value of the second customer;
the pre-checking module is used for pre-checking by utilizing a pre-trained credit line model based on the asset information of a third client and determining the guarantee amount granted to the third client; the third client is the client of which the risk value is smaller than a preset value in the second client;
the first prediction module is used for predicting the default risk probability of a fourth customer based on first insurance application period data of the fourth customer to obtain a first prediction result, and early warning is carried out according to the first prediction result; the fourth client is a client without overdue condition in the third clients;
the second prediction module is used for predicting the repayment condition of a fifth client by using a pre-trained revenue acceleration risk model based on second insurance application period data of the fifth client to obtain a second prediction result, and performing overdue prompt according to the second prediction result; the fifth client is a client with overdue condition in the third clients.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium having stored thereon machine executable instructions which, when invoked and executed by a processor, cause the processor to execute the method of any of claims 1 to 7.
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