CN111369342A - Loan approval method, device, equipment and storage medium based on machine learning - Google Patents
Loan approval method, device, equipment and storage medium based on machine learning Download PDFInfo
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Abstract
The application relates to a loan approval method, a loan approval device, loan approval equipment and a loan approval storage medium based on machine learning. The method comprises the following steps: obtaining loan application information input by a user and equipment information of input equipment currently used by the user, wherein the loan application information comprises loan information and borrower information; when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, acquiring external data of the user, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data; and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score. The method can improve the approval efficiency of the loan and reduce the approval risk brought by the manual loan approval.
Description
Technical Field
The application relates to the field of Internet, in particular to a loan approval method, a loan approval device, loan approval equipment and a loan approval storage medium based on machine learning.
Background
With the vigorous development of real estate, the housing loan business becomes an important expansion business of banks. How to provide the house loan service to the client more efficiently becomes a problem to be solved urgently by those skilled in the art.
The traditional house loan approval mainly adopts an offline application and offline approval mode. The examination and approval mode excessively depends on the subjective judgment of a client manager, and the requirements on the experience and personal quality of the client manager are high; meanwhile, for the client, the time cost of the approval is high, and the timeliness is poor, so that the loan approval efficiency is low.
Disclosure of Invention
In view of the above, it is necessary to provide a loan approval method, device, equipment and storage medium based on machine learning, aiming at the technical problem of low loan approval efficiency of the conventional technology.
In a first aspect, an embodiment of the present application provides a loan approval method based on machine learning, including:
obtaining loan application information input by a user and equipment information of input equipment currently used by the user, wherein the loan application information comprises loan information and borrower information;
when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, acquiring external data of the user, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score.
In a second aspect, an embodiment of the present application provides a loan approval apparatus based on machine learning, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring loan application information input by a user and equipment information of input equipment currently used by the user, and the loan application information comprises loan information and borrower information;
the second obtaining module is used for obtaining external data of the user when the repayment probability of the user is determined to be larger than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and the processing module is used for determining the grade of the user according to the loan application information, the external data and a preset grade rule and outputting an approval result according to the grade.
In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor, when executing the computer program, implements the method for approving a loan provided by the first aspect of the embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, implementing a method for approving a loan provided by the first aspect of the embodiments of the present application.
According to the loan approval method, the loan approval device, the loan approval equipment and the loan approval storage medium based on machine learning, provided by the embodiment of the application, the computer equipment acquires loan application information input by a user and equipment information of input equipment currently used by the user; when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model in the loan application information, acquiring external data of the user; and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score. In the whole loan approval process, the computer equipment can automatically determine repayment probability of the user through a machine learning model, namely a risk early warning model, based on loan application information input by the user, equipment information input by the user and historical transaction behavior information of the user, and when the repayment probability of the user is greater than a first threshold value, determine the grade of the user through a preset grade rule based on the acquired external data of the user and the loan application information of the user, and output an approval result according to the determined grade. Namely, the whole loan approval process is automatically completed by the computer equipment, and the client manager is not required to perform offline approval based on experience, so that the loan approval efficiency is improved, and the approval risk caused by manual loan approval is reduced.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for machine learning-based loan approval according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating an alternative method for machine learning-based loan approval according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an information relationship network according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a machine learning-based loan approval apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application are further described in detail by the following embodiments in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that the executing entity of the method embodiments described below may be a machine learning-based loan approval apparatus, which may be implemented as part or all of a computer device in software, hardware, or a combination of software and hardware. The method embodiments described below are described by way of example with the execution subject being a computer device.
Fig. 1 is a schematic flow chart of a loan approval method based on machine learning according to an embodiment of the present disclosure. This embodiment relates to a specific process of how a computer device automatically approves a loan for a user. As shown in fig. 1, the method may include:
s101, obtaining loan application information input by a user and equipment information of input equipment currently used by the user.
Specifically, the user may input the loan application information through input means such as client software of a bank, an applet, a public number, and the like, so that the computer device obtains the loan application information input by the user. The loan application information may include loan information and borrower information, and the borrower information may include principal lender information and participating lender information. Meanwhile, the computer device also needs to collect device information of an input device currently used by a user, where the device information may include location information of the device, a Media Access Control (MAC) address of the device, an Internet Protocol (IP) address, and the like, and when the input device is a Mobile device, the device information may further include an International Mobile Subscriber Identity (IMSI), an International Mobile Equipment Identity (IMEI), a device fingerprint, a Mobile phone Number, and the like.
S102, when the repayment probability of the user is determined to be larger than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, obtaining external data of the user.
Specifically, the computer device obtains the historical transaction behavior information of the user according to the identifier of the user. The historical transaction behavior information can be historical loan behavior information, historical consumption behavior information, social data, e-commerce data and the like. And inputting the obtained information of the borrower, the equipment and the historical transaction behavior information of the user into a risk early warning model by the computer equipment to obtain the repayment probability of the user. The risk early warning model is a machine learning model, the risk factor of the user can be predicted through the early warning model, and the repayment probability of the user is determined based on the predicted risk factor.
And when the repayment probability of the user is determined to be larger than a preset first threshold, acquiring external data of the user. The external data is data which is not pre-stored, namely data acquired through an external channel. For example, credit investigation data, accumulation fund data, social security data of a person and related data of the user acquired through an industry and commerce channel and a customs channel.
When the repayment probability of the user is determined to be less than or equal to the first threshold, the computer device outputs a withholding result to the user.
S103, determining the grade of the user according to the loan application information, the external data and a preset grade rule, and outputting an approval result according to the grade.
Specifically, the external data may include first income data of the user, the loan application information may include second income data of the user, the first income data is income data acquired by the computer device according to the external data of the user, and the second income data is income data input by the user, so that the computer device needs to respectively judge whether the first income data and the second income data match the target loan amount. Optionally, in the step S103, the process of determining the score of the user according to the loan application information, the external data, and a preset scoring rule may be: respectively judging whether the first income data and the second income data in the loan application information are matched with a target loan amount according to a preset mapping relation; and if at least one income data is matched, determining the grade of the user through a preset grade rule according to credit investigation data in the external data.
The mapping relationship comprises corresponding relationships between different income data and loan amounts. When the computer equipment determines that at least one income data is matched, the computer equipment determines the grade of the user according to credit investigation data in the external data and preset exclusion policy, calculation standard, hard policy, special screening policy and screening policy, and outputs an approval result based on the determined grade.
Optionally, if the first income data and the second income data are not matched, outputting a refusal result; and if the first income data is not matched and the second income data is matched, outputting first prompt information, wherein the first prompt information is used for indicating that income certification needs to be provided.
After obtaining the user's score, the computer device may output an approval result based on the obtained score. Optionally, when the score of the user is greater than or equal to a preset second threshold, the computer device outputs a first approval result, where the first approval result is used to represent that the loan approval passes; and when the score of the user is smaller than the second threshold value, outputting a second approval result by the computer equipment, wherein the second approval result is used for indicating the approval of the loan through the offline approval process.
According to the loan approval method based on machine learning provided by the embodiment of the application, the computer equipment acquires loan application information input by a user and equipment information of input equipment currently used by the user; when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model in the loan application information, acquiring external data of the user; and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score. In the whole loan approval process, the computer equipment can automatically determine repayment probability of the user through a machine learning model, namely a risk early warning model, based on loan application information input by the user, equipment information input by the user and historical transaction behavior information of the user, and when the repayment probability of the user is greater than a first threshold value, determine the grade of the user through a preset grade rule based on the acquired external data of the user and the loan application information of the user, and output an approval result according to the determined grade. Namely, the whole loan approval process is automatically completed by the computer equipment, and the client manager is not required to perform offline approval based on experience, so that the loan approval efficiency is improved, and the approval risk caused by manual loan approval is reduced.
In practical application, the loan application information may further include house information of a house to be purchased, and in order to further reduce the loan approval risk, to provide the user with a credit amount matched with the loan approval risk, optionally, the process of outputting the approval result according to the score by the computer device may further be: when the score is larger than or equal to the second threshold value, determining the house property of the house to be purchased according to the house information; when the house to be purchased is a second-hand house, judging whether the purchase price of the house to be purchased is matched with the market price; if so, outputting the first approval result; and if not, outputting second prompt information, wherein the first approval result is used for representing that the loan approval is passed, and the second prompt information is used for indicating that a house evaluation report of the house to be purchased needs to be provided.
Fig. 2 is a schematic flow chart of another method for machine learning-based loan approval according to an embodiment of the present disclosure. The embodiment relates to a specific process of how a computer device determines the repayment probability of a user based on a risk early warning model. On the basis of the foregoing embodiment, optionally, as shown in fig. 2, the step of determining the repayment probability of the user according to the borrower information, the device information, the historical transaction behavior information of the user, and the preset risk early warning model in S102 may be:
s201, constructing an information relation network of the user according to the information of the borrower, the equipment information and the historical transaction behavior information of the user.
Specifically, the computer device converts the borrower information, the input device information and the user's historical transaction behavior information in the obtained loan application information into graph data by using unstructured data extraction technologies such as texts and graphs, processes the graph data, screens out nodes and edges required for constructing the information relationship network, and constructs the information relationship network of the user according to the nodes and the edges. Wherein a node refers to the raw data associated with the user. For example, the node is an account number of the user, an equipment identifier (such as an equipment MAC address, an IP address, an IMEI, and an IMSI of a mobile phone card) of an input device currently used by the user, a mobile phone number, an identification number, current location information, a home address, a company address, and the like. The edge refers to the association relationship between nodes, such as whether the user owns an account number, whether the user uses an input device, whether the user uses a mobile phone card, whether a home address or a work address matches current location information, whether an application mobile phone number matches a bank card account opening mobile phone number when applying for loan, and whether resources in one account number are transferred to another account number. In this way, the computer device can construct an information relationship network as shown in FIG. 3. The client id (identification) in fig. 3 is a user identifier, and a Global Positioning System (GPS) address is current location information of the user.
S202, extracting risk characteristic information corresponding to the user from the information relation network.
Specifically, the computer device adopts a dual-mode network feature extraction framework to extract risk feature information corresponding to the user from the information relationship network. The dual-mode network feature extraction framework can comprise a self-centering network risk feature family, a local network clustering coefficient risk feature family, a global network risk feature family, a network information consistency risk feature family and a quadrangle risk feature family. The computer equipment can extract the number of input equipment used by a user and the number of people commonly used by the input equipment within a preset time through a self-center network risk feature family, can extract transaction times existing between the user and other users through a local network clustering coefficient risk feature family, can extract the relative importance degree, the center degree and the like of the user in a transaction network through a global network risk feature family, can extract whether the mobile phone number of the user is a common mobile phone number or not through a network information consistency risk feature family, the times of current position information deviating a preset distance from a home address and the like, and can extract the number of common account numbers of the input equipment and the times of loan co-application of the user at the current position within a preset time (such as within 1 day) through a quadrangle risk feature family.
And S203, respectively inputting the risk characteristic information into a plurality of preset risk early warning models to obtain corresponding output results.
Specifically, different risk early warning models are used for predicting different repayment risks of the user, and the repayment risks include any one of overdue probability, repayment capacity and repayment willingness. And respectively inputting the obtained risk characteristic information into a plurality of risk early warning models by the computer equipment to obtain a plurality of output results, namely a plurality of repayment risks.
Optionally, the multiple risk early warning models may include deep learning models such as a clustering algorithm, a document theme generation model, a natural language processing model, a long-term and short-term memory model, and the like. Each risk early warning model is obtained through training of a large amount of historical training data.
And S204, performing weighted calculation on all output results to obtain the repayment probability of the user.
Specifically, different output results have corresponding weight values, so that the computer equipment can perform weighted calculation on all output results according to the output results and the weight values corresponding to the output results to obtain the repayment probability of the user.
In this embodiment, the computer device may construct an information relationship network of the user based on the obtained borrower information, the device information of the input device, and the historical transaction behavior information of the user, extract risk feature information corresponding to the user from the information relationship network, and input the risk feature information into a plurality of risk early warning models to obtain each repayment risk representing the user, and determine the repayment probability of the user based on each repayment risk. Through the information relation network, the real information of the user can be comprehensively and deeply known, so that the obtained risk characteristic information is more comprehensive and accurate, the accuracy of the repayment risk predicted through the risk early warning model is improved, the accuracy of the calculated repayment probability is improved, and the risk of loan approval is reduced.
Optionally, after obtaining the information relationship network of the user, the computer device may further analyze the information relationship network of the user, determine a target risk identifier, and add the target risk identifier to a blacklist.
Specifically, the computer device determines the target risk identifier by analyzing the information relationship network, that is, by analyzing the similarity and the centrality between nodes in the information relationship network. The target risk identification can be a risky IP address, a risky area range, a risky device number and the like. The computer device adds the risky IP addresses, the risky area range and the risky device numbers to the existing blacklist to supplement the existing blacklist information, so that the information in the blacklist is more complete, and the computer device can pre-approve the loan application user based on the blacklist in the next loan approval process, thereby further reducing the risk of loan approval.
Fig. 4 is a schematic structural diagram of a loan approval apparatus based on machine learning according to an embodiment of the present application. As shown in fig. 4, the apparatus may include: a first acquisition module 10, a second acquisition module 11 and a processing module 12.
Specifically, the first obtaining module 10 is configured to obtain loan application information input by a user and device information of an input device currently used by the user, where the loan application information includes loan information and borrower information;
the second obtaining module 11 is configured to obtain external data of the user when it is determined that the repayment probability of the user is greater than a preset first threshold according to the borrower information, the device information, the historical transaction behavior information of the user, and a preset risk early warning model, where the risk early warning model is a machine learning model, and the external data is non-pre-stored data;
the processing module 12 is configured to determine a score of the user according to the loan application information, the external data, and a preset scoring rule, and output an approval result according to the score.
According to the loan approval device based on machine learning provided by the embodiment of the application, the computer equipment acquires loan application information input by a user and equipment information of input equipment currently used by the user; when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model in the loan application information, acquiring external data of the user; and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score. In the whole loan approval process, the computer equipment can automatically determine repayment probability of the user through a machine learning model, namely a risk early warning model, based on loan application information input by the user, equipment information input by the user and historical transaction behavior information of the user, and when the repayment probability of the user is greater than a first threshold value, determine the grade of the user through a preset grade rule based on the acquired external data of the user and the loan application information of the user, and output an approval result according to the determined grade. Namely, the whole loan approval process is automatically completed by the computer equipment, and the client manager is not required to perform offline approval based on experience, so that the loan approval efficiency is improved, and the approval risk caused by manual loan approval is reduced.
On the basis of the foregoing embodiment, optionally, the apparatus may further include a determining module, where the determining module may include: the device comprises a construction unit, an extraction unit, a prediction unit and a determination unit;
specifically, the construction unit is configured to construct an information relationship network of the user according to the borrower information, the device information, and the historical transaction behavior information of the user;
the extracting unit is used for extracting risk characteristic information corresponding to the user from the information relation network;
the prediction unit is used for respectively inputting the risk characteristic information into a plurality of preset risk early warning models to obtain corresponding output results, wherein different risk early warning models are used for predicting different repayment risks of the user, and the repayment risks comprise any one of overdue probability, repayment capacity and repayment willingness;
and the determining unit is used for performing weighted calculation on all output results to obtain the repayment probability of the user.
On the basis of the above embodiment, optionally, the determining module may further include a processing unit;
specifically, the processing unit is configured to analyze the information relationship network of the user, determine a target risk identifier, and add the target risk identifier to a blacklist.
On the basis of the foregoing embodiment, optionally, the external data includes first income data of the user, and the processing module 12 is specifically configured to respectively determine, according to a preset mapping relationship, whether the first income data and second income data in the loan application information match a target loan amount; and if at least one income data is matched, determining the grade of the user through a preset grade rule according to credit investigation data in the external data, wherein the mapping relation comprises the corresponding relation between different income data and loan amount.
On the basis of the foregoing embodiment, optionally, the processing module 12 is further configured to output a withholding result if the first income data and the second income data are not matched with each other; and if the first income data is not matched and the second income data is matched, outputting first prompt information, wherein the first prompt information is used for indicating that income certification needs to be provided.
On the basis of the foregoing embodiment, optionally, the processing module 12 is further configured to output a first approval result when the score is greater than or equal to a preset second threshold, where the first approval result is used to represent that the loan approval is passed; and outputting a second approval result when the score is smaller than the second threshold, wherein the second approval result is used for indicating the approval of the loan through the approval process under the line.
On the basis of the foregoing embodiment, optionally, the loan application information further includes house information of a house to be purchased, and the processing module 12 is further configured to determine, when the score is greater than or equal to the second threshold, a house property of the house to be purchased according to the house information; when the house to be purchased is a second-hand house, judging whether the purchase price of the house to be purchased is matched with the market price; if so, outputting the first approval result; and if not, outputting second prompt information, wherein the second prompt information is used for indicating that a house evaluation report of the house to be purchased needs to be provided.
In one embodiment, a computer device is provided, a schematic structural diagram of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store data during the machine learning based loan approval process. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a machine learning-based loan approval method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, the computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:
obtaining loan application information input by a user and equipment information of input equipment currently used by the user, wherein the loan application information comprises loan information and borrower information;
when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, acquiring external data of the user, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score.
In one embodiment, the processor, when executing the computer program, further performs the steps of: constructing an information relationship network of the user according to the borrower information, the equipment information and the historical transaction behavior information of the user; extracting risk characteristic information corresponding to the user from the information relation network; respectively inputting the risk characteristic information into a plurality of preset risk early warning models to obtain corresponding output results, wherein different risk early warning models are used for predicting different repayment risks of the user, and the repayment risks comprise any one of overdue probability, repayment capacity and repayment willingness; and performing weighted calculation on all output results to obtain the repayment probability of the user.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and analyzing the information relation network of the user, determining a target risk identifier, and adding the target risk identifier to a blacklist.
In one embodiment, the external data comprises first revenue data for the user, the processor when executing the computer program further performing the steps of: respectively judging whether the first income data and the second income data in the loan application information are matched with a target loan amount or not according to a preset mapping relation, wherein the mapping relation comprises corresponding relations between different income data and loan amounts; and if at least one income data is matched, determining the grade of the user through a preset grade rule according to credit investigation data in the external data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: if the first income data and the second income data are not matched, outputting a refusal result; and if the first income data is not matched and the second income data is matched, outputting first prompt information, wherein the first prompt information is used for indicating that income certification needs to be provided.
In one embodiment, the processor, when executing the computer program, further performs the steps of: when the score is larger than or equal to a preset second threshold value, outputting a first approval result, wherein the first approval result is used for representing that the loan approval is passed; and outputting a second approval result when the score is smaller than the second threshold, wherein the second approval result is used for indicating the approval of the loan through the approval process under the line.
In one embodiment, the loan application information further comprises information about a house to be purchased, and the processor when executing the computer program further performs the steps of: when the score is larger than or equal to the second threshold value, determining the house property of the house to be purchased according to the house information; when the house to be purchased is a second-hand house, judging whether the purchase price of the house to be purchased is matched with the market price; if so, outputting the first approval result; and if not, outputting second prompt information, wherein the second prompt information is used for indicating that a house evaluation report of the house to be purchased needs to be provided.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
obtaining loan application information input by a user and equipment information of input equipment currently used by the user, wherein the loan application information comprises loan information and borrower information;
when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, acquiring external data of the user, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing an information relationship network of the user according to the borrower information, the equipment information and the historical transaction behavior information of the user; extracting risk characteristic information corresponding to the user from the information relation network; respectively inputting the risk characteristic information into a plurality of preset risk early warning models to obtain corresponding output results, wherein different risk early warning models are used for predicting different repayment risks of the user, and the repayment risks comprise any one of overdue probability, repayment capacity and repayment willingness; and performing weighted calculation on all output results to obtain the repayment probability of the user.
In one embodiment, the computer program when executed by the processor further performs the steps of: and analyzing the information relation network of the user, determining a target risk identifier, and adding the target risk identifier to a blacklist.
In one embodiment, the external data comprises first revenue data of the user, the computer program when executed by the processor further implementing the steps of: respectively judging whether the first income data and the second income data in the loan application information are matched with a target loan amount or not according to a preset mapping relation, wherein the mapping relation comprises corresponding relations between different income data and loan amounts; and if at least one income data is matched, determining the grade of the user through a preset grade rule according to credit investigation data in the external data.
In one embodiment, the computer program when executed by the processor further performs the steps of: if the first income data and the second income data are not matched, outputting a refusal result; and if the first income data is not matched and the second income data is matched, outputting first prompt information, wherein the first prompt information is used for indicating that income certification needs to be provided.
In one embodiment, the computer program when executed by the processor further performs the steps of: when the score is larger than or equal to a preset second threshold value, outputting a first approval result, wherein the first approval result is used for representing that the loan approval is passed; and outputting a second approval result when the score is smaller than the second threshold, wherein the second approval result is used for indicating the approval of the loan through the approval process under the line.
In one embodiment, the loan application information further comprises house information of a house to be purchased, the computer program when executed by the processor further implementing the steps of: when the score is larger than or equal to the second threshold value, determining the house property of the house to be purchased according to the house information; when the house to be purchased is a second-hand house, judging whether the purchase price of the house to be purchased is matched with the market price; if so, outputting the first approval result; and if not, outputting second prompt information, wherein the second prompt information is used for indicating that a house evaluation report of the house to be purchased needs to be provided.
The loan approval device, the computer device and the storage medium based on machine learning provided in the above embodiments may execute the loan approval method based on machine learning provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the above embodiments, reference may be made to the machine learning-based loan approval method provided in any of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A loan approval method based on machine learning is characterized by comprising the following steps:
obtaining loan application information input by a user and equipment information of input equipment currently used by the user, wherein the loan application information comprises loan information and borrower information;
when determining that the repayment probability of the user is greater than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, acquiring external data of the user, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and determining the score of the user according to the loan application information, the external data and a preset scoring rule, and outputting an approval result according to the score.
2. The method of claim 1, wherein the determining the repayment probability of the user according to the borrower information, the device information, the historical transaction behavior information of the user and a preset risk early warning model comprises:
constructing an information relationship network of the user according to the borrower information, the equipment information and the historical transaction behavior information of the user;
extracting risk characteristic information corresponding to the user from the information relation network;
respectively inputting the risk characteristic information into a plurality of preset risk early warning models to obtain corresponding output results, wherein different risk early warning models are used for predicting different repayment risks of the user, and the repayment risks comprise any one of overdue probability, repayment capacity and repayment willingness;
and performing weighted calculation on all output results to obtain the repayment probability of the user.
3. The method of claim 2, further comprising:
and analyzing the information relation network of the user, determining a target risk identifier, and adding the target risk identifier to a blacklist.
4. The method of claim 2, wherein the external data comprises first revenue data of the user, and wherein determining the user's score based on the loan application information, the external data, and preset scoring rules comprises:
respectively judging whether the first income data and the second income data in the loan application information are matched with a target loan amount or not according to a preset mapping relation, wherein the mapping relation comprises corresponding relations between different income data and loan amounts;
and if at least one income data is matched, determining the grade of the user through a preset grade rule according to credit investigation data in the external data.
5. The method of claim 4, further comprising:
if the first income data and the second income data are not matched, outputting a refusal result;
and if the first income data is not matched and the second income data is matched, outputting first prompt information, wherein the first prompt information is used for indicating that income certification needs to be provided.
6. The method according to any one of claims 1 to 5, wherein the outputting of the approval result according to the score comprises:
when the score is larger than or equal to a preset second threshold value, outputting a first approval result, wherein the first approval result is used for representing that the loan approval is passed;
and outputting a second approval result when the score is smaller than the second threshold, wherein the second approval result is used for indicating the approval of the loan through the approval process under the line.
7. The method of claim 6, wherein the loan application information further includes information on a house of the house to be purchased, the method further comprising:
when the score is larger than or equal to the second threshold value, determining the house property of the house to be purchased according to the house information;
when the house to be purchased is a second-hand house, judging whether the purchase price of the house to be purchased is matched with the market price;
if so, outputting the first approval result;
and if not, outputting second prompt information, wherein the second prompt information is used for indicating that a house evaluation report of the house to be purchased needs to be provided.
8. A loan approval apparatus based on machine learning, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring loan application information input by a user and equipment information of input equipment currently used by the user, and the loan application information comprises loan information and borrower information;
the second obtaining module is used for obtaining external data of the user when the repayment probability of the user is determined to be larger than a preset first threshold value according to the borrower information, the equipment information, the historical transaction behavior information of the user and a preset risk early warning model, wherein the risk early warning model is a machine learning model, and the external data is non-prestored data;
and the processing module is used for determining the grade of the user according to the loan application information, the external data and a preset grade rule and outputting an approval result according to the grade.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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