CN116664278A - Information generation method, device, equipment and storage medium - Google Patents
Information generation method, device, equipment and storage medium Download PDFInfo
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- CN116664278A CN116664278A CN202310639566.8A CN202310639566A CN116664278A CN 116664278 A CN116664278 A CN 116664278A CN 202310639566 A CN202310639566 A CN 202310639566A CN 116664278 A CN116664278 A CN 116664278A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Abstract
The disclosure provides an information generation method, an information generation device and a storage medium, which can be applied to the technical field of artificial intelligence and the technical field of financial science and technology. The method comprises the following steps: responding to a received loan overdue risk assessment request of a target object, and acquiring historical transaction information of the target object and credit state information of the target object in a preset period; processing the historical transaction information to obtain historical transaction fluctuation information in a preset period; and generating the overdue risk assessment result information of the loan of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using the overdue risk assessment model of the loan.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technology and the field of financial technology, and in particular, to an information generating method, apparatus, device, medium, and program product.
Background
In financial services, the overdue house is subject to loss of default, and therefore the risk of overdue house needs to be evaluated to take corresponding strategies to reduce the loss.
In the related art, user attribute information such as employment status and cultural degree of a user and fund balance information of the user are generally obtained and processed to obtain an evaluation result of the overdue risk of a house credit, however, the risk evaluation dimension adopting the method has low fitting degree, and the accuracy of the obtained overdue risk evaluation result of the house credit is low.
Disclosure of Invention
In view of the foregoing, the present disclosure provides information generation methods, apparatuses, devices, media, and program products.
According to a first aspect of the present disclosure, there is provided an information generating method including: responding to a received loan overdue risk assessment request of a target object, and acquiring historical transaction information of the target object in a preset period and credit state information of the target object;
processing the historical transaction information to obtain historical transaction fluctuation information in the preset period; and
and generating loan overdue risk assessment result information of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using a loan overdue risk assessment model.
According to an embodiment of the disclosure, the generating, using a loan overdue risk assessment model, loan overdue risk assessment result information of the target object according to the historical transaction fluctuation information and credit status information of the target object includes:
extracting historical transaction fluctuation features from the historical transaction fluctuation information;
extracting credit status characteristics from the credit status information;
splicing the historical transaction fluctuation feature and the credit status feature to obtain a combined feature; and
and processing the combined characteristics by using the loan overdue risk assessment model to obtain the loan overdue risk assessment result information.
According to an embodiment of the present disclosure, the extracting the historical transaction volatility feature from the historical transaction volatility information includes:
obtaining target transaction fluctuation information from the historical transaction fluctuation information based on an attention mechanism, wherein the target transaction fluctuation information represents the transaction fluctuation information with the influence probability on the loan overdue risk assessment result being greater than a preset threshold value; and
and extracting the historical transaction fluctuation feature from the target transaction fluctuation information.
According to an embodiment of the present disclosure, the extracting the credit status feature from the credit status information includes:
determining the weight characteristics of the credit status information according to a preset rule;
coding the credit status information to obtain coding characteristics; and
and splicing the coding features and the weight features to obtain the credit status features.
According to an embodiment of the disclosure, the generating, using a loan overdue risk assessment model, loan overdue risk assessment result information of the target object according to the historical transaction fluctuation information and credit status information of the target object includes:
determining the weight of historical transaction fluctuation information and the weight of credit state information; and
and obtaining the overdue risk assessment result information of the loan according to the historical transaction fluctuation information, the credit state information of the target object, the weight of the historical transaction fluctuation information and the weight of the credit state information by using the overdue risk assessment model of the loan.
According to an embodiment of the disclosure, the obtaining the loan overdue risk assessment result information according to the historical transaction fluctuation information, the credit status information of the target object, the weight of the historical transaction fluctuation information, and the weight of the credit status information by using the loan overdue risk assessment model includes:
processing the historical transaction fluctuation information by using the loan overdue risk assessment model to obtain a first assessment result;
processing the weight of the credit state information by using the loan overdue risk assessment model to obtain a second assessment result; and
and obtaining the overdue risk assessment result information of the loan according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit state information.
According to an embodiment of the disclosure, the training method of the loan overdue risk assessment result model includes:
acquiring sample historical transaction information of a sample object in a preset period and sample credit state information of the sample object;
processing the sample historical transaction information to obtain sample historical transaction fluctuation information in the preset period;
obtaining a sample loan overdue risk assessment result of the sample object by using the initial model to the sample historical transaction fluctuation information and the sample credit state information;
obtaining a loss value based on the loss function according to the sample loan overdue risk assessment result and the sample object loan overdue risk assessment label; and
and adjusting model parameters of the initial model based on the loss value to obtain the loan overdue risk assessment result model.
A second aspect of the present disclosure provides an information generating apparatus including: and the acquisition module is used for responding to the received loan overdue risk assessment request of the target object and acquiring the historical transaction information of the target object in a preset period and the credit state information of the target object. The processing module is used for processing the historical transaction information to obtain historical transaction fluctuation information in the preset period; and the evaluation module is used for generating the loan overdue risk evaluation result information of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using a loan overdue risk evaluation model.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.
A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.
According to the information generation method, the device, the equipment, the medium and the program product, the historical transaction information of the target object in a preset period is obtained and processed by responding to the received loan overdue risk assessment request of the target object, the historical transaction fluctuation information is obtained, the credit state information is obtained, and the loan overdue risk assessment result information of the target object is generated according to the historical transaction fluctuation information and the credit state information based on the loan overdue risk assessment model. The loan overdue risk assessment result information is generated by utilizing the loan overdue risk assessment model according to the historical transaction fluctuation information and the credit state information, and comprehensive risk assessment can be performed, so that the problem that the degree of fit of the risk assessment dimension is low is at least partially solved, and the technical effect of improving the accuracy of the house loan overdue risk assessment result is achieved.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of an information generation method, apparatus, device, medium and program product according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of an information generation method according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a flowchart of generating loan overdue risk assessment result information for a target object, in accordance with an embodiment of the disclosure;
FIG. 4 schematically illustrates a flowchart of generating loan overdue risk assessment result information for a target object, in accordance with an embodiment of the disclosure;
FIG. 5 schematically illustrates a flowchart of a training method of a loan overdue risk assessment result model, in accordance with an embodiment of the disclosure;
fig. 6 schematically shows a block diagram of a structure of an information generating apparatus according to an embodiment of the present disclosure; and
fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement the information generating method according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical scheme of the disclosure, the related data (such as including but not limited to personal information of a user) are collected, stored, used, processed, transmitted, provided, disclosed, applied and the like, all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.
In financial services, default loss is generated when a house credit is overdue, and a great amount of resources are needed to be put into construction of a collection management system. As the last ring of the risk control management flow, the collect management system not only can directly reduce the loss of default generated by overdue house credits, but also can effectively manage target objects.
The related real-time risk assessment is usually to acquire and process the user attribute information such as employment status and cultural degree of the user and the fund balance information of the user so as to acquire the assessment result of real-time risk of the real-time credit, however, the risk assessment dimension fit degree in the way is low, and the acquired real-time risk assessment result of the real-time credit is low in accuracy.
The embodiment of the disclosure provides an information generation method, which comprises the steps of responding to a received loan overdue risk assessment request of a target object, and acquiring historical transaction information of the target object in a preset period and credit state information of the target object; processing the historical transaction information to obtain historical transaction fluctuation information in the preset period; and generating loan overdue risk assessment result information of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using a loan overdue risk assessment model.
Fig. 1 schematically illustrates an application scenario diagram of an information generation method according to an embodiment of the present disclosure.
As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that, the information generating method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the information generating apparatus provided by the embodiments of the present disclosure may be generally provided in the server 105. The information generating method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the information generating apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The information generation method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 4 based on the scenario described in fig. 1.
Fig. 2 schematically shows a flowchart of an information generation method according to an embodiment of the present disclosure.
As shown in fig. 2, the information generating method of this embodiment includes operations S210 to S230.
In operation S210, in response to the received loan overdue risk assessment request of the target object, historical transaction information of the target object and credit status information of the target object within a preset period are acquired.
According to embodiments of the present disclosure, a target object characterizes a user who needs to pay for a specified period of time. The historical transaction information of the target object is obtained, and the historical transaction information in a preset period is screened, wherein the preset period can be three months, six months, nine months or twelve months, and is not limited herein. The historical transaction information characterizes deposit balance increment of the target object in a preset period, daily financial assets, fund balance, monthly outflow count and the like.
The credit status information may be credit score, overdue balance, overdue days, loan organization number, etc. of the target object, which characterizes the credit degree of the target object.
In operation S220, the historical transaction information is processed to obtain historical transaction fluctuation information within a preset period.
According to an embodiment of the present disclosure, the historical transaction fluctuations characterize the historical transaction fluctuations of the target object over a preset period. The historical transaction fluctuation information may be obtained by processing the deposit balance in a preset period to obtain a month average amplification percentage of the deposit balance in the preset period, for example, a month average amplification percentage of one month to three months, a month average amplification percentage of four months to six months and a month average amplification percentage of seven months to nine months, thereby obtaining fluctuation information of the deposit balance. The historical transaction fluctuation information can also be historical transaction fluctuation information of the number of the outflow strokes in a preset period, and the like.
In operation S230, the loan overdue risk assessment result information of the target object is generated according to the historical transaction fluctuation information and the credit status information of the target object by using the loan overdue risk assessment model.
According to the embodiment of the disclosure, historical transaction fluctuation information and the credit state of the target object are input into a loan overdue risk assessment model, and loan overdue risk assessment result information of the target object is generated.
According to the embodiment of the disclosure, historical transaction information of a target object in a preset period is obtained and processed by responding to a received loan overdue risk assessment request of the target object, historical transaction fluctuation information is obtained, credit state information is obtained, and loan overdue risk assessment result information of the target object is generated according to the historical transaction fluctuation information and the credit state information based on a loan overdue risk assessment model. The loan overdue risk assessment result information is generated by utilizing the loan overdue risk assessment model according to the historical transaction fluctuation information and the credit state information, so that comprehensive risk assessment can be performed, the fit degree of the risk assessment dimension is improved, and the accuracy of the house loan overdue risk assessment result is improved.
Fig. 3 schematically illustrates a flowchart of generating loan overdue risk assessment result information for a target object, according to an embodiment of the disclosure.
As shown in fig. 3, the information generating method of this embodiment includes operations S310 to S340.
In operation S310, historical transaction volatility characteristics are extracted from the historical transaction volatility information.
According to embodiments of the present disclosure, the historical transaction volatility information includes a plurality of information attributes, such as a month average percentage increase of the deposit balance, an outgoing number of strokes, and the like. For the historical transaction fluctuation information, each information attribute can be directly used as a sub-box, and the historical transaction fluctuation characteristics are extracted; or dividing the data into multiple groups according to the frequency average to form a preliminary group, combining similar groups on the basis of the preliminary group to form a final group, and extracting the fluctuation characteristics of the historical transaction.
In operation S320, a credit status feature is extracted from the credit status information.
According to embodiments of the present disclosure, credit status information includes a plurality of information attributes, such as credit score, expiration date, and number of loan institutions, etc. For credit status information, each information attribute can be directly used as a sub-box, and historical transaction fluctuation characteristics are extracted; or dividing the data into multiple groups according to the frequency average to form a preliminary group, combining similar groups on the basis of the preliminary group to form a final group, and extracting the credit status characteristics.
In operation S330, the historical transaction volatility feature and the credit status feature are spliced to obtain a combination feature.
In operation S340, the combination feature is processed by using the loan overdue risk assessment model to obtain the information of the loan overdue risk assessment result.
According to the embodiment of the disclosure, the historical transaction fluctuation feature and the credit state feature are extracted and spliced to obtain the combined feature, the loan overdue risk assessment model is utilized to process the combined feature to obtain the loan overdue risk assessment result information, comprehensive risk assessment can be performed, the fitting degree of the risk assessment dimension is improved, and the accuracy of the house loan overdue risk assessment result is improved.
According to an embodiment of the present disclosure, extracting historical transaction volatility characteristics from the historical transaction volatility information in operation S310 includes obtaining target transaction volatility information from the historical transaction volatility information based on an attention mechanism, wherein the target transaction volatility information characterizes transaction volatility information having an influence probability on a loan overdue risk assessment result greater than a predetermined threshold; historical transaction volatility characteristics are extracted from the target transaction volatility information.
According to the embodiment of the disclosure, for the historical transaction fluctuation information with different information attributes such as deposit balance increment, daily financial property, fund balance, monthly outflow number and the like, the value of the historical transaction fluctuation information is determined according to the influence probability on the overdue risk assessment result of the loan. For example, the increment of the deposit balance is 0.6, the daily average financial asset is 0.3, the fund balance is 0.8, the average number of outflow in month is 0.4, and the other is 0.1. Under the condition that the preset threshold value is 0.2, the deposit balance increment is 0.6, the influence probability of historical transaction fluctuation information of daily average financial assets 0.3, fund balance 0.8 and month average outflow count 0.4 is larger than the preset threshold value, target transaction fluctuation information is obtained, and then historical transaction fluctuation characteristics are extracted from the target transaction fluctuation information.
According to the embodiment of the disclosure, the target transaction fluctuation information represents the transaction fluctuation information with the influence probability of the loan overdue risk assessment result being larger than the preset threshold value, so that the historical transaction fluctuation characteristics with the large influence probability of the loan overdue risk assessment result can be extracted, the fit degree of the risk assessment dimension is improved, and the accuracy of the house-loan overdue risk assessment result is improved.
In accordance with an embodiment of the present disclosure, extracting a credit status feature from credit status information includes: determining weight characteristics of credit state information according to a preset rule; encoding the credit status information to obtain encoding characteristics; and splicing the coding features and the weight features to obtain credit status features.
According to embodiments of the present disclosure, weight characteristics of credit status information are determined for credit status information of different information attributes such as credit score, overdue days, and loan institution number. For example, the weight characteristics of credit score, expiration date, and loan agency number are 0.6, 0.3, and 0.1, respectively.
The credit status information is encoded, for example, the credit score is encoded with "good" code being 1, the credit score is encoded with "good" code being 0, and the credit score is encoded with "bad" code being-1, so as to obtain the encoding characteristics of the credit score.
According to the embodiment of the disclosure, the encoding features and the weight features are spliced to obtain the credit status features, credit status information can be obtained in all aspects, comprehensive risk assessment is performed, and accuracy of the real-time overdue risk assessment result of the house credit is improved.
Fig. 4 schematically illustrates a flowchart of generating loan overdue risk assessment result information for a target object, according to an embodiment of the disclosure.
As shown in fig. 4, the information generating method of this embodiment includes operations S410 to S420.
In operation S410, the weight of the historical transaction volatility information and the weight of the credit status information are determined.
In operation S420, the loan overdue risk assessment result information is obtained according to the historical transaction fluctuation information, the credit status information of the target object, the weight of the historical transaction fluctuation information, and the weight of the credit status information by using the loan overdue risk assessment model.
According to an embodiment of the present disclosure, in operation S420, historical transaction fluctuation information is processed using a loan overdue risk assessment model to obtain a first assessment result; processing the weight of the credit state information by using the loan overdue risk assessment model to obtain a second assessment result; and obtaining loan overdue risk assessment result information according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit state information.
According to an embodiment of the present disclosure, the weight of the historical transaction volatility information and the weight of the credit status information are determined, for example, 0.6 and 0.4, respectively.
According to the embodiment of the disclosure, historical transaction fluctuation information is processed by using a loan overdue risk assessment model to obtain a first assessment result, for example, the first assessment result is 0.8; and processing the weight of the credit status information to obtain a second evaluation result, for example, the first evaluation result is 0.2.
According to the embodiment of the disclosure, the loan overdue risk assessment result information is obtained according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit status information, for example, the loan overdue risk assessment result information is 0.6×0.8+0.4×0.2=0.56.
According to the embodiment of the disclosure, comprehensive risk assessment can be performed according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit state information, and the accuracy of the loan overdue risk assessment result information is improved.
FIG. 5 schematically illustrates a flowchart of a training method of a loan overdue risk assessment result model, in accordance with an embodiment of the disclosure
As shown in fig. 5, the information generating method of this embodiment includes operations S510 to S550.
In operation S510, sample historical transaction information of a sample object and sample credit status information of the sample object within a preset period are acquired.
In operation S520, the sample history transaction information is processed to obtain sample history transaction fluctuation information within a preset period.
In operation S530, the sample historical transaction fluctuation information and the sample credit status information are used to obtain a sample loan overdue risk assessment result of the sample object.
In operation S540, a loss value is obtained based on the loss function according to the sample loan overdue risk assessment result and the sample object' S loan overdue risk assessment tag.
In operation S550, model parameters of the initial model are adjusted based on the loss value to obtain a loan overdue risk assessment result model.
According to the embodiments of the present disclosure, the sample historical transaction information, the sample historical transaction fluctuation information, and the sample credit status information are the same as the previous definition ranges of the historical transaction information, the historical transaction fluctuation information, and the credit status information of the target object, and are not described herein.
According to the embodiment of the disclosure, the loss function may be a cross entropy loss function, or other loss functions may be selected according to the requirements of an application scenario, and the type of the loss function is not specifically limited in the embodiment of the disclosure.
Based on the information generation method, the disclosure also provides an information generation device. The device will be described in detail below in connection with fig. 6.
Fig. 6 schematically shows a block diagram of the information generating apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the information generating apparatus 600 of this embodiment includes an acquisition module 610, a processing module 620, and an evaluation module 630.
The obtaining module 610 is configured to obtain historical transaction information of the target object and credit status information of the target object in a preset period in response to the received loan overdue risk assessment request of the target object. In an embodiment, the obtaining module 610 may be configured to perform the operation S210 described above, which is not described herein.
The processing module 620 is configured to process the historical transaction information to obtain historical transaction fluctuation information in a preset period. In an embodiment, the processing module 620 may be configured to perform the operation S220 described above, which is not described herein.
The evaluation module 630 is configured to generate loan overdue risk evaluation result information of the target object according to the historical transaction fluctuation information and the credit status information of the target object by using the loan overdue risk evaluation model. In an embodiment, the evaluation module 630 may be used to perform the operation S230 described above, which is not described herein.
According to an embodiment of the disclosure, the evaluation module comprises a first extraction sub-module, a second extraction sub-module, a stitching sub-module, and a first obtaining sub-module. And the first extraction submodule is used for extracting historical transaction fluctuation characteristics from the historical transaction fluctuation information. And the second extraction sub-module is used for extracting credit state characteristics from the credit state information. And the splicing sub-module is used for splicing the historical transaction fluctuation characteristics and the credit status characteristics to obtain combined characteristics. And the first obtaining submodule is used for processing the combined characteristics by using the overdue risk assessment model of the loan to obtain overdue risk assessment result information of the loan.
According to an embodiment of the present disclosure, the first extraction submodule includes a first obtaining unit and an extraction unit. The first obtaining unit is used for obtaining target transaction fluctuation information from historical transaction fluctuation information based on an attention mechanism, wherein the target transaction fluctuation information represents the transaction fluctuation information with the influence probability on the loan overdue risk assessment result being larger than a preset threshold value. And the extraction unit is used for extracting the historical transaction fluctuation feature from the target transaction fluctuation information.
According to an embodiment of the disclosure, the second extraction sub-module comprises a determination unit, an encoding unit and a splicing unit. And the determining unit is used for determining the weight characteristics of the credit state information according to a preset rule. And the encoding unit is used for encoding the credit state information to obtain encoding characteristics. And the splicing unit is used for splicing the coding features and the weight features to obtain credit status features.
According to an embodiment of the present disclosure, the evaluation module includes a determination sub-module and a second get sub-module. And the determining submodule is used for determining the weight of the historical transaction fluctuation information and the weight of the credit state information. And the second obtaining submodule is used for obtaining the overdue risk assessment result information of the loan according to the historical transaction fluctuation information, the credit state information of the target object, the weight of the historical transaction fluctuation information and the weight of the credit state information by using the overdue risk assessment model of the loan.
According to an embodiment of the present disclosure, the second obtaining sub-module includes a second obtaining unit, a third obtaining unit, and a fourth obtaining unit. And the second obtaining unit is used for processing the historical transaction fluctuation information by using the loan overdue risk assessment model to obtain a first assessment result. And the third obtaining unit is used for processing the weight of the credit state information by using the loan overdue risk assessment model to obtain a second assessment result. And the fourth obtaining unit is used for obtaining the overdue risk assessment result information of the loan according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit state information.
Any of the acquisition module 610, the processing module 620, and the evaluation module 630 may be combined in one module to be implemented, or any of the modules may be split into multiple modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the acquisition module 610, the processing module 620, and the evaluation module 630 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging circuitry, or in any one of or a suitable combination of any of three implementations of software, hardware, and firmware. Alternatively, at least one of the acquisition module 610, the processing module 620 and the evaluation module 630 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.
Fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement the information generating method according to an embodiment of the disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the program may be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 700 may further include an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The electronic device 700 may also include one or more of the following components connected to an input/output (I/O) interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to an input/output (I/O) interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 702 and/or RAM 703 and/or one or more memories other than ROM 702 and RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to implement the item recommendation method provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed over a network medium in the form of signals, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (11)
1. An information generation method, comprising:
responding to a received loan overdue risk assessment request of a target object, and acquiring historical transaction information of the target object in a preset period and credit state information of the target object;
processing the historical transaction information to obtain historical transaction fluctuation information in the preset period; and
and generating loan overdue risk assessment result information of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using a loan overdue risk assessment model.
2. The method of claim 1, wherein the generating, using a loan overdue risk assessment model, loan overdue risk assessment result information for the target object based on the historical transaction fluctuation information and credit status information for the target object, comprises:
extracting historical transaction fluctuation features from the historical transaction fluctuation information;
extracting credit status features from the credit status information;
splicing the historical transaction fluctuation feature and the credit status feature to obtain a combined feature; and
and processing the combined characteristics by using the loan overdue risk assessment model to obtain the information of the loan overdue risk assessment result.
3. The method of claim 2, wherein the extracting historical transaction volatility characteristics from the historical transaction volatility information comprises:
obtaining target transaction fluctuation information from the historical transaction fluctuation information based on an attention mechanism, wherein the target transaction fluctuation information represents transaction fluctuation information with the influence probability on a loan overdue risk assessment result being larger than a preset threshold value; and
the historical transaction volatility feature is extracted from the target transaction volatility information.
4. The method of claim 2, wherein the extracting credit status features from the credit status information comprises:
determining the weight characteristics of the credit status information according to a preset rule;
encoding the credit status information to obtain encoding characteristics; and
and splicing the coding feature and the weight feature to obtain the credit status feature.
5. The method of claim 1, wherein the generating, using a loan overdue risk assessment model, loan overdue risk assessment result information for the target object based on the historical transaction fluctuation information and credit status information for the target object, comprises:
determining the weight of historical transaction fluctuation information and the weight of credit state information; and
and obtaining the overdue risk assessment result information of the loan according to the historical transaction fluctuation information, the credit state information of the target object, the weight of the historical transaction fluctuation information and the weight of the credit state information by using the overdue risk assessment model of the loan.
6. The method of claim 5, wherein the obtaining the loan overdue risk assessment result information using the loan overdue risk assessment model based on the historical transaction volatility information, the credit status information of the target object, the weight of the historical transaction volatility information, and the weight of the credit status information comprises:
processing the historical transaction fluctuation information by using the loan overdue risk assessment model to obtain a first assessment result;
processing the weight of the credit state information by using the loan overdue risk assessment model to obtain a second assessment result; and
and obtaining the loan overdue risk assessment result information according to the first assessment result, the second assessment result, the weight of the historical transaction fluctuation information and the weight of the credit state information.
7. The method of claim 1, wherein the training method of the loan overdue risk assessment result model comprises:
acquiring sample historical transaction information of a sample object in a preset period and sample credit state information of the sample object;
processing the sample historical transaction information to obtain sample historical transaction fluctuation information in the preset period;
obtaining a sample loan overdue risk assessment result of the sample object by using the initial model to the sample historical transaction fluctuation information and the sample credit state information;
obtaining a loss value based on the loss function according to the sample loan overdue risk assessment result and the sample object loan overdue risk assessment label; and
and adjusting model parameters of the initial model based on the loss value to obtain the loan overdue risk assessment result model.
8. An information generating apparatus comprising:
the acquisition module is used for responding to the received loan overdue risk assessment request of the target object and acquiring historical transaction information of the target object in a preset period and credit state information of the target object;
the processing module is used for processing the historical transaction information to obtain historical transaction fluctuation information in the preset period; and
and the evaluation module is used for generating the overdue risk evaluation result information of the loan of the target object according to the historical transaction fluctuation information and the credit state information of the target object by using the overdue risk evaluation model of the loan.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-7.
11. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.
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| CN202310639566.8A CN116664278A (en) | 2023-05-31 | 2023-05-31 | Information generation method, device, equipment and storage medium |
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| CN202310639566.8A CN116664278A (en) | 2023-05-31 | 2023-05-31 | Information generation method, device, equipment and storage medium |
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