CN115409614A

CN115409614A - Data processing method, device, equipment, medium and product

Info

Publication number: CN115409614A
Application number: CN202211191884.4A
Authority: CN
Inventors: 刘锦山; 徐蔚峰; 蔡佳煌; 林乐凝
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2022-11-29

Abstract

The application discloses a data processing method, device, equipment, medium and product. The method comprises the following steps: after the target user obtains the loan money, obtaining an abnormal object list and user information of the target user, wherein the user information comprises the fund flow direction of the target user; determining a target relation between a target user and a target object in the abnormal object list according to the user information; and under the condition that the target relationship meets the preset relationship, determining that the target user has abnormal loan behaviors. By adopting the data processing method, the data processing device, the data processing equipment, the data processing medium and the data processing product, whether abnormal loan behaviors exist in the target user can be continuously monitored after the loan is issued, and the abnormal loan behaviors of the target user can be timely found so as to take corresponding measures in time and avoid huge loss.

Description

Data processing method, device, equipment, medium and product

Technical Field

The present application relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, medium, and product.

Background

With the development of loan service, more and more users transact the loan service.

If some users apply for loan through special means, huge loss will be brought to banks, so in order to avoid causing loss to banks, abnormal loan behaviors of users need to be found in time so as to take corresponding measures in time, however, no method capable of finding abnormal loan behaviors of users in time is found in related technologies at present.

Disclosure of Invention

The embodiment of the application provides a data processing method, a data processing device, equipment, a medium and a product, which can at least solve the problem that the abnormal loan behavior of a user cannot be found in time in the prior art.

In a first aspect, an embodiment of the present application provides a data processing method, where the method includes:

after a target user obtains loan money, obtaining an abnormal object list and user information of the target user, wherein the user information comprises the fund flow direction of the target user;

determining a target relation between the target user and a target object in the abnormal object list according to the user information;

and under the condition that the target relationship meets a preset relationship, determining that the target user has abnormal loan behaviors.

In some embodiments, the user information further includes loan application information of the target user, the loan application information including a unit in which the target user is located.

In some embodiments, the exception object list includes an exception account, an exception user with historical exception behavior, and an exception unit, and the number of exception users included in the employee of the exception unit exceeds a first threshold. The preset relationship comprises at least one of:

the loan amount flows into the abnormal account;

the fund exchange exists between the account corresponding to the target user and the account corresponding to the abnormal user;

the unit where the target user is located is the abnormal unit;

the number of the abnormal users in the staff of the unit where the target user is located exceeds a second threshold value, and the second threshold value is smaller than the first threshold value.

In some embodiments, after the determining that the target user has abnormal loan behavior, the method further comprises:

and generating prompt information to prompt the user to check the abnormal loan behavior of the target user.

adding the target user to the list of abnormal objects.

In a second aspect, an embodiment of the present application provides a data processing apparatus, including:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an abnormal object list and user information of a target user after the target user acquires loan money, and the user information comprises the fund flow direction of the target user;

the first determining module is used for determining the target relation between the target user and the target object in the abnormal object list according to the user information;

and the second determining module is used for determining that the target user has abnormal loan behaviors under the condition that the target relationship meets a preset relationship.

In a third aspect, an embodiment of the present application provides an electronic device, where the device includes: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as shown in any of the embodiments of the first aspect.

In a fourth aspect, the present application provides a computer storage medium, on which computer program instructions are stored, and when executed by a processor, the computer program instructions implement the data processing method shown in any one of the embodiments of the first aspect.

In a fifth aspect, the present application provides a computer program product, and when executed by a processor of an electronic device, the instructions in the computer program product cause the electronic device to execute the data processing method shown in any one of the embodiments of the first aspect.

The data processing method, the data processing device, the data processing equipment, the data processing medium and the data processing product can acquire the abnormal object list and the user information of the target user after the target user acquires the loan money, determine the target relationship between the target user and the target object in the abnormal object list according to the user information, and determine that the target user has abnormal loan behaviors when the target relationship meets the preset relationship. Because the user information comprises the fund flow direction of the target user, by adopting the data processing method, the device, the equipment, the medium and the product of the embodiment of the application, whether the target user has the abnormal loan behavior can be determined according to the fund flow direction of the target user after the target user obtains the loan money, so that whether the target user has the abnormal loan behavior can be continuously monitored after the loan is issued, the abnormal loan behavior of the target user can be timely found, the corresponding measures can be timely taken, and the huge loss is avoided.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings may be obtained according to the drawings without creative efforts.

Fig. 1 is a flowchart of a data processing method according to an embodiment of the present application;

FIG. 2 is a flow chart of another data processing method provided by an embodiment of the present application;

fig. 3 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.

In addition, it should be noted that, in the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with relevant regulations of national laws and regulations.

With the development of the online banking business and the internet technology, the completion of online loan by an individual customer through an electronic channel is more rapid and convenient. Meanwhile, due to the rapid development of information technology, some users may obtain loans through various abnormal means such as counterfeit materials, fakery identities, intermediary packages and the like. Thus, a great loss is brought to the bank.

Based on this, the embodiment of the present application provides a data processing method, that is, after a target user obtains a loan fund, an abnormal object list and user information of the target user are obtained, where the user information includes a fund flow direction of the target user; determining a target relation between the target user and a target object in the abnormal object list according to the user information; and under the condition that the target relationship meets a preset relationship, determining that the target user has abnormal loan behaviors.

Therefore, after the target user obtains the loan money, the abnormal object list and the user information of the target user can be obtained, the target relationship between the target user and the target object in the abnormal object list is determined according to the user information, and the target user is determined to have abnormal loan behaviors under the condition that the target relationship meets the preset relationship. Because the user information comprises the fund flow direction of the target user, by adopting the data processing method, the device, the equipment, the medium and the product of the embodiment of the application, whether the target user has the abnormal loan behavior can be determined according to the fund flow direction of the target user after the target user obtains the loan money, so that whether the target user has the abnormal loan behavior can be continuously monitored after the loan is issued, the abnormal loan behavior of the target user can be timely found, the corresponding measures can be timely taken, and the huge loss is avoided.

The data processing method and the data processing apparatus provided in the embodiments of the present application are described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.

Fig. 1 shows a flow chart of a data processing method provided in an embodiment of the present application, and it should be noted that the data processing method may be applied to a data processing apparatus, as shown in fig. 1, the data processing method may include the following steps:

s110, after the target user obtains the loan money, obtaining an abnormal object list and user information of the target user;

s120, determining the target relation between the target user and the target object in the abnormal object list according to the user information;

and S130, determining that the target user has abnormal loan behaviors under the condition that the target relationship meets the preset relationship.

Referring to S110, after the target user obtains the loan money, the data processing apparatus may acquire user information of the target user and acquire an abnormal object list from internal and/or external components. The target user may be a user who applies for a loan and successfully obtains the loan. The user information may include a flow of funds for the target user. The exception object list may be obtained from within and/or outside the bank.

In some embodiments, in order to more accurately determine whether the target user has abnormal loan behavior, the user information may further include loan application information of the target user, which may include the unit in which the target user is located.

Here, the loan application information may further include other information, such as personal information of the target user, the loan amount, and the like, which is not limited herein.

In some examples, according to the user information of the target user, the unit in which the target user is located may be determined to be unit a, and the loan amount of the target user flows to account B.

In this way, since the user information may include information of multiple dimensions, whether the target user has abnormal loan behavior may be more comprehensively identified from multiple angles.

In some embodiments, in order to further improve the accuracy of the abnormal loan behavior identification, the abnormal account, the abnormal users with historical abnormal behavior, and the abnormal unit may be included in the abnormal object list, and the number of the abnormal users included in the staff of the abnormal unit may exceed the first threshold.

Here, the abnormal user may be a user who has a history of abnormal behavior, that is, a user who has experienced abnormal behavior, and the history of abnormal behavior may include abnormal loan behavior, and may also include other abnormal behavior. If the number of abnormal users existing in the employee of a certain unit exceeds a first threshold value, the unit can be determined to be an abnormal unit. The first threshold may be set according to actual requirements.

In some examples, exception accounts B and C, exception users D and E, and exception units a and F may be included in the exception object list.

In this way, since the abnormal object list includes a plurality of abnormal objects, the accuracy of identifying abnormal loan behavior can be further improved.

Referring to S120, the exception object list may include one or more target objects, which may specifically be an exception account, an exception user, and/or an exception unit. The data processing apparatus may determine a target relationship between the target user and the target object according to the user information of the target user. The target relationship may include at least one of whether the loan amount of the target user flows into the abnormal account, whether a fund exchange exists between the account corresponding to the target user and the account corresponding to the abnormal user, whether the unit where the target user is located is the abnormal unit, and whether the number of the abnormal users in the staff of the unit where the target user is located exceeds a second threshold. The second threshold may be less than the first threshold.

In some examples, the target relationship may be that the unit a in which the target user is located is an abnormal unit and the loan amount of the target user flows to the abnormal account B.

Referring to S130, the data processing device may determine that the target relationship is that the dog satisfies the preset relationship, and if the target relationship satisfies the preset relationship, the data processing device may determine that the target user has an abnormal loan behavior; if the target relationship does not satisfy the preset relationship, the data processing device may determine that the target user does not have an abnormal loan behavior.

In some embodiments, to more accurately identify whether the target user has abnormal loan behavior, the preset relationship may include at least one of:

the loan money flows into an abnormal account;

fund exchange exists between the account corresponding to the target user and the account corresponding to the abnormal user;

the unit of the target user is an abnormal unit;

the number of abnormal users in the staff of the unit where the target user is located exceeds a second threshold value, and the second threshold value is smaller than the first threshold value.

Here, if the number of abnormal users in the employee of a certain unit does not exceed the first threshold, the unit may not be determined as an abnormal unit, but in order to improve the accuracy of identifying abnormal loan behavior, a second threshold may be set, which is smaller than the first threshold, and if the number of abnormal users in the employee of the unit where the target user is located does not exceed the first threshold but exceeds the second threshold, it may be determined that the target user has abnormal loan behavior.

In some examples, the target relationship may be that the unit a where the target user is located is an abnormal unit, and the loan money of the target user flows to the abnormal account B, and the preset relationship is satisfied, so that it may be determined that the target user has an abnormal loan behavior.

Therefore, whether the target user has abnormal loan behaviors or not can be identified more accurately by setting the preset relationship.

In some implementations, the identification of whether the target user has abnormal loan behavior may be timed.

Based on the foregoing S110-S130, in another possible embodiment, as shown in fig. 2, after S130, the method may further include:

and S140, generating prompt information to prompt the user to check the abnormal loan behavior of the target user.

Here, the data processing apparatus may generate prompt information after determining that the target user has abnormal loan behavior, and the prompt information may be used to prompt the user to check the abnormal loan behavior of the target user to confirm whether the target user really has abnormal loan behavior. After the verification confirms that the target user has abnormal loan behavior, corresponding measures can be taken, such as freezing the loan money of the target user.

In some examples, the prompt message may be at least one of a sound, a vibration, an icon, and a text, and may also be other prompt messages, which is not limited herein.

Therefore, by generating the prompt message, the user can be prompted to check after the abnormal loan behavior is recognized, so that great loss can be avoided.

In some embodiments, to improve the accuracy of the subsequent abnormal loan behavior identification, after S130, the method may further include:

adding the target user to the abnormal object list.

Specifically, after the user checks the abnormal loan behavior of the target user and confirms that the target user really has the abnormal loan behavior, the target user may be determined to be an abnormal user, and the target user is added to the abnormal object list to expand the abnormal object list.

In some examples, the target user may be added to the list of exception objects as an exception user, and the account of the target user may be added to the list of exception objects as an exception account.

In some embodiments, after the target user is determined to be an abnormal user, it may be determined whether the number of abnormal users in employees of the unit where the target user is located exceeds a first threshold, and if the number of abnormal users exceeds , the unit where the target user is located may be added to the abnormal object list as an abnormal unit.

In this way, after the target user is confirmed to have the abnormal loan behavior, the target user is added to the abnormal object list, so that the abnormal object list can be expanded, and the accuracy of subsequent abnormal loan behavior identification can be improved conveniently.

In some embodiments, it may also be identified whether the user has abnormal loan behavior according to the loan application information of the user when the user applies for a loan but does not obtain the loan money, that is, during the process of examining and approving the loan, so that it may be identified that some users have abnormal loan behavior. After the user obtains the loan money, the user with abnormal loan behavior can be further identified by the method in the above embodiments, so that the user with abnormal loan behavior can be identified to a greater extent, and the loss of the bank is reduced.

Based on the same inventive concept, the embodiment of the application also provides a data processing device. The data processing apparatus provided in the embodiment of the present application is described in detail below with reference to fig. 3.

Fig. 3 shows a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

As shown in fig. 3, the data processing apparatus may include:

the obtaining module 301 is configured to obtain an abnormal object list and user information of a target user after the target user obtains a loan money, where the user information includes a fund flow direction of the target user;

a first determining module 302, configured to determine, according to the user information, a target relationship between a target user and a target object in the abnormal object list;

and the second determining module 303 is configured to determine that the target user has an abnormal loan behavior when the target relationship meets the preset relationship.

In some embodiments, to more accurately determine whether the target user has abnormal loan behavior, the user information may further include loan application information of the target user, which may include the entity in which the target user is located.

loan funds flow into the abnormal account;

the unit of the target user is an abnormal unit;

In some embodiments, to save manpower and improve efficiency, the apparatus may further include:

and the generating module is used for generating prompt information after determining that the target user has the abnormal loan behavior so as to prompt the user to check the abnormal loan behavior of the target user.

In some embodiments, to improve the accuracy of the subsequent abnormal loan behavior identification, the apparatus may further include:

and the adding module is used for adding the target user to the abnormal object list after determining that the target user has abnormal loan behaviors.

Fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.

As shown in fig. 4, the electronic device 4 is a structure diagram of an exemplary hardware architecture of an electronic device capable of implementing the data processing method and the data processing apparatus according to the embodiment of the present application. The electronic device may refer to an electronic device in the embodiments of the present application.

The electronic device 4 may comprise a processor 401 and a memory 402 in which computer program instructions are stored.

Specifically, the processor 401 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. The memory 402 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid-state memory. In particular embodiments, memory 402 may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory 402 comprises one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to a method according to an aspect of the present application.

The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any of the data processing methods in the above embodiments.

In one example, the electronic device can also include a communication interface 403 and a bus 404. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 404 to complete communication therebetween.

The communication interface 403 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application.

The bus 404 comprises hardware, software, or both to couple the components of the electronic device to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 404 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The electronic device may execute the data processing method in the embodiment of the present application, so as to implement the data processing method and apparatus described in conjunction with fig. 1 to 3.

In addition, in combination with the data processing method in the foregoing embodiments, the embodiments of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions, when executed by a processor, implement any of the data processing methods in the above embodiments.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an Erasable ROM (EROM), a floppy disk, a CD-ROM, an optical disk, a hard disk, an optical fiber medium, a Radio Frequency (RF) link, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As described above, only the specific embodiments of the present application are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. A method of data processing, the method comprising:

2. The method of claim 1, wherein the user information further includes loan application information for the target user, the loan application information including a unit in which the target user is located.

3. The method according to claim 2, wherein the abnormal object list comprises an abnormal account, an abnormal user with historical abnormal behavior and an abnormal unit, and the number of the abnormal users included in the staff of the abnormal unit exceeds a first threshold.

4. The method of claim 3, wherein the preset relationship comprises at least one of:

the loan money flows into the abnormal account;

the unit where the target user is located is the abnormal unit;

5. The method of claim 1, wherein after the determining that the target user has abnormal loan behavior, the method further comprises:

6. The method of claim 1, wherein after the determining that the target user has abnormal loan behavior, the method further comprises:

adding the target user to the list of abnormal objects.

7. A data processing apparatus, characterized in that the apparatus comprises:

8. An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements a data processing method as claimed in any one of claims 1-6.

9. A computer storage medium having computer program instructions stored thereon which, when executed by a processor, implement a data processing method according to any one of claims 1 to 6.

10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the data processing method according to any of claims 1-6.