CN117635309A

CN117635309A - Credit fraud risk determination method, apparatus, device and storage medium

Info

Publication number: CN117635309A
Application number: CN202311649924.XA
Authority: CN
Inventors: 王加正
Original assignee: Agricultural Bank of China
Current assignee: Agricultural Bank of China
Priority date: 2023-12-04
Filing date: 2023-12-04
Publication date: 2024-03-01

Abstract

The invention discloses a credit fraud risk determination method, a credit fraud risk determination device, credit fraud risk determination equipment and a credit fraud risk determination storage medium. The method comprises the following steps: acquiring target credit application information corresponding to a target credit item; determining historical credit sample information based on the target credit application information and predetermined historical credit application information; determining a credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information; based on the credit fraud probability and the preset fraud probability, determining the credit fraud risk corresponding to the target credit item, and modifying the anti-fraud rule, so that the rationality of the anti-fraud rule is improved, the accuracy of identifying the fraud risk is improved, and the risk of passing fraud messages is further reduced.

Description

Credit fraud risk determination method, apparatus, device and storage medium

Technical Field

The present invention relates to the field of credit risk determining technologies, and in particular, to a credit fraud risk determining method, apparatus, device, and storage medium.

Background

The financial institutions have anti-fraud rules such as repeated application and group application, but the anti-fraud rules are mostly based on a mechanism of fixing the number of applications in a fixed time interval. Criminals can still avoid anti-fraud rules through time vulnerabilities and threshold vulnerabilities and successfully pass loan audits.

In the prior art, the number of times of occurrence of unit names and the number of times of occurrence of unit addresses in one month or three months are counted, a threshold in one month and a threshold in three months are set respectively, and if the threshold is exceeded, the application is pushed to a manual examination link.

In the current group fraud identification method, the unit address units and unit names in a relational database are strictly consistent and are counted, the situations of unit abbreviations, full names, address filling habits and the like are not considered, and the situation of omission exists in actual statistics; the fixed time interval and the fixed threshold value can also ignore loopholes such as that the threshold value is not reached and the time interval is not exceeded in the statistical range, so that the accuracy of determining fraud is low, criminals can still avoid anti-fraud rules through time loopholes and threshold value loopholes, and the criminals can pass loan auditing smoothly.

Disclosure of Invention

The invention provides a credit fraud risk determining method, a credit fraud risk determining device, credit fraud risk determining equipment and a credit fraud risk determining storage medium, which are used for modifying anti-fraud rules, improving the rationality of the anti-fraud rules, improving the accuracy of identifying fraud risks and further reducing the risk of approval passing fraud messages.

According to an aspect of the present invention, a credit fraud risk determination method is provided. The method comprises the following steps: acquiring target credit application information corresponding to a target credit item;

determining historical credit sample information based on the target credit application information and predetermined historical credit application information;

determining a credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information;

and determining the credit fraud risk corresponding to the target credit item based on the credit fraud probability and the preset fraud probability.

According to another aspect of the present invention, a credit fraud risk determining apparatus is provided. The device comprises:

the target credit application information acquisition module is used for acquiring target credit application information corresponding to the target credit item;

a historical credit sample information determination module for determining historical credit sample information based on the target credit application information and predetermined historical credit application information;

the project credit fraud probability determining module is used for determining the credit fraud probability corresponding to the target credit project based on the historical credit sample information and the target credit application information;

and the project credit fraud risk determining module is used for determining the credit fraud risk corresponding to the target credit project based on the credit fraud probability and a preset fraud probability.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the credit fraud risk determination method according to any of the embodiments of the present invention.

According to another aspect of the present invention there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the credit fraud risk determination method according to any embodiment of the present invention.

According to the technical scheme, the target credit application information corresponding to the target credit item is obtained. Historical credit sample information is determined based on the target credit application information and predetermined historical credit application information. And determining the credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information. And determining the credit fraud risk corresponding to the target credit item based on the credit fraud probability and the preset fraud probability to modify the anti-fraud rule, improve the rationality of the anti-fraud rule, improve the accuracy of identifying the fraud risk, and further reduce the risk of approval passing through the fraud message.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a credit fraud risk determination method provided according to an embodiment of the present invention;

FIG. 2 is a flow chart of a credit fraud risk determination method provided according to a second embodiment of the present invention;

FIG. 3 is a block diagram of a credit fraud risk determination apparatus provided according to a third embodiment of the present invention;

fig. 4 is a schematic diagram of the architecture of an electronic device implementing a credit fraud risk determining method of an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a credit fraud risk determination method according to an embodiment of the present invention. The method may be performed by a credit fraud risk determination means, which may be implemented in hardware and/or software, which may be configured in an electronic device. As shown in fig. 1, the method includes:

s101, acquiring target credit application information corresponding to a target credit item.

The target credit item may be a company or unit to which an credit is to be applied. The target credit application information may refer to credit application information of the target credit item.

Specifically, the target credit application information corresponding to the target credit item may be obtained in a credit application library or application form.

S102, determining historical credit sample information based on the target credit application information and the predetermined historical credit application information.

The historical credit application information may be credit application information obtained by screening according to preset conditions before the target credit project applies for credit. The historical credit sample information may refer to historical credit information that determines a probability of fraud for the target credit item.

Specifically, the information of the target credit application is compared with the predetermined historical credit application information, the credit application information which can be used for determining the fraud probability of the target credit application information in the historical credit application information is determined, and the historical credit application information is determined as historical credit sample information.

Illustratively, in order to increase the accuracy of determining credit fraud, a credit application pick is required to obtain the historical credit application information, the determining process of which includes:

carrying out information analysis processing on the target credit application information to determine an item credit application area corresponding to the target credit item; based on a historical credit database, determining the historical credit application information according to the project credit application area and a preset historical period.

Wherein, the historical credit database may refer to a database storing historical credit information. The project credit application area may refer to the city or region in which the target credit project application credit resides. The preset history period is a predetermined period of time, which may be one year or half a year.

Specifically, credit application information in the project credit application area and within a preset history period is screened in a history credit database, and is determined as history credit application information.

S103, determining the credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information.

The credit fraud probability may refer to a fraud probability corresponding to the target credit item.

Specifically, the historical credit sample information and the target credit application information may be input into a predetermined fraud probability formula, and the credit fraud probability corresponding to the target credit item may be determined according to the output of the fraud probability formula.

Illustratively, the determining, based on the historical credit sample information and the target credit application information, a credit fraud probability corresponding to the target credit item includes: analyzing the target credit application information to obtain target credit application time corresponding to the target credit item; analyzing the historical credit sample information to obtain average application time of sample credit and the number of historical credit samples; determining the credit fraud probability according to the target credit application time, the sample credit average application time and the historical credit sample number.

The target credit application time may refer to an application time or date of the target credit item. Sample credit average application time may refer to the average application time or date corresponding to all historical credit sample information. The number of historical credit samples refers to the number of samples of all historical credit sample information.

Specifically, a target credit application time, the sample credit average application time, and the historical credit sample number are input into a credit fraud probability formula to determine the credit fraud probability.

Illustratively, the credit fraud probability formula is as follows:

wherein f (t) represents a credit fraud probability; t represents the sum of the number of historical credit samples and the number of credits of the target credit item, i.e. the total number of applications; t represents a target credit application time; t is t ₀ Representing sample credit average application time.

Ideally, under the condition that no marketer actively goes to marketing, and the enterprise normally operates, the situation that staff funds shortage needs to apply for loans is a gentle curve and exponential growth condition cannot be presented in a short time, and staff applying for loans of enterprises in the same region is a random event. When criminals participate in the system, the application curve can be abnormally increased. Gaussian distribution, also known as normal distribution or normal distribution. Under very general sufficient conditions, a large number of independent random variables approximately obey normal distribution, wherein the normal distribution is a bell-shaped curve, and the closer to the center, the larger the value is; the more off-center the smaller the value. Its probability density function (Gaussian function)The characteristics are as follows: when x=u, the probability value is maximum; the probability gradually decreases when the line deviates to both sides along the line x=u; in [ u-sigma, u+sigma ]]Within the interval, the total probability (total energy) accounts for 68%; in [ u-2σ, u+2σ ]]Within the interval, the total probability (total energy) is 95%. Normally, the average application time of staff of the same enterprise should be uniformly distributed, and a short-time concentrated burst mode is presented when group fraud occurs. The book is provided withThe invention sets the total number of applications as T in a preset period, sets credit deception application information to obey Gaussian distribution, and takes [ mu-2sigma, mu+2sigma]And obtaining a formula through interval modeling.

The total number of applications T is constrained in the interval [ u-2σ, u+2σ ] (i.e., t=σ), and the integral formula is as follows:

according to the characteristics of the Gaussian density distribution, the longer the time is, the lower the fraud risk is, the higher the fraud probability is, the more the number of applications is, when the total number of applications T is fixed. And then the credit fraud probability formula is obtained.

S104, determining the credit fraud risk corresponding to the target credit item based on the credit fraud probability and the preset fraud probability.

Wherein the pre-set fraud probability may be used to determine whether the target credit item has a threshold value of fraud risk.

Specifically, the credit fraud probability may be compared with a preset fraud probability, and in the case that the credit fraud probability is greater than or equal to the preset fraud probability, the target credit item is determined to be a credit fraud item; in the event that the credit fraud probability is less than the preset fraud probability, determining that the target credit item is not a credit fraud item.

Illustratively, after said determining the credit fraud risk corresponding to the target credit item, further comprising: and marking the fraud identification of the target credit item so as to enable a rechecker to manually inspect the target credit item based on the fraud identification.

Specifically, the target credit item marked with the fraud identification is manually checked, so that the misjudgment probability is reduced.

Example two

Fig. 2 is a flowchart of a credit fraud risk determining method according to a second embodiment of the present invention, where feature determination history credit sample information is further refined based on the above embodiments. As shown in fig. 2, the method includes:

s201, acquiring target credit application information corresponding to the target credit item.

S202, carrying out information analysis processing on the target credit application information to obtain target credit item identification information and target credit item position information.

Wherein the target credit item identification information may refer to business name information or item name information of the target credit item. The target credit item location information may refer to business address information or item address information of the target credit item.

Specifically, the target credit application information is subjected to information analysis processing to obtain target credit item identification information and target credit item location information.

S203, carrying out information analysis processing on the historical credit application information to obtain historical credit item identification information and historical credit item position information corresponding to each historical loan item.

Wherein, the historical credit item identification information may refer to business name information or item name information of the historical credit item. The historical credit item location information may refer to business address information or item address information of the historical credit item.

Specifically, for each historical credit item, the historical credit application information is subjected to information analysis processing to obtain historical credit item identification information and historical credit item location information.

S204, according to the target credit item identification information, the target credit item position information, the historical credit item identification information corresponding to each historical loan item and the historical credit item position information, historical credit sample information is determined.

Specifically, the target credit item identification information and the historical credit item identification information may be compared, and the target credit item location information is compared with the historical credit item location information of the same historical credit item, thereby determining the required historical credit sample information.

Illustratively, said determining historical credit sample information based on said target credit item identification information and said target credit item location information, and said historical credit item identification information and said historical credit item location information for each of said historical credit items, comprises:

determining an item identification risk coefficient according to the historical credit item identification information and the target credit item identification information corresponding to each historical loan item; determining a project position risk coefficient according to the historical credit project position information and the target credit project position information corresponding to the historical loan project; determining a historical project credit risk coefficient corresponding to the historical loan project according to the project identification risk coefficient and the project position risk coefficient; and under the condition that the historical project credit risk coefficient is larger than a preset credit risk coefficient, determining the historical credit application information corresponding to the historical loan project as the historical credit sample information.

The item identifier risk coefficient may refer to a jekcal coefficient of the item identifier. The item location risk factor may refer to a jaccard factor for the item location. The jaccard coefficient is used to compare similarity to variability between finite sample sets. The greater the value of the Jaccard coefficient, the higher the sample similarity. The historical item credit risk factor may be used to represent the extent of impact on the target credit item.

Specifically, historical credit item identification information and target credit item identification information are entered into a Jaccard formula to determine an item identification risk factor. Historical credit item location information and target credit item location information are entered into a jaccard formula to determine an item location risk factor. The product of the item location risk coefficient and the item identification risk coefficient may be determined as a historical item credit risk coefficient for the historical credit item. And determining historical credit application information corresponding to each historical credit project as historical credit sample information under the condition that the historical project credit risk coefficient is larger than a preset credit risk coefficient.

The invention also provides a method for determining risk factors, which takes project identification risk factors as an example and comprises the following steps: analyzing the historical credit project identification information to obtain a historical credit project identification character; analyzing the target credit item identification information to obtain a target credit item identification character; determining an identification character intersection and an identification character union of the historical credit item identification character and the target credit item identification character; and determining an item identification risk coefficient based on the identification character intersection and the identification character union.

Respectively analyzing the historical credit item identification information and the target credit item identification information to obtain a historical credit item identification character and a target credit item identification character, determining an identification character intersection and an identification character union of the historical credit item identification character and the target credit item identification character, and inputting the identification character intersection and the identification character union into a Jaccard coefficient formula to obtain the item identification risk coefficient. Illustratively, the Jacquard coefficient formula is as follows:

wherein A represents a historical credit item identification character and B represents a target credit item identification character.

In the invention, the determining process of the risk coefficient of the project position is similar to the determining process of the risk coefficient of the project identification, and the description is omitted.

S205, determining the credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information.

S206, determining the credit fraud risk corresponding to the target credit item based on the credit fraud probability and the preset fraud probability.

According to the technical scheme, the target credit application information is subjected to information analysis processing to obtain target credit item identification information and target credit item position information; carrying out information analysis processing on the historical credit application information to obtain historical credit project identification information and historical credit project position information corresponding to each historical loan project; according to the target credit item identification information and the target credit item position information, and the history credit item identification information and the history credit item position information corresponding to each history credit item, history credit sample information is determined, sample application information which is similar to the target credit application information in identification and position can be further screened from the history credit application information, so that the correlation between the history credit sample information and the target credit application information can be improved, and the accuracy of determining the credit fraud risk corresponding to the target credit item is improved.

Example III

Fig. 3 is a schematic structural diagram of a credit fraud risk determining apparatus according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:

a target credit application information obtaining module 301, configured to obtain target credit application information corresponding to a target credit item;

a historical credit sample information determination module 302 for determining historical credit sample information based on the target credit application information and predetermined historical credit application information;

an item credit fraud probability determination module 303, configured to determine a credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information;

an item credit fraud risk determination module 304, configured to determine a credit fraud risk corresponding to the target credit item based on the credit fraud probability and a preset fraud probability.

Optionally, the apparatus further includes: a historical credit application information determination module for:

carrying out information analysis processing on the target credit application information to determine an item credit application area corresponding to the target credit item;

based on a historical credit database, determining the historical credit application information according to the project credit application area and a preset historical period.

Optionally, the historical credit sample information determination module 302 includes:

the target credit application information analysis unit is used for carrying out information analysis processing on the target credit application information to obtain target credit item identification information and target credit item position information;

the historical credit application information analysis unit is used for carrying out information analysis processing on the historical credit application information to obtain historical credit project identification information and historical credit project position information corresponding to each historical loan project;

a history credit sample information determining unit, configured to determine history credit sample information according to the target credit item identification information and the target credit item location information, and the history credit item identification information and the history credit item location information corresponding to each history loan item.

Optionally, the historical credit sample information determining unit includes:

an identification risk coefficient determining subunit, configured to determine, for each historical loan item, an item identification risk coefficient according to the historical credit item identification information and the target credit item identification information corresponding to the historical loan item;

a position risk coefficient determining subunit, configured to determine a project position risk coefficient according to the historical credit project position information and the target credit project position information corresponding to the historical loan project;

a credit risk coefficient determining subunit, configured to determine a historical item credit risk coefficient corresponding to the historical loan item according to the item identification risk coefficient and the item location risk coefficient;

and the credit sample information determining subunit is used for determining the historical credit application information corresponding to the historical loan item as the historical credit sample information under the condition that the historical item credit risk coefficient is larger than a preset credit risk coefficient.

Optionally, the risk factor determination subunit is identified, specifically for:

analyzing the historical credit project identification information to obtain a historical credit project identification character;

analyzing the target credit item identification information to obtain a target credit item identification character;

determining an identification character intersection and an identification character union of the historical credit item identification character and the target credit item identification character;

and determining an item identification risk coefficient based on the identification character intersection and the identification character union.

Optionally, the project credit fraud probability determination module 303 is specifically configured to:

analyzing the target credit application information to obtain target credit application time corresponding to the target credit item;

analyzing the historical credit sample information to obtain average application time of sample credit and the number of historical credit samples;

determining the credit fraud probability according to the target credit application time, the sample credit average application time and the historical credit sample number.

Optionally, the project credit fraud risk determination module 304 is specifically configured to:

and marking the fraud identification of the target credit item so as to enable a rechecker to manually inspect the target credit item based on the fraud identification.

The credit fraud risk determining device provided by the embodiment of the invention can execute the credit fraud risk determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.

Example IV

Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the method credit fraud risk determination.

In some embodiments, the method credit fraud risk determination may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the method credit fraud risk determination described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method credit fraud risk determination in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A credit fraud risk determining method, comprising:

acquiring target credit application information corresponding to a target credit item;

2. The method according to claim 1, wherein the determining of the historical credit application information includes:

3. The method of claim 1, wherein the determining historical credit sample information based on the target credit application information and predetermined historical credit application information comprises:

the information analysis processing is carried out on the target credit application information to obtain target credit item identification information and target credit item position information;

carrying out information analysis processing on the historical credit application information to obtain historical credit item identification information and historical credit item position information corresponding to each historical loan item;

and determining historical credit sample information according to the target credit item identification information and the target credit item position information, and the historical credit item identification information and the historical credit item position information corresponding to each historical loan item.

4. The method of claim 3 wherein said determining historical credit sample information based on said target credit item identification information and said target credit item location information, and said historical credit item identification information and said historical credit item location information for each of said historical credit items comprises:

determining an item identification risk coefficient according to the historical credit item identification information and the target credit item identification information corresponding to each historical loan item;

determining a project position risk coefficient according to the historical credit project position information and the target credit project position information corresponding to the historical loan project;

determining a historical project credit risk coefficient corresponding to the historical loan project according to the project identification risk coefficient and the project position risk coefficient;

and under the condition that the historical project credit risk coefficient is larger than a preset credit risk coefficient, determining the historical credit application information corresponding to the historical loan project as the historical credit sample information.

5. The method of claim 4, wherein said determining an item identification risk factor based on said historical credit item identification information and said target credit item identification information for said historical loan item, comprises:

6. The method of claim 1, wherein the determining a credit fraud probability corresponding to the target credit item based on the historical credit sample information and the target credit application information comprises:

7. The method of claim 1, further comprising, after said determining a credit fraud risk for the target credit item:

8. A credit fraud risk determining apparatus, comprising:

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the credit fraud risk determination method of any of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to implement the credit fraud risk determination method of any of claims 1 to 7 when executed.