CN112419040A - Credit anti-fraud identification method, credit anti-fraud identification device and storage medium - Google Patents
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Abstract
The invention provides a credit anti-fraud identification method, a credit anti-fraud identification device and a credit anti-fraud identification storage medium, wherein the method comprises the following steps: acquiring power data of at least one dimension of an enterprise to be identified; inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized; and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified. Therefore, the credit fraud risk of the enterprise can be more scientifically and objectively identified based on the electric power data of the enterprise, a new credit anti-fraud auxiliary tool is provided for bank loan, the capacity of bank precaution and credit risk control is effectively enhanced, and the sustainable development of bank economy is realized.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a credit anti-fraud identification method, a credit anti-fraud identification device and a credit anti-fraud identification storage medium.
Background
When the bank settles financing for an enterprise, the credit risk of the enterprise needs to be checked. At present, related data submitted by enterprises are generally checked manually by bank personnel, and the problems of relatively lagged information, higher labor cost and time cost, incomplete coverage of customer groups and the like exist in the credit checking stage. Therefore, a new credit anti-fraud identification method is needed.
Disclosure of Invention
The invention provides a credit anti-fraud identification method, a credit anti-fraud identification device and a credit anti-fraud identification storage medium, which are used for better credit anti-fraud identification.
The invention provides a credit anti-fraud identification method, which comprises the following steps:
acquiring power data of at least one dimension of an enterprise to be identified;
inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized;
and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
The invention also provides a credit anti-fraud recognition device, comprising: a memory, a processor, and a communications component;
the memory for storing a computer program;
the processor, coupled with the memory, to execute the computer program to:
acquiring power data of at least one dimension of an enterprise to be identified;
inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized;
and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
The present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the credit anti-fraud identification method described above.
The invention has the beneficial effects that:
the credit anti-fraud identification method and the credit anti-fraud identification device provided by the invention are used for acquiring at least one dimension of electric power data of an enterprise to be identified; inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized; and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified. Therefore, the credit fraud risk of the enterprise can be more scientifically and objectively identified based on the electric power data of the enterprise, a new credit anti-fraud auxiliary tool is provided for bank loan, the capacity of bank precaution and credit risk control is effectively enhanced, and the sustainable development of bank economy is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating a credit anti-fraud identification method according to an exemplary embodiment of the present invention;
fig. 2 is a schematic structural diagram of a credit fraud prevention identification apparatus according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a credit fraud prevention identification method according to an exemplary embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
and 103, if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
In this embodiment, the anti-fraud identification model can more scientifically and objectively identify the credit fraud risk of a business based on the power data of the business. The anti-fraud identification model outputs the credit risk score of the enterprise to be identified based on the electric power data of the enterprise, and if the credit risk score is larger than a preset score threshold value, the credit fraud risk of the enterprise to be identified is determined; and if the credit risk score is not larger than the preset score threshold value, determining that the enterprise to be identified has no credit fraud risk. Wherein the score threshold is set according to actual conditions.
In the present embodiment, the business to be identified may be a small micro-business, but is not limited to a small micro-business.
As an example, the at least one dimension of power data includes:
the system comprises start-up verification data, power failure feedback degree, power utilization difference degree, power consumption fluctuation data, power fee payment level data, power utilization behavior data and default power utilization data.
As an example, the start-up verification data at least includes account information of a power consumption number and power transmission state information;
the electricity consumption difference degree at least comprises a monthly electricity consumption difference and an annual electricity consumption difference;
the electricity consumption fluctuation data at least comprises the times that electricity consumption is less than the same industry in nearly 12 months and electricity consumption fluctuation in nearly 12 months;
the electric charge payment level data at least comprises an electric charge payment time difference of nearly 12 months, a payment amount difference of nearly 12 months and a current balance level;
the electricity usage behavior data comprises a 12-month average electricity usage growth rate;
the default electricity utilization data comprise arrearage duration, default electricity utilization behavior times and electricity stealing behavior times.
The credit anti-fraud identification method provided by the embodiment of the invention is characterized in that electric power data of at least one dimension of an enterprise to be identified is acquired; inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized; and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified. Therefore, the credit fraud risk of the enterprise can be more scientifically and objectively identified based on the electric power data of the enterprise, a new credit anti-fraud auxiliary tool is provided for bank loan, the capacity of bank precaution and credit risk control is effectively enhanced, and the sustainable development of bank economy is realized.
On the basis of the foregoing embodiment, optionally, before acquiring the power data of at least one dimension of the enterprise to be identified, the method further includes:
obtaining various types of sample data for training the anti-fraud recognition model, wherein the various types of sample data comprise sample data related to start-up verification, sample data related to power failure feedback, sample data related to power consumption difference, sample data related to power consumption fluctuation, sample data related to power consumption payment level, sample data related to power consumption behavior data and sample data related to default power consumption data;
obtaining labeling results of various sample data;
training an initial logistic regression model according to various sample data and labeling results thereof to obtain the anti-fraud recognition model;
the trained anti-fraud recognition model can output the start-up verification score, the power failure feedback score, the power utilization difference score, the power consumption fluctuation score, the power fee payment level score, the power utilization behavior score and the default power utilization score of the enterprise to be recognized, and output the credit risk score of the enterprise to be recognized based on the scores.
In particular, in training an anti-fraud recognition model, sample data may be prepared from multiple dimensions to perform fraud risk assessment on power data for multiple dimensions of an enterprise. Model training is prior art and further description of model training is found in the prior art.
In this embodiment, the labeling result of each type of sample data can be understood as the expected output result of each type of sample data. For example, for sample data related to start-up verification, the result is labeled as a start-up verification score of the expected model output.
In this embodiment, the anti-fraud identification model may perform weighted summation on the startup verification score, the power failure feedback score, the power utilization difference score, the power consumption fluctuation score, the power fee payment level score, the power utilization behavior score, and the default power utilization score to obtain the credit risk score of the enterprise to be identified. Wherein, in the weighted summation, the weight of each summation item is set according to experience.
In the embodiment, the fraud risk of an enterprise can be automatically identified by establishing the anti-fraud identification model, and the risk identification is more scientific and objective.
Fig. 2 is a schematic structural diagram of a credit fraud prevention identification apparatus according to an exemplary embodiment of the present invention. As shown in fig. 2, the apparatus includes: the method comprises the following steps: memory 11, processor 12 and communication component 13.
The memory 11 is used for storing a computer program and may be configured to store other various data to support operations on the processor. Examples of such data include instructions for any application or method operating on the processor, contact data, phonebook data, messages, pictures, videos, and so forth.
The memory 11 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
A processor 12, coupled to the memory 11, for executing the computer program in the memory 11 for:
acquiring power data of at least one dimension of an enterprise to be identified;
inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized;
and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
Further, the power data of the at least one dimension includes:
the system comprises start-up verification data, power failure feedback degree, power utilization difference degree, power consumption fluctuation data, power fee payment level data, power utilization behavior data and default power utilization data.
Further, the start-up verification data at least comprises account information of a power consumption number and power transmission state information; and the number of the first and second groups,
the electricity consumption difference degree at least comprises a monthly electricity consumption difference and an annual electricity consumption difference;
the electricity consumption fluctuation data at least comprises the times that electricity consumption is less than the same industry in nearly 12 months and electricity consumption fluctuation in nearly 12 months;
the electric charge payment level data at least comprises an electric charge payment time difference of nearly 12 months, a payment amount difference of nearly 12 months and a current balance level;
the electricity usage behavior data comprises a 12-month average electricity usage growth rate;
the default electricity utilization data comprise arrearage duration, default electricity utilization behavior times and electricity stealing behavior times.
Further, the processor 12, when training the anti-fraud recognition model, is specifically configured to:
obtaining various types of sample data for training the anti-fraud recognition model, wherein the various types of sample data comprise sample data related to start-up verification, sample data related to power failure feedback, sample data related to power consumption difference, sample data related to power consumption fluctuation, sample data related to power consumption payment level, sample data related to power consumption behavior data and sample data related to default power consumption data;
obtaining labeling results of various sample data;
training an initial logistic regression model according to various sample data and labeling results thereof to obtain the anti-fraud recognition model;
the trained anti-fraud recognition model can output the start-up verification score, the power failure feedback score, the power utilization difference score, the power consumption fluctuation score, the power fee payment level score, the power utilization behavior score and the default power utilization score of the enterprise to be recognized, and output the credit risk score of the enterprise to be recognized based on the scores.
The apparatus shown in fig. 2 can perform the method of the embodiment shown in fig. 1, and reference may be made to the related description of the embodiment shown in fig. 1 for a part of this embodiment that is not described in detail. The implementation process and technical effect of the technical solution refer to the description in the embodiment shown in fig. 1, and are not described herein again.
Further, as shown in fig. 2, the apparatus further includes: display 14, power supply 15, audio 16, and other components. Only some of the components are schematically shown in fig. 2, and it is not meant that the processor includes only the components shown in fig. 2. In addition, the components shown by the dashed boxes in fig. 2 are optional components, but not necessary components, and may be determined according to a specific implementation form of the credit fraud prevention recognition apparatus. If the credit anti-fraud recognition apparatus is implemented as a terminal device such as a notebook computer, a tablet, a mobile phone, etc., the components shown by the dashed boxes in fig. 2 may be included; if the credit anti-fraud recognition apparatus is implemented as a server-side device such as a conventional server, a cloud server, or a server array, the components shown by the dashed boxes in fig. 2 are not included.
Accordingly, the embodiment of the present invention further provides a computer readable storage medium storing a computer program, where the computer program is capable of implementing the steps that can be executed by a processor in the above method embodiments when executed.
The communication component of fig. 2 described above is configured to facilitate communication between the device in which the communication component is located and other devices in a wired or wireless manner. The device where the communication component is located can access a wireless network based on a communication standard, such as a WiFi, a 2G, 3G, 4G/LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
The display of fig. 2 described above includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The power supply assembly of fig. 2 described above provides power to the various components of the device in which the power supply assembly is located. The power components may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device in which the power component is located.
The audio components of fig. 2 described above may be configured to output and/or input audio signals. For example, the audio component includes a Microphone (MIC) configured to receive an external audio signal when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described 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 flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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, embedded processor, 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (7)
1. A credit fraud identification method, comprising:
acquiring power data of at least one dimension of an enterprise to be identified;
inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized;
and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
2. The credit anti-fraud identification method according to claim 1, characterized in that said at least one dimension of power data comprises:
the system comprises start-up verification data, power failure feedback degree, power utilization difference degree, power consumption fluctuation data, power fee payment level data, power utilization behavior data and default power utilization data.
3. The credit fraud prevention identification method according to claim 2, wherein the start-up verification data includes at least a power consumption number accounting information, a power transmission status information;
the electricity consumption difference degree at least comprises a monthly electricity consumption difference and an annual electricity consumption difference;
the electricity consumption fluctuation data at least comprises the times that electricity consumption is less than the same industry in nearly 12 months and electricity consumption fluctuation in nearly 12 months;
the electric charge payment level data at least comprises an electric charge payment time difference of nearly 12 months, a payment amount difference of nearly 12 months and a current balance level;
the electricity usage behavior data comprises a 12-month average electricity usage growth rate;
the default electricity utilization data comprise arrearage duration, default electricity utilization behavior times and electricity stealing behavior times.
4. The credit anti-fraud identification method according to claim 1, further comprising, before obtaining power data for at least one dimension of a business to be identified:
obtaining various types of sample data for training the anti-fraud recognition model, wherein the various types of sample data comprise sample data related to start-up verification, sample data related to power failure feedback, sample data related to power consumption difference, sample data related to power consumption fluctuation, sample data related to power consumption payment level, sample data related to power consumption behavior data and sample data related to default power consumption data;
obtaining labeling results of various sample data;
training an initial logistic regression model according to various sample data and labeling results thereof to obtain the anti-fraud recognition model;
the trained anti-fraud recognition model can output the start-up verification score, the power failure feedback score, the power utilization difference score, the power consumption fluctuation score, the power fee payment level score, the power utilization behavior score and the default power utilization score of the enterprise to be recognized, and output the credit risk score of the enterprise to be recognized based on the scores.
5. A credit fraud identification apparatus, comprising: a memory, a processor, and a communications component;
the memory for storing a computer program;
the processor, coupled with the memory, to execute the computer program to:
acquiring power data of at least one dimension of an enterprise to be identified;
inputting the power data of at least one dimension into a pre-trained anti-fraud recognition model for recognition to obtain a credit risk score of the enterprise to be recognized;
and if the credit risk score is larger than a preset score threshold value, determining that the credit fraud risk exists in the enterprise to be identified.
6. The apparatus of claim 5, wherein the processor, when training the anti-fraud recognition model, is specifically configured to:
obtaining various types of sample data for training the anti-fraud recognition model, wherein the various types of sample data comprise sample data related to start-up verification, sample data related to power failure feedback, sample data related to power consumption difference, sample data related to power consumption fluctuation, sample data related to power consumption payment level, sample data related to power consumption behavior data and sample data related to default power consumption data;
obtaining labeling results of various sample data;
training an initial logistic regression model according to various sample data and labeling results thereof to obtain the anti-fraud recognition model;
the trained anti-fraud recognition model can output the start-up verification score, the power failure feedback score, the power utilization difference score, the power consumption fluctuation score, the power fee payment level score, the power utilization behavior score and the default power utilization score of the enterprise to be recognized, and output the credit risk score of the enterprise to be recognized based on the scores.
7. A storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the credit anti-fraud identification method of any of claims 1 to 4 when run.
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| CN113313407A (en) * | 2021-06-16 | 2021-08-27 | 上海交通大学 | Enterprise power utilization behavior identification method and device |
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| CN113095931A (en) * | 2021-04-25 | 2021-07-09 | 国家电网有限公司 | Post-loan risk monitoring method and device and computer equipment |
| CN113313407A (en) * | 2021-06-16 | 2021-08-27 | 上海交通大学 | Enterprise power utilization behavior identification method and device |
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