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Credit risk control decision method and apparatus

A decision-making method and credit technology, applied in the direction of instruments, finance, data processing applications, etc., can solve the problems of consuming manpower and material resources, large scoring errors, and low efficiency, and achieve high accuracy, avoid interference from human factors, and high efficiency Effect

Inactive Publication Date: 2018-11-13
湖南蜂投金融信息服务有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in practice, it is found that in the process of user credit evaluation and decision-making, the existing manual evaluation and decision-making methods are used, which is inefficient, has large scoring errors, and consumes manpower and material resources.

Method used

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  • Credit risk control decision method and apparatus
  • Credit risk control decision method and apparatus
  • Credit risk control decision method and apparatus

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0043] see figure 1 , figure 1 It is a schematic flowchart of the credit risk control decision-making method provided in Embodiment 1 of the present invention. like figure 1 As shown, the credit risk control decision-making method may include the following steps:

[0044] S101. Construct an artificial intelligence-based credit assessment model based on credit assessment big data, and construct an artificial intelligence-based risk control decision-making model based on risk control decision-making big data.

[0045] In this embodiment, the credit evaluation model is used to evaluate the user's credit situation, and the risk control decision model is used to generate decision data. By adopting the credit evaluation model and the risk control decision-making model for risk control decision-making, the decision-making results obtained are more objective.

[0046] In this embodiment, after building the credit evaluation model and risk control decision-making model based on big...

Embodiment 2

[0070] see figure 2 , figure 2 It is a schematic flowchart of the credit risk control decision-making method provided in Embodiment 2 of the present invention. like figure 2 As shown, the credit risk control decision-making method may include the following steps:

[0071] S201. Construct an artificial intelligence-based credit assessment model based on credit assessment big data, and construct an artificial intelligence-based risk control decision-making model based on risk control decision-making big data.

[0072] In this embodiment, the credit evaluation model is used to evaluate the user's credit situation, and the risk control decision model is used to generate decision data.

[0073] S202. Obtain the basic information of the user.

[0074] In this embodiment, the user information includes one or more of the user's contact information, user's geographic information, and user-related demographic information, which is not limited in this embodiment.

[0075] S203. O...

Embodiment 3

[0089] see image 3 , image 3 It is a schematic structural diagram of the credit risk control decision-making device provided in Embodiment 3 of the present invention. like image 3 As shown, the credit risk control decision-making device includes:

[0090] The construction module 301 is used to construct an artificial intelligence-based credit assessment model based on the credit assessment big data, and construct an artificial intelligence-based risk control decision-making model based on the risk control decision-making big data, wherein the credit assessment model is used to assess the user's credit situation, The risk control decision model is used to generate decision data.

[0091] An acquisition module 302, configured to acquire basic information of the user.

[0092] The information judging module 303 is used to judge whether the basic information is real information.

[0093] The evaluation module 304 is used to obtain the user's interbank credit data from the ...

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Abstract

The invention provides a credit risk control decision method and apparatus. The method comprises the following steps: constructing a credit evaluation model based on artificial intelligence accordingto credit evaluation big data, and constructing a risk control decision model based on artificial intelligence according to risk control decision big data, wherein the credit evaluation model is usedfor evaluating the credit situation of a user, and the risk control decision model is used for generating decision data; obtaining the basic information of the user, and judging whether the basic information is real information; if yes, obtaining interbank credit data of the user from an interbank financial credit institution, and evaluating the credit situation of the user with reference to the credit evaluation model and the interbank credit data; and generating decision data with reference to the risk control decision model and the credit situation, and outputting the decision data. By adoption of the credit risk control decision method provided by the invention, the user information can be automatically evaluated, and corresponding decision is made, so that the efficiency is high, andthe accuracy is high.

Description

technical field [0001] The present invention relates to the field of e-commerce, in particular to a credit risk control decision-making method and device. Background technique [0002] With the rapid development of social economy, traditional small loan credit institutions and Internet small loan companies can start from different scenarios and provide flexible financial services, real-time credit, real-time lending, pre-loan risk assessment, loan risk management, and post-loan risk Early warning realizes Internet-based small loan business. In the process of applying for a loan for existing credit, there are usually steps such as data authenticity verification, credit evaluation, and decision-making. However, in practice, it is found that in the process of user credit evaluation and decision-making, manual evaluation and decision-making are currently used, which is inefficient, has large scoring errors, and consumes manpower and material resources. Contents of the inventi...

Claims

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Application Information

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IPC IPC(8): G06Q40/02
CPCG06Q40/03
Inventor 付翔翔
Owner 湖南蜂投金融信息服务有限公司
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