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Credit review model construction method, device and system

A technology for constructing methods and models, applied in the computer field, can solve the problems of difficult to guarantee the accuracy of evaluation results and difficult to guarantee timeliness.

Inactive Publication Date: 2020-01-31
优轩(北京)信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to reduce the loss of the platform, the process of credit review requires the participation of a large number of personnel with professional background knowledge, and it takes a certain amount of time to evaluate the credit of potential borrowers. As the business grows, the amount of credit review increases. The traditional method In terms of timeliness, it is difficult to guarantee. Some platforms build a credit review model by sorting and analyzing historical data. During the credit review process, apply for information, and then input the application information into the credit review model. Finally, credit review, the model Output an evaluation result based on the application information
[0004] The construction method of the credit review model shown in the prior art inputs the evaluation results and application information into the computer at the same time, and the computer constructs the credit review model through the relationship between the application information and the evaluation results, but due to the uniqueness of the financial application information, directly The application information and evaluation results are input into the model constructed by the computer, and the accuracy of the evaluation results output by the above-mentioned credit review model is difficult to guarantee

Method used

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  • Credit review model construction method, device and system
  • Credit review model construction method, device and system

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Experimental program
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Embodiment 2

[0127] see image 3 The second aspect of the embodiment of the present application shows a method for constructing a credit review model, the method comprising:

[0128] S101 acquires historical data, and divides the historical data into data sets, wherein the data sets include: a training set, and a test set;

[0129] Historical data consists of multiple modeling individuals, including credit review data, and evaluation results, where the evaluation results are converted into a computer-recognizable language, for example, "1" for passing, and "0" for failing;

[0130] The credit review data includes a large number of characteristics such as the user's basic information, credit review application information, credit report and user behavior information;

[0131] Select a part of the data from the historical data according to the purchase type and time, and split this part of the data into a training set and a test set. Two methods are used for data splitting. , and the other...

Embodiment 3

[0138] In the financial industry, as an important feature, the week usually has a greater impact on the evaluation results. For example, the contribution of Sunday to the credit review model is greater than that of Monday (working day) to the credit review model. Therefore, the week should be input into the computer as an important feature in the modeling process to participate in the construction of the model. However, in the process of obtaining historical data, the characteristics of the week are automatically generated in time format, for example: 12:31 on May 8, 2018. Obviously, the data in time format cannot reflect the contribution of the week to the credit review model, resulting in a low accuracy of the credit review model constructed.

[0139] In order to solve the above problems, the embodiment of this application shows a data conversion method. For details, please refer to Figure 4 The technical solution shown in embodiment 3 and embodiment 2 has similar steps. T...

Embodiment 4

[0146] Usually some features and useless information with poor predictive ability of credit review results. In the process of model construction, using these useless information as the characteristics of model construction will undoubtedly increase the amount of data processing by the computer, reduce the bandwidth of the system, and resources. utilization rate.

[0147] In order to solve the above problems, the embodiment of the application shows a method for determining invalid information. For details, please refer to Figure 5 :

[0148] The technical solution shown in embodiment 4 has similar steps with the technical solution shown in embodiment 3. The only difference is that in the technical solution shown in embodiment 3, the step of deleting useless features in the data set and obtaining the data to be processed include:

[0149] S1021111 Statistically predicting the response value of the prediction result in the data set, the non-response value of the prediction res...

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Abstract

The embodiment of the invention discloses a credit review model construction method, device and system, according to the scheme provided by the embodiment of the invention, the historical data is segmented and screened in advance;, some modeling individuals with high missing rate and some characteristics with relatively poor prediction capability are removed; the remaining data is taken as a feature candidate set; based on an ensemble learning model of Logistic Regression, the credit review model is constructed. According to the technical scheme, the timeliness and efficiency of credit reviewcan be greatly improved, the loan ability and repayment willingness of a borrower can be analyzed from all aspects and dimensions based on historical data, the malicious fraud phenomenon can be recognized, and the prediction accuracy and robustness are improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method, device and system for constructing a credit review model. Background technique [0002] With the continuous development of science and technology and the rapid rise of the Internet plus industry, Internet plus finance has also kept up with the pace of the times. Online lending has entered thousands of households. Borrowers can quickly collect funds. At the same time, investors and The guarantor can get a relatively satisfactory income, and social thinking is added to the platform to create a complete online financial ecosystem. Of course, with the continuous expansion of the platform business and the continuous increase in the number of users, and some criminals Crooked ideas to carry out fraudulent activities on the platform have caused many difficulties and losses to the operation of the platform. [0003] In order to reduce the loss of the platform, the pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/21G06Q40/02
CPCG06Q40/03
Inventor 解智郭汝元孙乐为张锋乔森庞敏辉邱慧
Owner 优轩(北京)信息科技有限公司