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Credit score card model training method and taxpayer abnormal risk assessment method

A credit scoring and taxpayer technology, applied in data processing applications, digital data information retrieval, special data processing applications, etc., can solve problems such as monotonicity of binning results, achieve reasonable binning results, reduce tax losses, Avoid monotonic effects

Pending Publication Date: 2020-03-24
CHINA NAT SOFTWARE & SERVICE
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the standard WOE binning process usually uses equal frequency or equidistant method for binning, and different features have different optimal binning methods, and usually requires professionals to perform binning and merging work, and ensure that Monotonicity of binning results

Method used

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  • Credit score card model training method and taxpayer abnormal risk assessment method
  • Credit score card model training method and taxpayer abnormal risk assessment method
  • Credit score card model training method and taxpayer abnormal risk assessment method

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Experimental program
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Effect test

Embodiment Construction

[0042] This part describes in detail the specific implementation of the invention.

[0043] The optimal WOE binned credit scorecard model proposed by the present invention and the abnormal risk assessment method for taxpayers are mainly divided into figure 1 There are 9 steps in total from s1 to s9 shown.

[0044] The s1 step is sample selection, and the sample selection stage is mainly to determine the division of black and white samples and the time interval for sample selection. In the present invention, the main goal is to predict the risk of taxpayers becoming abnormal households, so taxpayers whose taxpayer status is abnormal are used as black samples, and taxpayers whose taxpayer status is normal are used as white samples. At the same time, according to the time when the taxpayer became an abnormal household, the historical period, observation period, and performance period are divided, and the samples of the historical period are used for model training, and the sampl...

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Abstract

The invention discloses a credit score card model training method and a taxpayer abnormal risk assessment method. The risk assessment method provided by the invention comprises the following steps of:s1, obtaining a training sample; s2, obtaining initial features; s3, serializing the category features; s4, selecting continuous feature initial binning points; s5, selecting a continuous feature optimal binning point; s6, ensuring the monotonicity of a continuous feature binning result, s7, selecting features with high predictability for feature selection, s8, training the risk model by using alogistic regression model, and s9, predicting the abnormal risk of the taxpayer by using the learned taxpayer abnormal risk model. According to the method, the prediction result of the model is improved, potential risk taxpayers can be found in advance, tax authorities are helped to control invoice receiving and invoicing behaviors of the risk taxpayers in advance, and the cost of false invoicingof enterprises made by criminals is increased.

Description

technical field [0001] The present invention relates to a credit score card model and a taxpayer's abnormal risk assessment method, specifically a method for calculating WOE binning by using information gain and automatically merging WOE binning according to the requirements of the credit score card model on the monotonicity of the binning results The credit score card model and taxpayer abnormal risk assessment method belong to the field of computer big data processing. Background technique [0002] The credit score card is a mature credit evaluation model, which is mainly used in financial risk prediction in the financial field. Generalized linear models for classification. Using mathematical statistics technology, through in-depth mining of personal and corporate basic information, credit conditions, personal connections, identity traits, asset status, behavior preferences and other data, it is found that there are massive data that can reflect personal or corporate risk...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q40/00G06F16/2458
CPCG06Q10/0635G06Q40/10
Inventor 刘宗前韩佶兴王彦武锦李雪峰付婷婷郭乐乐
Owner CHINA NAT SOFTWARE & SERVICE
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