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Discrimination Method of Bank's Corporate Loan Default Based on Logistic Regression

A technology of logistic regression and discriminant method, applied in the computer field, can solve problems such as small sample size, inability to meet the needs of business departments for risk investigation, and reduced work efficiency of business personnel

Active Publication Date: 2021-12-14
北京大唐神州科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] 1. There are defects such as small sample size, short consideration cycle and less consideration of regional factors in the default of corporate loans, which makes the prediction of default probability inaccurate, and the imprecise algorithm reduces the work efficiency of business personnel
[0007] 2. The algorithm used is single, which cannot meet the needs of business departments for real-time and dynamic risk investigation
[0008] 3. The setting of model and algorithm thresholds is unreasonable, and frequent warnings lead to too high frequency of risk investigation, which aggravates the burden of investigation for customers in the financial industry during the repayment period

Method used

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  • Discrimination Method of Bank's Corporate Loan Default Based on Logistic Regression
  • Discrimination Method of Bank's Corporate Loan Default Based on Logistic Regression
  • Discrimination Method of Bank's Corporate Loan Default Based on Logistic Regression

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0254] According to a specific embodiment of the present invention, the method for judging the default of a bank's corporate loan based on binomial logistic regression of the present invention will be described in detail below.

[0255] The invention provides a method for judging the default of a bank's corporate loan based on logistic regression, comprising the following steps:

[0256] Default predictor pre-extraction step,

[0257] Using one-way analysis of variance or multicollinearity verification method, according to the company's basic information and company financial indicators in the database, extract the N indicators that have the greatest impact on the default rate;

[0258] The basic information of the enterprise includes: enterprise name, data year, default or not, date of establishment, number of on-the-job employees, total assets and the borrower's organization code;

[0259] The financial indicators of the enterprise: main business income, current ratio, quic...

Embodiment 2

[0268] According to a specific embodiment of the present invention, the method for judging the default of a bank's corporate loan based on binomial logistic regression of the present invention will be described in detail below.

[0269] The invention provides a method for judging the default of a bank's corporate loan based on logistic regression, comprising the following steps:

[0270] Default predictor pre-extraction step,

[0271] Using one-way analysis of variance or multicollinearity verification method, according to the company's basic information and company financial indicators in the database, extract the N indicators that have the greatest impact on the default rate;

[0272] The basic information of the enterprise includes: enterprise name, data year, default or not, date of establishment, number of on-the-job employees, total assets and the borrower's organization code;

[0273] The financial indicators of the enterprise: main business income, current ratio, quic...

Embodiment 3

[0288] According to a specific embodiment of the present invention, the process of extracting the N indicators that have the greatest impact on the default rate by the one-way ANOVA method of the present invention will be described in detail below.

[0289] The one-way ANOVA method is used to extract the N indicators that have the greatest impact on the default rate, including the following steps:

[0290] One-way analysis of variance is carried out for each indicator and default or not, and the data in the set year is taken, and the value of the test statistic F is obtained according to the following formula:

[0291]

[0292] in,

[0293] k is the set year number;

[0294] n is the number of all companies in the same industry of the company in the database;

[0295] is the mean value of the index variable corresponding to each company in the same industry in the i-th year database;

[0296] is the total mean value of the index variable corresponding to each company...

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Abstract

The invention provides a method for judging default of a bank's corporate loan based on logistic regression, and belongs to the technical field of computers. The present invention adopts the gray correlation method to calculate the default tendency coefficient of each industry; adopts single factor analysis to extract several explanatory variables that have the greatest influence on the explained variable, and takes the variance cumulative contribution rate L of the original variable extracted from the single factor analysis to reach The first m indicators of the preset threshold are the default prediction indicators. Each indicator is a linear combination of the original variables, and the indicators are not correlated with each other. The binomial logistic regression model is established through the binomial logistic regression algorithm to predict the probability of default. Early warning and identification of financial customers with potential default risks.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method for judging defaults of bank corporate loans based on logistic regression. Background technique [0002] With the rapid development of the financial economy in recent years, loans have become an important support method for enterprises to raise funds, and the inevitable occurrence of corporate defaults has followed, and more and more frequent occurrences. Changes in the banking business structure and operating environment and the rapid development of financial innovation have prompted more and more banks to realize the need to build a more comprehensive and systematic credit risk management system to deal with the increasingly complex reality of financial risks. Therefore, the default risk of loan companies Measurement has great practical significance. [0003] At present, research on the measurement and evaluation of default probability in foreign academic and financ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/18G06Q10/06G06Q40/02
CPCG06F17/18G06Q10/06393G06Q40/03
Inventor 不公告发明人
Owner 北京大唐神州科技有限公司
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