Bank-to-public loan default discrimination method based on logistic regression

A logistic regression and discriminant technology, applied in the computer field, can solve problems such as inaccurate algorithms, high frequency of risk investigation, and short consideration period

Active Publication Date: 2021-08-20
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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  • Bank-to-public loan default discrimination method based on logistic regression
  • Bank-to-public loan default discrimination method based on logistic regression
  • Bank-to-public loan default discrimination method based on logistic regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

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

[0255] The invention provides a method for judging a bank's corporate loan default 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, extract the N indicators that have the greatest impact on the default rate according to the basic information of the enterprise and the financial indicators of the enterprise in the database;

[0258] The basic information of the enterprise includes: enterprise name, data year, whether it is in breach of contract, date of establishment, number of employees, total assets and the organization code of the borrower;

[0259] The financial indicators of the enterprise: main business income, cu...

Embodiment 2

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

[0269] The invention provides a method for judging a bank's corporate loan default 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, extract the N indicators that have the greatest impact on the default rate according to the basic information of the enterprise and the financial indicators of the enterprise in the database;

[0272] The basic information of the enterprise includes: enterprise name, data year, whether it is in breach of contract, date of establishment, number of employees, total assets and the organization code of the borrower;

[0273] The financial indicators of the enterprise: main business income, cu...

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 single-factor analysis of variance method of the present invention will be described in detail below.

[0289] Using the one-way analysis of variance method to extract the N indicators that have the greatest impact on the default rate includes the following steps:

[0290] One-way analysis of variance is carried out for each indicator independently 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 as the enterprise in the database;

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

[0296] To se...

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Abstract

The invention provides a bank-to-public loan default discrimination method based on logistic regression, and belongs to the technical field of computers. The default tendency coefficient of each industry is calculated by adopting a grey correlation method; several explanatory variables which have the largest influence on the explained variables are extracted through single-factor analysis, original variables extracted from the single-factor analysis, first m indexes with the variance cumulative contribution rate L reaching a preset threshold value are taken as default prediction indexes, each index is a linear combination of the original variables, the indexes are not related to one another. A binomial logistic regression model is established through a binomial logistic regression algorithm for default probability prediction, and financial customers with potential default risks are early warned and recognized in advance.

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 circles an...

Claims

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Application Information

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Patent Type & Authority Applications(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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