Credit risk index prediction method and device

Abstract

The embodiment of the invention provides a credit risk index prediction method and device. The method comprises the steps that: credit risk indexes and corresponding to-be-selected bank macroscopic indexes are determined; data quality analysis and correlation analysis are performed on the credit risk indexes and the to-be-selected bank macroscopic indexes, and target macroscopic indexes in the to-be-selected bank macroscopic indexes are determined according to analysis results of the data quality analysis and the correlation analysis; and a linear regression prediction model is constructed according to the credit risk indexes and the target macroscopic indexes, and credit risk index prediction data are obtained according to the linear regression prediction model. According to the method and device of the invention, credit risk indexes in the financial risk control field can be effectively and accurately predicted.

Description

technical field [0001] This application relates to the field of financial risk control, in particular to a method and device for predicting credit risk indicators. Background technique [0002] After the financial crisis in 2008, the role of stress testing increased rapidly, and regulators put forward many new stringent requirements for bank stress testing. Industrial and Commercial Bank of China has met its third-tier standard and needs to carry out stress tests and submit reports on a regular basis), the BASEL committee updated the stress test principles in October 2018, and the China Banking and Insurance Regulatory Commission has proposed requirements for stress tests in various regulatory guidelines. As an important tool for banks to assess risks in a forward-looking manner and plan ahead for risk prevention, stress testing has been integrated into daily risk management. [0003] From the point of view of the coverage of the stress test, it has expanded from the origin...

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Problems solved by technology

[0004] Existing modeling schemes are mostly used in the pan-economic and financial fields, and are rarely used to model and predict credit risk indicators such as customer default rate, financial default rate, PD, LGD, etc. for specific

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