SVM-based credit default prediction method under differential privacy

Abstract

The invention discloses an SVM-based credit default prediction method under differential privacy in the technical field of credit default. The SVM-based credit default prediction method comprises thefollowing steps of S1, preprocessing data; S2, carrying out variable selection; and S3, designing a weighted SVM optimization model under differential privacy according to a differential privacy serial combinatorial property. An effective solution is provided for a differential privacy SVM learning problem under data imbalance, and the solution can specifically solve the data imbalance problem faced when differential privacy SVM learning is used to predict customer default. The method is suitable for application scenes with data imbalance including credit card default prediction, such as disaster prediction, medical diagnosis and other fields, and is also suitable for the technical scheme of the invention.

Description

technical field [0001] The invention relates to the technical field of credit default, in particular to a credit default prediction method based on SVM under differential privacy. Background technique [0002] With the rapid development of social economy, more and more people use credit cards to achieve advanced consumption. While people are enjoying the consumption convenience brought by credit cards, more and more credit card debt problems also arise. Some people defaulted on their credit cards due to their inability to repay credit card debts in time, causing financial institutions and consumers to suffer huge economic losses, seriously disrupting the existing financial order, and hitting consumers' financial information. How to effectively identify potential credit card default customers and reduce the phenomenon of credit card default is a huge challenge for financial institutions in the process of risk control and management. As a classic machine learning method, SVM...

AI Technical Summary

Problems solved by technology

And in the context of differential privacy, the existing theory has the defect of ε-differential privacy insatiable when solving such problems, and cannot be directly applied to solve the problem of data imbalance through simple theoretical expansion.

This technical defect is a phased problem faced in the development of differential privacy SVM learning technology from theoretical research to practical application.

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