A new fuzzy support vector machine to evaluate credit risk

Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.

I. Introduction

Today, one of the most significant threats for many businesses, despite their size and the nature of their operations, is counterparts' credit risk. Extant evidence shows that in the past two decades bankruptcies and defaults have occurred at higher rates than at any time. Due to recent financial crises and regulatory concerns credit risk assessment is an area that has seen a resurgence of interest from both the academic world and the business community. Especially for credit-granting institutions, such as commercial banks and some retailers, the ability to discriminate faithful customers from bad ones is crucial. In order to enable the interested parties to take either preventive or corrective action, the need for efficient and reliable models that predict defaults accurately is imperative.