Credit Risk Prediction Using Fuzzy Immune Learning

Abstract

The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.