%0 Journal Article
%T Credit Risk Prediction Using Fuzzy Immune Learning
%A Ehsan Kamalloo
%A Mohammad Saniee Abadeh
%J Advances in Fuzzy Systems
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/651324
%X 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. 1. Introduction Banks and financial agencies employ credit scoring models extensively to determine good and bad credits. Loans are usually the most significant cause of risk in banks. Using credit scoring will reduce the time of loan approval procedure [1] and save cost per loan and enhance credit decisions. This enhancement helps lenders to guarantee that they are applying the same criteria to same groups of borrowers [2]. In these situations banks can supervise the existing loans much easier than before [3]. Because of the fast growth of autofinancing in the last two decades, the use of data mining for credit risk prediction increases rapidly [4¨C7]. The first investigation into credit scoring was started by Olson and Wu in 2010 to classify credit applications as good or bad payers [8]. Fair and Isaac presented a credit scoring model in the early 60s [9]. Since then, various models have been developed using traditional statistical methods such as discriminant analysis method in [10, 11]. Ordinary linear regression has also been used as another traditional statistic method for credit scoring [12, 13]. Recent techniques of credit risk assessment [14¨C20] treat lending decision problem as a binary classification problem [8]. The performance of bioinspired algorithms, like artificial neural networks and evolutionary computation, for various data mining problems has been demonstrated by many
%U http://www.hindawi.com/journals/afs/2014/651324/