Artificial Immune Systems:A Predictive Model for credit scoring

Abstract

With the advent of the global financial crisis which hit the global economy, credit scoring has become of the essence. The global financial crisis also known as the “credit crunch” was largely attributed to the issuance of credit to individuals with no capacity to return the money. Credit scoring has become a very important task in the credit industry. Various credit scoring methods such in areas as artificial neural networks (ANNs), statistical based methods and decision trees have been proposed to increase the accuracy of credit scoring models. The proposed Artificial Immune Systems (AIS) are an artificial intelligence technique modelled on natural immune system processes have been used to solve various kinds of real life processes with success. In this paper we compare the performance of current classifiers used in credit rating against Artificial Immune Systems. Artificial Immune Systems have various algorithms used to implement them. The algorithm under consideration is the negative selection algorithm. Artificial Immune Systems (AIS) are found to be produce competitive results very close to traditional artificial intelligent systems such as Neural Network