Adeptness Evaluation of Memory Based Classifiers for Credit Risk Analysis

Banking industry is an important source of finance in any country. Credit Risk analysis is a critical and decisive task in banking sector. Loan sanction procedure can be followed based on the credit risk analysis of any customer. Automation of decision making in financial applications using best algorithms and classifiers is much useful. This work evaluates the adeptness of different Memory based classifiers on credit risk analysis. The German credit data have been taken for adeptness evaluation and is done using open source machine learning tool. The performances of different memory based classifier are analyzed and a practical guideline for selecting exceptional and well suited algorithm for credit analysis is presented. Apart from that, some discreet criteria for relating and evaluating the best classifiers are discussed.