Абстрактный

Adeptness Comparison between Instance Based and K Star Classifiers for Credit Risk Scrutiny

C. Lakshmi Devasena

Banking Industry is a significant source of finance. In Banking, Credit Risk assessment is a crucial and decisive task to sanction loans. Automation of decision making for sanctioning loans by analyzing the credit risk of customers using best algorithms and classifiers is of important need today. This work evaluates and compares the adeptness between Instance based classifier and K Star Classifier for credit risk assessment. German credit data is taken as a sample data for adeptness estimation. The performances of both classifiers are analyzed using machine learning tool and a practical guideline for selecting well suited classifier for credit risk assessment is presented. In addition, some diplomatic criteria for evaluating and relating best classifier are discussed.