Fund is the greatest variable of the Banking Industry. In Banking Industry achievement and disappointment depends on the credit. Keeping money Industries are focused today with increment in volume, speed and assortment of new and existing information. Managing and analyzing the massive data is more difficult. The credit scoring databases are often large and characterized by redundant and irrelevant features. With this features, classification methods become more difficult. This difficulty can be solved by using feature selection methods. The main objective of the feature selection is to reduce the size of dimensions, costs and increase the classification accuracy. This research paper uses a filter feature selection model for finding the optimal feature subset to evaluate the credit risk. The filter model is implemented using WEKA tool. Comparison study is made to find the credit risk assessment.
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Classification, Data Mining, Credit Risk, Feature Selection, Filter .