Credit Risk Analysis Using Fuzzy Logic with Machine Learning Models

Author(s)	G.Arutjothi, C.Senthamarai
Country	India
Abstract	Credit Risk is an important issue in the Banking Industry. Credit risk Prediction and assessment of credit is a difficult task for credit managers. Banking Industry has large amount of data related to the behavior of the customer and their credit history, but this raw data is not useful for making correct judgment in credit decisions. The banking industry is need of a correct credit decision making system, to distinguish between good customers and default customers. The data mining domain is suitable for assessing credit risk and making good decisions on credit. Feature selection is one of the main pre-processing step in the data mining. This paper use FuzzyRoughSetTheory (FRST) for finding feature subset. Four other feature selection methods are used to find the optimal feature subset. The four feature selection methods are Information Gain, Relief, Chi-Squared and Wrapper subset model. These different feature selection methods are compared in terms of accuracy and efficiency.
Keywords	Credit Risk, Feature Selection, FuzzyRoughSetTheory(FRST)
Field	Computer Applications
Published In	Volume 5, Issue 3, May-June 2023
Published On	2023-05-31
Cite This	Credit Risk Analysis Using Fuzzy Logic with Machine Learning Models - G.Arutjothi, C.Senthamarai - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.3298
DOI	https://doi.org/10.36948/ijfmr.2023.v05i03.3298
Short DOI	https://doi.org/gr97tk

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