Paper Title :Credit Risk Prediction Using Machine Learning Analytics: An Ensemble Model

Author :Trishita Saha, Saroj Kumar Biswas, Saptarsi Sanyal, Binoti Patro, Biswajit Purkayastha

Article Citation :Trishita Saha ,Saroj Kumar Biswas ,Saptarsi Sanyal ,Binoti Patro ,Biswajit Purkayastha , (2023 ) " Credit Risk Prediction Using Machine Learning Analytics: An Ensemble Model " , International Journal of Management and Applied Science (IJMAS) , pp. 79-83, Volume-9,Issue-9

Abstract : The occurrence of the likelihood that a borrower would payback his debt to his lender (bank) is quite a challenging task. Various factors must be taken into consideration for deciding on advancing any type of loans. This decision making process requires sufficient quantitative data for computation purpose which is a very time consuming and involves a risk ofhuman errors. To overcome this tedious and hectic task, an expert system named Expert System for Credit Risk Prediction usingExtra-Trees (ESCRPET) has been proposed that uses acombination of oversampling technique,„Synthetic Minority Oversampling Technique‟ (SMOTE) and undersampling technique, „Edited Nearest Neighbor‟ (ENN) to deal with class imbalance problem,uses Boruta feature selection techniqueto select relevant features that are useful for model training and finally classification is done using Extra-Trees ensemble bagging technique. The performance of the proposed model is measured in terms of accuracy and f1-score using 10-folds cross validation and is compared with single classifier based models, ensemble models and various models present in literature. Keywords - Credit Risk, SMOTE, ENN, Boruta, Extra Tree

Type : Research paper

Published : Volume-9,Issue-9

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