Comparison of machine learning methods in bad loan predictions: analysis of micro finance data
Keywords:
credit risk management, Machine Learning Algorithms, FinanceAbstract
Credit risk management is crucial for lending institutions to safeguard themselves against financial losses and maintain financial stability. Machine learning methods have been useful in analyzing borrower data and identifying bad loans that would be almost impossible for humans to detect. Client data from Microfinance Institution (MFI) in Uzbekistan have been used to build machine learning methods to predict loan delinquency. Their performances were evaluated based on five metrics: accuracy, sensitivity, specificity, negative (npv) and positive predictive value (ppv). The findings suggest that none of the machine learning used in the study methods have absolute advantage over the rest in all five-performance metrics. However, Extreme Gradient Boosting (XGBoost) produced the highest average performance compared to other methods.
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