Performance Evaluation of Traditional Classifiers on Prediction of Credit Recovery

Abstract

In the era of Big Data, machine learning is an emerging technique to analyze the large volume of data and is used to make critical business decisions. It is broadly used in different area such as medical, telecom, social media, banking data analysis and so on to learn the data and perform predictive analysis as well as building recommendation system. With the progression of technology, data availability and computing power, most of the banks and financial institutions are adapting their business model with technological development. Credit risk analysis is a cardinal field for banking and financial institutions, and there are numerous credit risk technique exists to predict the creditworthiness of the customer and loan default probability. In this study, we explore credit defaulter dataset of Bangladeshi bank and conduct several traditional machine learning classifier to predict the delinquent clients who possessed the highest probability of short-term credit recovery. Furthermore, we perform feature engineering to identify the important features for credit recovery prediction. We then apply our final features on different machine learning classifier and compare the predictive accuracy with the other classifier. We observe that random forest classifier gives 90% accuracy in credit recovery prediction. Finally, we propose a noble strategy to identify the potential customer for recovering the credit amount by using supervised machine learning techniques.