Machine Learning application for selecting efficient Loan Applicants in Private Banks of Bangladesh

Abstract

Machine Learning Applications have been well accepted for various financial processes throughout the world. Supervised Learning processes for objective classification by Naïve Bayes classifiers have been supporting many definitive segregation processes. Various banks in Bangladesh have found challenging moments to identify financially and ethically qualified loan applicants. In this research process, we have confirmed the safe applicant’s list using definitive variable measures through identifiable questions. Our research process has successfully segregated the given applicants using Naïve Bayes classifier with the proof of lowering loan default rate from an average of 23.26%% to 11.76% and development of financial ratios as performance indicators of these banks through various financial ratios as indicators of these banks.