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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.
Highlights
Private Banks in Bangladesh has been facing serious challenges since the very beginning in terms of identifying appropriate loan applicants
The NPL within the growing banking sector of Bangladesh since the beginning of financial flexibility in Bangladesh has played as a positive inhibitor for financial independence (Rezina, 2020) but failed to effectively high-light correct loan provision policy
In this research paper we collectively focus on factors that are associated with human faults rather than honest mistakes in order to make the research process effective
Summary
Private Banks in Bangladesh has been facing serious challenges since the very beginning in terms of identifying appropriate loan applicants. Banks overall financial development through this process needs to be an important indirect factor for loan approval. Its shown by HM Sami that in case of rate along with higher rates of disapproval (Kemalbay effective asset selection both financial ratios and classification method seems important (Sami, 2021).
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