Leveraging Machine Learning for Creditworthiness Assessment in Banking

Karthika Gopalakrishnan

Abstract: Credit scoring is a critical aspect of banking operations, facilitating informed lending decisions and risk management. Traditional credit scoring methods, while effective, often exhibit limitations in adaptability and predictive accuracy. This paper explores the role of machine learning (ML) techniques in enhancing credit scoring processes within the banking sector. We begin by elucidating the significance of credit scoring for banks, followed by an analysis of conventional credit scoring methodologies and their associated shortcomings. Subsequently, we delve into the application of ML algorithms to credit scoring, highlighting their potential to address existing limitations and improve predictive performance. Furthermore, we present a case study utilizing a sample dataset to demonstrate the efficacy of ML - based credit scoring models. Finally, we discuss future research directions and potential advancements in the field of credit scoring using ML.

Keywords: Credit scoring, Machine learning, Banking, Predictive modeling, Risk management

How to Cite?: Karthika Gopalakrishnan, "Leveraging Machine Learning for Creditworthiness Assessment in Banking", Volume 12 Issue 7, July 2023, International Journal of Science and Research (IJSR), Pages: 2284-2288, https://www.ijsr.net/getabstract.php?paperid=SR24531144804, DOI: https://dx.doi.org/10.21275/SR24531144804