THE ROLE OF MACHINE LEARNING IN CREDIT RISK ASSESSMENT: EMPOWERING LENDING DECISIONS

Abstract

Credit risk assessment is a critical process in the lending industry that determines the likelihood of borrowers defaulting on their credit obligations. With the emergence of machine learning algorithms, credit risk assessment has experienced a transformative shift towards more accurate and data-driven approaches. This article explores the significant role of machine learning in credit risk assessment and its impact on lending decisions. The article begins by discussing the mathematical calculations and algorithms involved in data analysis, feature extraction, predictive modeling, risk scoring, and fraud detection. Key concepts such as correlation analysis, information gain, logistic regression, support vector machines, neural networks, and ensemble learning techniques are explained. Furthermore, the evaluation metrics used to assess model performance, hyper parameter tuning techniques for optimizing models, and the estimation of the probability of default are discussed. The article concludes by highlighting the importance of credit scores derived from machine learning models in assessing creditworthiness. By leveraging these mathematical calculations and algorithms, machine learning empowers lenders to make informed decisions, improves the accuracy of credit risk assessments, and provides borrowers with fairer access to credit opportunities.