Project Abstract

Abstract
The increasing volume of data in the banking sector has led to a growing interest in leveraging machine learning techniques for credit scoring applications. This research project explores the application of machine learning algorithms in credit scoring for banking institutions. The primary objective is to develop a predictive model that can accurately assess the creditworthiness of loan applicants based on historical data. The research begins with an in-depth examination of the current credit scoring practices in banking institutions and the limitations associated with traditional methods. A comprehensive review of relevant literature on machine learning applications in credit scoring provides insights into the latest trends and advancements in the field. The methodology section outlines the steps involved in building and evaluating the machine learning model, including data collection, preprocessing, feature selection, model training, and performance evaluation. Various machine learning algorithms such as logistic regression, random forest, and gradient boosting are compared and evaluated for their effectiveness in credit scoring. The findings from the study reveal that machine learning models outperform traditional credit scoring methods in terms of accuracy, efficiency, and predictive power. The discussion delves into the implications of these findings for banking institutions, highlighting the potential benefits of adopting machine learning technologies in credit risk assessment processes. In conclusion, this research project underscores the significance of incorporating machine learning techniques in credit scoring for banking institutions to enhance decision-making processes, reduce risks, and improve overall operational efficiency. The study contributes to the existing body of knowledge by demonstrating the practical applications and benefits of machine learning in the banking sector.

Project Overview