Project Abstract

Abstract
The banking industry relies heavily on accurate credit risk assessment to make informed decisions regarding loan approvals and risk management. Traditional methods of credit risk assessment are often time-consuming and prone to human error, leading to inefficiencies and potential financial losses for commercial banks. In recent years, the advent of machine learning algorithms has revolutionized the field of credit risk assessment, offering more efficient and accurate predictive modeling techniques. This research project explores the application of machine learning algorithms in predictive modeling for credit risk assessment in commercial banking. The primary objective is to develop a predictive model that can effectively assess the credit risk of loan applicants, thereby enabling banks to make more informed lending decisions. The research will focus on the implementation and evaluation of various machine learning algorithms, such as logistic regression, random forests, and support vector machines, to determine their effectiveness in predicting credit risk. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of terms. Chapter 2 presents a comprehensive literature review on credit risk assessment in commercial banking, highlighting the evolution of traditional methods and the emergence of machine learning algorithms in the field. Chapter 3 outlines the research methodology, detailing the data collection process, variable selection, model development, and evaluation techniques. The chapter also discusses the implementation of machine learning algorithms and the evaluation of model performance using metrics such as accuracy, precision, recall, and F1 score. Chapter 4 presents a detailed discussion of the findings obtained from the application of machine learning algorithms in credit risk assessment. The chapter examines the predictive performance of each algorithm, identifies key factors influencing credit risk, and discusses the implications of the findings on commercial banking practices. Chapter 5 concludes the research project by summarizing the key findings, discussing the implications for the banking industry, and providing recommendations for future research. The research contributes to the growing body of knowledge on the application of machine learning algorithms in credit risk assessment and provides valuable insights for commercial banks looking to enhance their risk management processes. Overall, this research project aims to demonstrate the potential of machine learning algorithms in improving credit risk assessment practices in commercial banking, ultimately helping banks make more informed lending decisions and mitigate financial risks.

Project Overview