Application of Machine Learning in Credit Scoring for Improved Risk Assessment in Banking

Thesis Abstract

Abstract
The banking industry plays a critical role in the economic system by facilitating financial transactions and providing credit to individuals and businesses. One of the key processes in banking is credit scoring, which involves assessing the creditworthiness of potential borrowers to determine the risk of default. Traditional credit scoring methods have limitations in accurately predicting credit risk, leading to potential financial losses for banks. This research project focuses on the application of machine learning techniques to enhance credit scoring for improved risk assessment in banking. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter highlights the importance of credit scoring in banking and the need for more accurate risk assessment methods to mitigate financial risks. Chapter Two presents a comprehensive literature review on credit scoring, machine learning algorithms, and their applications in the banking industry. The chapter explores existing studies and research findings related to credit risk assessment, machine learning models, and their effectiveness in improving credit scoring accuracy. Chapter Three outlines the research methodology employed in this study, including data collection methods, sample selection, variables, model development, and evaluation criteria. The chapter details the process of applying machine learning algorithms to credit scoring and explains the rationale behind the chosen methodology. Chapter Four presents a detailed discussion of the research findings, including the performance evaluation of machine learning models in credit scoring. The chapter analyzes the results, compares different algorithms, and discusses the implications of using machine learning for credit risk assessment in banking. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for the banking industry, and suggesting recommendations for future research. The chapter highlights the potential benefits of applying machine learning in credit scoring, such as improved accuracy, efficiency, and risk management. Overall, this research project contributes to the existing literature on credit scoring and machine learning in banking by demonstrating the effectiveness of advanced algorithms in enhancing risk assessment processes. The findings of this study have practical implications for banks and financial institutions seeking to improve their credit scoring systems and mitigate credit risks effectively.