Predictive modeling for credit risk assessment in banking using machine learning algorithms

Thesis Abstract

Abstract
The banking sector plays a crucial role in the global economy by providing financial services, including lending activities that carry inherent risks. Credit risk assessment is a critical aspect of banking operations to evaluate the likelihood of borrowers defaulting on their loans. Traditional credit risk assessment methods have limitations in accurately predicting default risk, leading to potential financial losses for banks. In response to these challenges, this thesis explores the application of machine learning algorithms for predictive modeling in credit risk assessment within the banking industry. Chapter 1 introduces the research by providing an overview of the background of the study, highlighting the problem statement, setting the objectives, outlining the limitations and scope of the study, discussing the significance of the research, and detailing the structure of the thesis. This chapter also includes the definition of key terms related to credit risk assessment, machine learning, and predictive modeling. Chapter 2 presents a comprehensive literature review on credit risk assessment, machine learning algorithms, and their applications in the banking sector. The review covers ten key areas, including the evolution of credit risk assessment methods, the role of machine learning in finance, and the advantages and challenges of using machine learning for credit risk modeling. Chapter 3 focuses on the research methodology employed in this study. It details the research design, data collection methods, selection of variables, model development process, evaluation metrics, and validation techniques used to assess the performance of the predictive modeling approach. Additionally, it discusses ethical considerations and potential biases in the research process. Chapter 4 presents an in-depth discussion of the findings obtained from applying machine learning algorithms to credit risk assessment in banking. The chapter analyzes the predictive accuracy, model interpretability, feature importance, and overall performance of the developed models. It also examines the impact of different algorithm choices on the predictive capabilities of the models. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, highlighting the contributions to the field of banking and finance, and suggesting areas for future research. The chapter also provides recommendations for banks and financial institutions to enhance their credit risk assessment processes using machine learning models. In conclusion, this thesis contributes to the existing body of knowledge by demonstrating the effectiveness of machine learning algorithms in improving credit risk assessment in banking. The research findings highlight the potential for enhanced risk management practices, reduced default rates, and improved decision-making processes in the financial industry through the adoption of advanced predictive modeling techniques.