Thesis Abstract

Abstract
The banking sector faces significant challenges in managing credit risk due to the large volume and complexity of loan portfolios. In recent years, advancements in machine learning techniques have provided new opportunities to enhance credit risk assessment processes. This thesis investigates the application of machine learning in credit risk assessment within the banking industry. The study aims to explore how machine learning algorithms can improve the accuracy and efficiency of credit risk evaluation, ultimately leading to better decision-making and risk management practices. 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 literature review in Chapter Two delves into ten key areas related to machine learning, credit risk assessment, and their intersection in the banking sector. This comprehensive review sets the foundation for understanding the current state of research and practices in the field. Chapter Three outlines the research methodology, detailing the research design, data collection methods, sample selection, variables, data analysis techniques, and ethical considerations. The chapter also discusses the implementation of machine learning models for credit risk assessment and the evaluation criteria used to measure their effectiveness. In Chapter Four, the findings of the study are presented, analyzed, and discussed in detail. The chapter explores how different machine learning algorithms perform in credit risk assessment tasks and compares their accuracy, efficiency, and interpretability. The results shed light on the potential benefits and challenges of implementing machine learning in banking credit risk assessment. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing their implications for the banking industry, and offering recommendations for future research and practical applications. The study highlights the importance of leveraging machine learning in credit risk assessment to enhance risk management practices, improve decision-making processes, and ultimately foster a more stable and resilient banking sector. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning in credit risk assessment in banking. By exploring the potential of machine learning algorithms to revolutionize traditional credit risk evaluation methods, this research aims to provide valuable insights for financial institutions seeking to enhance their risk management practices and adapt to the evolving landscape of the banking industry.

Thesis Overview

The project titled "Application of Machine Learning in Credit Risk Assessment in Banking" aims to explore the utilization of machine learning techniques in enhancing credit risk assessment processes within the banking sector. Credit risk assessment plays a crucial role in the financial industry by evaluating the likelihood of a borrower defaulting on a loan. Traditional credit risk assessment methods often rely on historical data and predefined rules, which may not fully capture the complexities and dynamics of modern financial markets. Machine learning, a subset of artificial intelligence, offers the potential to revolutionize credit risk assessment by enabling the analysis of vast amounts of data to identify patterns and predict outcomes. This project seeks to investigate how machine learning algorithms, such as decision trees, random forests, and neural networks, can be applied to improve the accuracy and efficiency of credit risk assessment models. The research will begin by providing an introduction to the topic, discussing the background of credit risk assessment in banking, identifying the problem statement, outlining the objectives of the study, and specifying the limitations and scope of the research. The significance of the study will be highlighted to emphasize the potential impact of integrating machine learning in credit risk assessment practices. Subsequently, a comprehensive literature review will be conducted to explore existing studies, methodologies, and findings related to machine learning applications in credit risk assessment. This review will serve as a foundation for understanding the current landscape and identifying gaps that the project aims to address. The research methodology section will detail the approach, data sources, variables, and techniques that will be employed to develop and evaluate machine learning models for credit risk assessment. Various aspects, such as data preprocessing, model selection, and evaluation metrics, will be discussed to ensure the robustness and reliability of the research findings. The findings of the study will be presented and analyzed in the subsequent chapter, providing insights into the performance of different machine learning algorithms in credit risk assessment scenarios. The discussion will delve into the strengths, weaknesses, and implications of the models developed, offering recommendations for practical implementation in banking institutions. Finally, the conclusion and summary chapter will synthesize the key findings, contributions, and implications of the research. The project aims to contribute to the advancement of credit risk assessment practices in banking through the application of machine learning, ultimately enhancing decision-making processes and risk management strategies in the financial sector.