Thesis Abstract

Abstract
The banking sector plays a crucial role in financial stability, economic growth, and overall societal well-being. One of the key challenges faced by banks is managing loan default risk effectively to maintain financial health and sustainability. With the advent of advanced technologies, particularly machine learning algorithms, there is an opportunity to enhance the predictive capabilities of banks in assessing and managing loan default risk. This thesis investigates the application of machine learning algorithms in predicting loan default risk in the banking sector. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the stage for understanding the importance of predicting loan default risk and the role of machine learning algorithms in this context. Chapter 2 presents a comprehensive review of the existing literature related to loan default risk prediction, machine learning algorithms, and their applications in the banking sector. The chapter synthesizes and analyzes previous research studies, providing insights into the current state of knowledge and identifying gaps that the present study aims to address. Chapter 3 details the research methodology adopted in this study. It includes discussions on the research design, data collection methods, data preprocessing techniques, feature selection, model selection, model evaluation, and validation procedures. The chapter outlines the steps taken to develop and validate machine learning models for predicting loan default risk. In Chapter 4, the findings of the study are presented and discussed in detail. The performance of various machine learning algorithms in predicting loan default risk is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The chapter also explores the factors influencing loan default risk and how machine learning models can help banks make informed decisions to mitigate this risk effectively. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the study. It also discusses the practical implications of using machine learning algorithms for predicting loan default risk in the banking sector and suggests areas for future research to further enhance the predictive accuracy and effectiveness of such models. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in predicting loan default risk in the banking sector. By leveraging advanced technologies, banks can enhance their risk management practices, improve decision-making processes, and ultimately foster financial stability and sustainability in the industry.

Thesis Overview