Thesis Abstract

Abstract
Credit risk assessment is a critical process for banking institutions to evaluate the likelihood of borrowers defaulting on their loans. Traditional credit risk assessment methods often rely on historical data and static models, which may not capture the dynamic nature of credit risk. This thesis explores the use of predictive modeling techniques to enhance credit risk assessment in banking institutions. The objective of this study is to develop a predictive model that can accurately predict credit default risk by leveraging machine learning algorithms and big data analytics. Chapter 1 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. Chapter 2 presents a comprehensive literature review on credit risk assessment, predictive modeling techniques, machine learning algorithms, and their applications in the banking sector. The literature review also discusses the challenges and opportunities of using predictive modeling for credit risk assessment. Chapter 3 outlines the research methodology employed in this study, including data collection methods, data preprocessing techniques, feature selection, model development, and model evaluation. The chapter also discusses the selection of machine learning algorithms and the rationale behind their choice. Moreover, it describes the performance metrics used to evaluate the predictive model. In Chapter 4, the findings of the study are presented and discussed in detail. The chapter highlights the performance of the developed predictive model in predicting credit default risk compared to traditional credit risk assessment methods. It also analyzes the key factors influencing credit risk and provides insights into improving credit risk assessment accuracy. Chapter 5 concludes the thesis by summarizing the key findings, implications of the research, limitations, and future research directions. The study contributes to the existing literature by demonstrating the effectiveness of predictive modeling for credit risk assessment in banking institutions. The findings of this research can help financial institutions make more informed lending decisions and mitigate credit risk effectively. In conclusion, this thesis underscores the importance of leveraging predictive modeling techniques to enhance credit risk assessment in banking institutions. By developing a robust predictive model, banking institutions can improve their risk management practices, optimize lending decisions, and ultimately enhance financial stability.

Thesis Overview