Thesis Abstract

Abstract
The banking sector plays a crucial role in the economy by providing financial services and facilitating economic activities. Credit risk assessment is a key process in banking operations to evaluate the creditworthiness of borrowers and manage the risk of default. Traditional credit risk assessment methods often rely on historical data and subjective judgment, which may not always be effective in predicting credit risk accurately. In recent years, predictive modeling techniques have gained popularity in the banking sector as a more data-driven and objective approach to credit risk assessment. This thesis aims to develop a predictive modeling framework for credit risk assessment in the banking sector. The research will focus on leveraging machine learning algorithms and statistical techniques to analyze large volumes of data and identify patterns that can help predict credit risk more accurately. The study will utilize a dataset of historical loan information from a banking institution to train and test the predictive models. Chapter 1 provides an introduction to the research topic, background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on credit risk assessment in the banking sector, covering traditional methods, challenges, and the emergence of predictive modeling techniques. Chapter 3 outlines the research methodology, including data collection, preprocessing, feature selection, model building, and evaluation techniques. The chapter also discusses the selection of machine learning algorithms and statistical methods suitable for credit risk assessment. Chapter 4 presents a detailed discussion of the findings from the predictive modeling analysis. The chapter includes an evaluation of model performance, analysis of key factors influencing credit risk, and comparison with traditional credit risk assessment methods. Finally, Chapter 5 summarizes the research findings, conclusions, and implications for the banking sector. The study highlights the potential benefits of predictive modeling for credit risk assessment, such as improved accuracy, efficiency, and risk management. The thesis also discusses future research directions and practical applications of predictive modeling in the banking sector. Overall, this research contributes to the growing body of knowledge on predictive modeling for credit risk assessment in the banking sector and provides valuable insights for banks and financial institutions seeking to enhance their credit risk management practices.

Thesis Overview