Thesis Abstract

Abstract
Credit scoring is a critical process in banking that enables financial institutions to assess the creditworthiness of potential borrowers. Traditional credit scoring methods often rely on manual assessment and predefined rules, which may not fully capture the complexity of borrower behavior. This study explores the application of machine learning techniques in credit scoring, specifically focusing on the use of advanced algorithms to improve the accuracy and efficiency of credit risk assessment. The research begins with a comprehensive review of existing literature on credit scoring methodologies, highlighting the limitations of traditional approaches and the potential benefits of machine learning in this context. The study then presents the research methodology, including data collection procedures, feature selection techniques, model development, and evaluation metrics. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks are implemented and compared to identify the most effective approach for credit scoring. The findings of the study indicate that machine learning models outperform traditional credit scoring methods in terms of predictive accuracy and risk assessment. The results demonstrate the potential of machine learning to enhance credit scoring processes by leveraging large volumes of data and identifying complex patterns in borrower behavior. Furthermore, the study provides insights into the key factors influencing creditworthiness and the importance of feature selection in developing robust credit scoring models. In conclusion, the research highlights the significance of using machine learning for credit scoring in banking and the potential benefits of implementing advanced algorithms in credit risk assessment. The study contributes to the existing body of knowledge by demonstrating the effectiveness of machine learning techniques in improving the accuracy and efficiency of credit scoring processes. The findings of this research have implications for financial institutions seeking to enhance their credit risk management practices and make more informed lending decisions based on data-driven insights. Keywords Credit Scoring, Machine Learning, Banking, Credit Risk Assessment, Predictive Modeling, Feature Selection, Algorithm Comparison, Financial Institutions.

Thesis Overview