Application of Machine Learning in Credit Scoring for Improved Risk Assessment in Retail Banking

Thesis Abstract

Abstract
The banking industry is constantly evolving, and the adoption of advanced technologies such as machine learning has revolutionized the way financial institutions assess credit risk. This thesis explores the application of machine learning in credit scoring to enhance risk assessment in retail banking. The research aims to investigate the effectiveness of machine learning algorithms in predicting credit risk and improving decision-making processes within the banking sector. Chapter 1 provides an introduction to the study, including the background of the research, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to credit scoring, machine learning, risk assessment, and retail banking. In Chapter 3, the research methodology is detailed, outlining the approach, research design, data collection methods, sampling techniques, variables, and data analysis procedures. The chapter also discusses ethical considerations and limitations encountered during the research process. Chapter 4 delves into a thorough discussion of the findings obtained from applying machine learning algorithms to credit scoring in retail banking. The analysis includes a comparison of traditional credit scoring methods with machine learning models and evaluates the performance metrics to assess the predictive accuracy and efficiency of the algorithms. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies. The conclusion highlights the significance of using machine learning in credit scoring for improved risk assessment in retail banking and emphasizes the potential benefits for financial institutions in enhancing their decision-making processes. Overall, this thesis contributes to the existing literature by demonstrating the practical applications of machine learning in credit scoring within the context of retail banking. The findings of this research offer valuable insights for banks and financial institutions seeking to leverage advanced technologies to mitigate credit risk, improve lending practices, and optimize their overall financial performance.