Utilizing Machine Learning for Credit Scoring in Retail Banking

Thesis Abstract

Abstract
The financial industry, particularly retail banking, has witnessed significant advancements in recent years with the incorporation of machine learning techniques in various aspects of operations. Credit scoring, a critical process in retail banking, plays a crucial role in assessing the creditworthiness of potential borrowers. Traditional credit scoring models have limitations in terms of accuracy and efficiency, thus motivating the exploration of machine learning algorithms for improved credit risk assessment. This thesis investigates the application of machine learning in credit scoring within the context of retail banking. The study begins with a comprehensive introduction that outlines the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. A detailed literature review in Chapter Two delves into existing research on credit scoring models, machine learning algorithms, and their applications in the banking sector. The review highlights the strengths and limitations of different approaches, providing a foundation for the empirical research conducted in this thesis. Chapter Three focuses on the research methodology employed in this study, including data collection methods, sample selection, variable identification, model development, and evaluation techniques. The methodology section also discusses the ethical considerations and limitations encountered during the research process. In Chapter Four, the findings of the empirical analysis are presented and discussed in detail. The study evaluates the performance of various machine learning algorithms in credit scoring, comparing their predictive accuracy, efficiency, and interpretability. The results shed light on the potential of machine learning models to enhance credit risk assessment in retail banking, offering insights for practitioners and policymakers. Finally, Chapter Five provides a comprehensive conclusion and summary of the thesis, highlighting the key findings, implications, and recommendations for future research. The study contributes to the existing literature by demonstrating the effectiveness of machine learning techniques in credit scoring and their potential to revolutionize risk management practices in retail banking. Overall, this thesis underscores the importance of leveraging machine learning for credit scoring in retail banking, emphasizing the need for continuous innovation and adaptation to meet the evolving demands of the financial industry. By embracing advanced technologies and analytical tools, banks can enhance their credit risk assessment processes, ultimately leading to more informed lending decisions and improved financial stability.