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.

Thesis Overview

The project titled "Utilizing Machine Learning for Credit Scoring in Retail Banking" aims to investigate and implement the application of machine learning techniques in the credit scoring process within the retail banking sector. This research seeks to address the limitations and challenges faced by traditional credit scoring methods by leveraging the power of machine learning algorithms to enhance accuracy, efficiency, and predictive capabilities in assessing creditworthiness. The research will begin with a comprehensive review of the existing literature on credit scoring in retail banking, highlighting the strengths and weaknesses of conventional methods and exploring the potential benefits of integrating machine learning approaches. This literature review will provide a solid foundation for understanding the current landscape of credit scoring practices and establishing the rationale for adopting machine learning techniques. The methodology chapter will outline the research design, data collection methods, and the selection of machine learning algorithms suitable for credit scoring applications. Various aspects such as data preprocessing, feature selection, model training, evaluation metrics, and validation techniques will be discussed in detail to ensure the robustness and reliability of the proposed credit scoring model. The findings chapter will present the results of the empirical analysis conducted to evaluate the performance of the machine learning-based credit scoring model. The discussion will focus on the comparative analysis of the machine learning model with traditional credit scoring methods, highlighting the improvements achieved in terms of accuracy, speed, and predictive power. The conclusion and summary chapter will provide a comprehensive overview of the research findings, discussing the implications of utilizing machine learning for credit scoring in retail banking. The study will conclude with recommendations for practitioners and policymakers on the adoption of machine learning technologies to enhance credit risk assessment processes and improve decision-making in the banking industry. Overall, this research project aims to contribute to the growing body of knowledge on the application of machine learning in the financial sector, specifically in the domain of credit scoring. By leveraging advanced analytical techniques, this study seeks to offer valuable insights and practical recommendations for enhancing credit risk management practices in retail banking institutions.