Thesis Abstract

Abstract
The banking sector has undergone significant transformations with the rapid advancements in technology, particularly in the field of machine learning. One area that has seen substantial benefits from these innovations is credit scoring, a crucial aspect of retail banking that determines the creditworthiness of customers. This thesis explores the application of machine learning algorithms in credit scoring within the context of retail banking. Chapter One provides an introduction to the research topic, presenting a background of the study, defining the problem statement, outlining the objectives of the study, discussing the limitations and scope of the research, highlighting the significance of the study, and presenting the structure of the thesis along with defining key terms. Chapter Two delves into a comprehensive literature review, analyzing existing studies, and research findings related to machine learning algorithms in credit scoring in retail banking. This chapter aims to provide a solid theoretical foundation for the research study. Chapter Three focuses on the research methodology employed in this study. It includes details on the research design, data collection methods, sampling techniques, data analysis tools, and ethical considerations. The chapter also discusses the limitations and potential biases of the chosen methodology. Chapter Four presents the findings of the research study, showcasing the results obtained from applying various machine learning algorithms to credit scoring in retail banking. The chapter includes a detailed analysis of these findings, highlighting the strengths and weaknesses of different algorithms in predicting creditworthiness. Chapter Five serves as the conclusion and summary of the thesis. It encapsulates the key findings of the research, discusses the implications of the results, and provides recommendations for future research in this area. This chapter also emphasizes the practical significance of utilizing machine learning algorithms for credit scoring in retail banking and the potential impact on improving decision-making processes in the industry. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in credit scoring within the retail banking sector. The findings of this research hold implications for financial institutions looking to enhance their credit risk assessment processes and improve their overall efficiency and accuracy in decision-making.

Thesis Overview

The project titled "Utilizing Machine Learning Algorithms for Credit Scoring in Retail Banking" aims to explore the application of machine learning algorithms in the context of credit scoring within the retail banking sector. Credit scoring is a critical process used by financial institutions to assess the creditworthiness of potential borrowers, enabling them to make informed decisions regarding loan approvals and interest rates. Traditional credit scoring methods rely on predetermined rules and historical data, which may not fully capture the complexity and variability of individual credit profiles. Machine learning algorithms offer a promising alternative by leveraging advanced computational techniques to analyze large volumes of data and identify patterns that may not be apparent through traditional methods. By utilizing machine learning algorithms, banks can enhance the accuracy and efficiency of their credit scoring processes, leading to better risk management, improved customer experience, and increased profitability. This research overview will delve into the key components of the project, including the background of the study, problem statement, objectives, methodology, findings, and conclusions. The study will begin with an introduction to the significance of credit scoring in retail banking and the potential benefits of incorporating machine learning algorithms into this process. The background of the study will provide a comprehensive overview of existing literature on credit scoring and machine learning in banking. The problem statement will highlight the limitations of traditional credit scoring methods and the challenges faced by banks in accurately assessing credit risk. The objectives of the study will outline the specific goals and research questions that the project aims to address, such as evaluating the performance of machine learning algorithms in credit scoring and identifying best practices for implementation in retail banking. The methodology section will detail the research design, data collection methods, and analytical techniques used to evaluate the effectiveness of machine learning algorithms in credit scoring. This will include a discussion of the types of algorithms selected, the data sources utilized, and the evaluation metrics employed to measure the performance of the models. The findings section will present the results of the analysis, including the accuracy levels, predictive power, and efficiency of the machine learning algorithms in credit scoring. This will involve a detailed discussion of the model outputs, insights gained from the data, and comparisons with traditional credit scoring methods. Finally, the conclusions will summarize the key findings of the study, discuss their implications for retail banking, and provide recommendations for future research and practical implementation. Overall, this research aims to contribute to the growing body of knowledge on the application of machine learning algorithms in credit scoring and provide valuable insights for banks seeking to enhance their risk management processes.