Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in credit scoring for loan approval within the banking sector. Credit scoring is a critical process that helps financial institutions evaluate the creditworthiness of loan applicants and make informed decisions regarding loan approvals. Traditional credit scoring methods have limitations in terms of accuracy and efficiency, prompting the need for more advanced and automated approaches such as machine learning. The research begins with an examination of the background of credit scoring in the banking sector, highlighting the importance of accurate risk assessment in loan approval processes. The problem statement identifies the challenges faced by traditional credit scoring methods and the potential benefits of integrating machine learning algorithms. The objective of the study is to investigate the effectiveness of machine learning models in improving credit scoring accuracy and efficiency. The study acknowledges the limitations of the research, including data availability and model interpretability issues. The scope of the study focuses on the application of machine learning algorithms in credit scoring within a specific banking context. The significance of the research lies in its potential to enhance loan approval processes, mitigate risks, and improve financial inclusion by providing more accurate credit assessments. The structure of the thesis is outlined, detailing the chapters that will cover the introduction, literature review, research methodology, discussion of findings, and conclusion. Definitions of key terms related to credit scoring, machine learning, and banking are provided to ensure clarity and understanding throughout the thesis. The literature review delves into existing research on credit scoring models, machine learning applications in finance, and the benefits of using advanced algorithms for credit risk assessment. The research methodology section describes the data collection process, variable selection, model development, and evaluation metrics used to assess the performance of machine learning models in credit scoring. Findings from the study indicate that machine learning algorithms, such as decision trees, random forests, and neural networks, outperform traditional credit scoring models in terms of accuracy and predictive power. The discussion delves into the implications of these findings for the banking sector, highlighting the potential for improved loan approval processes and reduced default rates. In conclusion, this thesis underscores the value of leveraging machine learning techniques for credit scoring in the banking sector. By enhancing the accuracy and efficiency of credit assessments, financial institutions can make more informed lending decisions, reduce risks, and improve overall financial stability. The study contributes to the growing body of research on the application of machine learning in finance and underscores its potential to revolutionize credit scoring processes in the banking sector.

Thesis Overview