Thesis Abstract

Abstract
This thesis explores the utilization of machine learning techniques in credit scoring within the banking sector. The traditional credit scoring methods have limitations in accurately predicting creditworthiness, leading to potential risks for banks. Machine learning offers a promising solution by leveraging advanced algorithms to analyze vast amounts of data and identify patterns that can enhance credit risk assessment. Chapter 1 provides an introduction to the research study, presenting the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also includes a definition of key terms related to the research topic. Chapter 2 consists of a comprehensive literature review that examines existing studies, frameworks, and models related to credit scoring and machine learning applications in the banking industry. This chapter aims to provide a thorough understanding of the current state of credit scoring practices and the potential benefits of integrating machine learning algorithms. Chapter 3 outlines the research methodology employed in this study, detailing the research design, data collection methods, variables, sampling techniques, and the machine learning algorithms used for credit scoring analysis. The chapter also discusses the validation process and the evaluation criteria for assessing the performance of the machine learning models. Chapter 4 presents an in-depth discussion of the research findings obtained from applying machine learning techniques in credit scoring for banks. The analysis includes the comparison of traditional credit scoring methods with machine learning models, highlighting the advantages and challenges faced in implementing these advanced algorithms in a banking environment. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future research and practical applications. The conclusion also emphasizes the potential impact of machine learning in enhancing credit risk assessment for banks, improving decision-making processes, and mitigating financial risks. Overall, this thesis contributes to the existing body of knowledge by demonstrating the effectiveness of machine learning in credit scoring for banks and highlighting the opportunities for innovation and improvement in credit risk management practices. The findings of this study have practical implications for financial institutions seeking to enhance their credit assessment processes and optimize risk management strategies using advanced technology.

Thesis Overview

The project titled "Application of Machine Learning in Credit Scoring for Banks" focuses on the utilization of machine learning techniques in enhancing credit scoring processes within the banking sector. This research aims to address the increasing need for accurate and efficient credit risk assessment, a critical function for financial institutions in mitigating potential losses and ensuring sound lending practices. With the rise of big data and advancements in artificial intelligence, machine learning offers a promising approach to improving credit scoring models by leveraging complex algorithms to analyze vast amounts of data and predict creditworthiness more effectively. The project will begin with an introduction that provides an overview of the significance of credit scoring in banking, highlighting the challenges faced by traditional methods and the potential benefits of incorporating machine learning. The background of the study will explore the evolution of credit scoring techniques and the emergence of machine learning as a disruptive technology in the financial industry. The problem statement will outline the current limitations and inefficiencies in credit scoring processes, underscoring the need for innovative solutions to enhance accuracy and efficiency. The objectives of the study will be clearly defined to guide the research process, with a focus on developing and evaluating machine learning models for credit scoring applications. The limitations and scope of the study will be delineated to provide a clear understanding of the research boundaries and constraints. The significance of the study will be emphasized to underscore the potential impact of implementing machine learning in credit scoring on risk management practices, financial decision-making, and overall banking operations. The research methodology section will detail the approach and techniques used to design, implement, and evaluate machine learning models for credit scoring. It will include data collection methods, model development processes, feature selection techniques, model evaluation metrics, and validation procedures. The chapter will also discuss the dataset used for training and testing the models, as well as the tools and software employed for analysis. In the discussion of findings chapter, the research outcomes, insights, and implications of applying machine learning in credit scoring will be thoroughly examined. The performance of different machine learning algorithms, such as logistic regression, decision trees, random forest, and neural networks, will be compared and analyzed in terms of predictive accuracy, interpretability, and scalability. The impact of feature engineering, model tuning, and ensemble methods on credit scoring performance will also be discussed. Finally, the conclusion and summary chapter will consolidate the key findings, contributions, and recommendations of the study. It will highlight the potential benefits of integrating machine learning into credit scoring practices, such as improved risk assessment, reduced default rates, enhanced customer segmentation, and streamlined decision-making processes. The chapter will conclude with a reflection on the research outcomes and propose future directions for advancing the application of machine learning in credit scoring for banks.