Thesis Abstract

Abstract
The use of machine learning in credit scoring has gained significant attention in the banking sector due to its potential to enhance decision-making processes and improve credit risk assessment. This thesis explores the application of machine learning techniques in credit scoring for banks, with a focus on improving accuracy, efficiency, and predictive capabilities. The study aims to address the limitations of traditional credit scoring models by leveraging advanced machine learning algorithms to analyze and predict creditworthiness. Chapter 1 provides an introduction to the research topic, detailing the background of study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of applying machine learning in credit scoring for banks. Chapter 2 presents a comprehensive literature review that examines existing studies, methodologies, and applications of machine learning in credit scoring. The review covers ten key areas, including traditional credit scoring models, machine learning algorithms, data preprocessing techniques, feature selection methods, model evaluation metrics, and regulatory considerations. Chapter 3 outlines the research methodology employed in this study, detailing the research design, data collection methods, sample selection criteria, variables, data preprocessing steps, machine learning algorithms used, model evaluation techniques, and ethical considerations. The chapter provides a transparent overview of the research process and methodology adopted to achieve the study objectives. Chapter 4 presents a detailed discussion of the findings obtained from applying machine learning techniques in credit scoring for banks. The chapter analyzes the performance of different machine learning algorithms in predicting credit risk, identifies key factors influencing credit scoring outcomes, and discusses the implications of using machine learning for credit assessment. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the study. The chapter highlights the significance of applying machine learning in credit scoring for banks, discusses the limitations and future research directions, and provides recommendations for enhancing credit risk assessment practices in the banking sector. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning in credit scoring for banks. By leveraging advanced algorithms and techniques, banks can improve credit risk assessment, enhance decision-making processes, and ultimately optimize lending practices. The findings of this study provide valuable insights for practitioners, researchers, and policymakers seeking to enhance credit scoring methodologies in the financial industry.

Thesis Overview

The project titled "Application of Machine Learning in Credit Scoring for Banks" focuses on the implementation of machine learning techniques in the credit scoring process within the banking sector. This research aims to explore how machine learning algorithms can enhance the accuracy and efficiency of credit scoring models used by banks to assess the creditworthiness of loan applicants. The traditional credit scoring methods used by banks often rely on manual assessment and predetermined rules, which may lead to subjective evaluations and potentially overlook important factors affecting credit risk. By integrating machine learning algorithms into the credit scoring process, this study seeks to leverage the power of data-driven analysis to improve credit assessment outcomes. The research will involve a comprehensive review of existing literature on credit scoring, machine learning, and their applications in the banking industry. This review will provide a theoretical foundation for understanding the current practices and challenges in credit scoring, as well as the potential benefits of incorporating machine learning techniques. The methodology for this research will include data collection from historical loan data, feature selection, model development using machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks. The performance of these models will be evaluated based on metrics such as accuracy, precision, recall, and F1 score to assess their effectiveness in credit scoring. The findings of this study are expected to demonstrate the advantages of using machine learning in credit scoring, including improved predictive accuracy, reduced manual intervention, and enhanced risk management capabilities for banks. The implications of these findings will be discussed in the context of the banking industry, highlighting the potential for wider adoption of machine learning technologies in credit assessment processes. Overall, this research aims to contribute to the ongoing evolution of credit scoring practices in the banking sector by showcasing the benefits and challenges of integrating machine learning into the credit evaluation process. The insights gained from this study will have implications for improving credit risk assessment, enhancing decision-making processes, and ultimately supporting more efficient and effective lending practices in the banking industry.