Thesis Abstract

Abstract
The banking and finance industry plays a vital role in the global economy, with credit scoring being a fundamental aspect of lending decisions. Traditional credit scoring methods have limitations in accurately assessing creditworthiness, leading to potential risks for financial institutions. This research focuses on the application of machine learning techniques in credit scoring for banks to enhance the accuracy and efficiency of credit risk assessment. Chapter One provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter establishes the foundation for the study and highlights the importance of leveraging machine learning in credit scoring for banks. Chapter Two presents a comprehensive literature review on credit scoring, machine learning algorithms, and their applications in the banking sector. The chapter explores existing studies, models, and methodologies related to credit scoring and machine learning in financial institutions, providing a theoretical framework for the research. Chapter Three details the research methodology employed in this study, including data collection methods, sampling techniques, model development, and evaluation criteria. The chapter outlines the steps taken to implement machine learning algorithms for credit scoring and explains the rationale behind the chosen methodologies. Chapter Four presents the findings of the research, analyzing the performance of machine learning models in credit scoring for banks. The chapter discusses the accuracy, efficiency, and predictive power of the models, highlighting their strengths and limitations in comparison to traditional credit scoring methods. Chapter Five offers a conclusion and summary of the research findings, emphasizing the implications of using machine learning in credit scoring for banks. The chapter discusses the practical significance of the research outcomes, recommendations for future studies, and potential applications of machine learning in enhancing credit risk assessment processes. In conclusion, this research contributes to the advancement of credit scoring practices in the banking sector by demonstrating the effectiveness of machine learning techniques in improving credit risk assessment. The findings of this study have implications for financial institutions seeking to enhance their lending decisions through innovative technologies, ultimately leading to more informed and accurate credit evaluations.

Thesis Overview

The project titled "Application of Machine Learning in Credit Scoring for Banks" aims to explore the integration of machine learning techniques in the credit scoring process within the banking sector. Credit scoring plays a crucial role in determining the creditworthiness of individuals and businesses, enabling banks to make informed decisions regarding loan approvals and interest rates. Traditional credit scoring models rely on statistical methods and historical data, which may have limitations in capturing complex patterns and trends in credit behavior. Machine learning offers a promising approach to enhance credit scoring by leveraging algorithms that can analyze vast amounts of data, identify patterns, and make accurate predictions. By utilizing machine learning models, banks can potentially improve the accuracy of credit assessments, reduce the risk of default, and enhance overall decision-making processes. This research project seeks to investigate the effectiveness of machine learning algorithms, such as neural networks, decision trees, and random forests, in predicting credit risk and improving credit scoring outcomes. The research will involve collecting and analyzing a diverse dataset of credit-related information, including borrower demographics, financial history, loan characteristics, and repayment behavior. By applying machine learning techniques to this dataset, the study aims to develop predictive models that can assess credit risk more accurately than traditional methods. The project will also explore the interpretability of machine learning models in the context of credit scoring, considering the importance of transparency and explainability in banking decisions. Through a comprehensive review of existing literature on credit scoring, machine learning applications in finance, and related methodologies, this research will establish a foundation for understanding the current state of the field and identifying gaps for further investigation. The methodology will involve data preprocessing, feature selection, model training, evaluation, and validation to assess the performance of machine learning algorithms in credit scoring applications. Overall, this project seeks to contribute to the growing body of knowledge on the application of machine learning in credit scoring for banks, with the ultimate goal of enhancing credit risk assessment practices, improving loan portfolio management, and promoting financial inclusion. By leveraging advanced technologies and analytical tools, banks can potentially transform their credit assessment processes and adapt to the evolving landscape of the financial industry."