Thesis Abstract

Abstract
The banking sector plays a crucial role in the economy by facilitating financial transactions and managing risks associated with lending activities. One of the key challenges faced by commercial banks is assessing credit risk accurately to minimize loan default rates and optimize lending decisions. Traditional credit risk assessment methods often rely on historical data and statistical analysis, which may not capture the complex and dynamic nature of credit risk. To address this limitation, this study proposes the use of predictive modeling techniques based on machine learning algorithms to enhance credit risk assessment in commercial banking. 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 key definitions of terms. The chapter sets the foundation for the study by highlighting the importance of credit risk assessment in commercial banking and the potential benefits of leveraging machine learning algorithms for predictive modeling. Chapter Two presents a comprehensive literature review that explores existing research on credit risk assessment, machine learning algorithms, and their applications in commercial banking. The chapter examines various studies and methodologies related to credit risk modeling, highlighting the strengths and limitations of different approaches. By synthesizing the literature, this chapter provides a theoretical framework for the research study. Chapter Three details the research methodology employed in this study, including data collection methods, model development techniques, variable selection processes, model evaluation criteria, and validation procedures. The chapter outlines the steps taken to build and validate predictive models for credit risk assessment using machine learning algorithms, ensuring the rigor and reliability of the research findings. Chapter Four presents an in-depth discussion of the research findings, including the performance evaluation of the predictive models developed for credit risk assessment. The chapter analyzes the accuracy, interpretability, and predictive power of the machine learning algorithms in detecting and predicting credit risk in commercial banking. The findings are supported by empirical evidence and statistical analysis, providing insights into the effectiveness of predictive modeling for credit risk assessment. Chapter Five concludes the thesis by summarizing the key findings, implications, and contributions of the study. The chapter discusses the practical implications of using machine learning algorithms for credit risk assessment in commercial banking and offers recommendations for future research and industry applications. Overall, this study contributes to the advancement of credit risk assessment practices in commercial banking by leveraging predictive modeling techniques based on machine learning algorithms. In conclusion, this thesis underscores the importance of adopting innovative approaches to credit risk assessment in commercial banking and demonstrates the potential of machine learning algorithms for enhancing predictive modeling capabilities. By leveraging advanced technologies and data analytics, commercial banks can improve their risk management processes, make more informed lending decisions, and enhance overall financial performance.

Thesis Overview

The project titled "Predictive modeling for credit risk assessment in commercial banking using machine learning algorithms" aims to leverage the power of machine learning techniques to enhance the credit risk assessment process in commercial banking. With the increasing volume of data available in the banking sector, traditional methods of credit risk assessment are proving to be inadequate in effectively evaluating the creditworthiness of borrowers. This research seeks to address this challenge by developing predictive models that can accurately predict credit risk based on a variety of factors using machine learning algorithms. The study will begin with a comprehensive literature review to examine the existing methods and models for credit risk assessment in commercial banking. By critically analyzing previous research in the field, the project aims to identify the limitations of current approaches and explore the potential of machine learning algorithms to overcome these challenges. This review will provide a theoretical foundation for the research and guide the selection of appropriate methodologies and algorithms for the predictive modeling process. The research methodology will involve collecting and analyzing relevant data from commercial banking institutions to train and test the predictive models. Various machine learning algorithms, such as decision trees, random forests, and neural networks, will be applied to the dataset to build predictive models for credit risk assessment. The performance of these models will be evaluated based on metrics such as accuracy, precision, recall, and F1 score to determine their effectiveness in predicting credit risk. The findings of the study will be presented and discussed in detail in Chapter Four of the thesis. This chapter will provide a comprehensive analysis of the predictive models developed, highlighting their strengths and weaknesses in assessing credit risk in commercial banking. The discussion will also explore the implications of the research findings for the banking industry and suggest potential areas for future research and development. In conclusion, this research project seeks to contribute to the advancement of credit risk assessment practices in commercial banking by harnessing the capabilities of machine learning algorithms. By developing accurate and reliable predictive models, the study aims to help banking institutions make more informed lending decisions and mitigate the risks associated with credit defaults. The outcomes of this research have the potential to enhance the efficiency and effectiveness of credit risk assessment processes, ultimately benefiting both banks and borrowers in the financial ecosystem.