Thesis Abstract

Abstract
In the realm of commercial banking, the assessment of credit risk plays a pivotal role in maintaining financial stability and sustainability. Traditional methods of credit risk assessment have limitations in accurately predicting potential defaults, leading to significant financial losses for banks. To address this challenge, the utilization of machine learning algorithms for predictive modeling has emerged as a promising solution. This thesis focuses on exploring the application of machine learning algorithms in developing a predictive model for credit risk assessment in commercial banking. The research begins with a comprehensive introduction that outlines the background of the study, the problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two delves into a thorough literature review, encompassing ten key areas related to credit risk assessment, machine learning algorithms, and their integration in commercial banking practices. Chapter three details the research methodology, including the research design, data collection methods, sampling techniques, variable selection, model development, and evaluation criteria. The methodology section also discusses the ethical considerations and limitations encountered during the research process. Chapter four presents an in-depth discussion of the research findings, highlighting the performance and effectiveness of the developed predictive model in assessing credit risk. The chapter analyzes the results obtained through the application of machine learning algorithms and compares them with traditional credit risk assessment methods. Furthermore, the implications of the findings on commercial banking practices and risk management strategies are discussed. Finally, chapter five offers a conclusive summary of the research, emphasizing the significance of predictive modeling in enhancing credit risk assessment in commercial banking. The chapter also discusses the implications of the research findings, recommendations for future studies, and the overall contribution of the thesis to the field of banking and finance. Overall, this thesis contributes to the existing body of knowledge by demonstrating the potential of machine learning algorithms in improving credit risk assessment practices in commercial banking. The research findings provide valuable insights for financial institutions seeking to enhance their risk management processes and mitigate potential financial risks associated with credit defaults.

Thesis Overview

The project titled "Predictive modeling for credit risk assessment in commercial banking using machine learning algorithms" aims to leverage advanced machine learning techniques to enhance the process of credit risk assessment in the commercial banking sector. Credit risk assessment is a critical aspect of banking operations, as it involves evaluating the likelihood of a borrower defaulting on a loan or credit agreement. Traditional methods of credit risk assessment often rely on historical data and predefined rules, which may not fully capture the complexities and dynamics of modern financial markets. Machine learning algorithms offer a promising solution by enabling banks to analyze large volumes of data, identify patterns, and make more accurate predictions regarding credit risk. By applying predictive modeling techniques, this project seeks to develop a more robust and efficient credit risk assessment framework that can help banks improve their lending decisions, minimize default risks, and enhance overall risk management practices. The research will begin with a comprehensive literature review to explore existing methodologies, models, and technologies related to credit risk assessment and machine learning in the banking sector. This review will provide a solid foundation for understanding the current state of the art and identifying gaps or opportunities for improvement in the field. The subsequent research methodology chapter will outline the specific steps and techniques that will be employed to develop and validate the predictive models for credit risk assessment. This will involve data collection, preprocessing, feature selection, model training, evaluation, and validation using real-world banking data sets. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be considered and compared to identify the most effective approach for credit risk prediction. The discussion of findings chapter will present the results of the predictive modeling experiments, including the performance metrics, accuracy levels, and comparative analysis of different algorithms. The findings will be critically evaluated to assess the strengths and limitations of the proposed models and provide insights into their practical implications for commercial banks. In the conclusion and summary chapter, the key findings, implications, and contributions of the research will be summarized. Recommendations for future research directions and practical applications of the developed credit risk assessment models will be discussed to guide further advancements in this important area of banking and finance. Overall, this project seeks to advance the field of credit risk assessment in commercial banking by harnessing the power of machine learning algorithms to create more effective and efficient predictive models. The outcomes of this research have the potential to benefit banks, financial institutions, regulators, and borrowers by improving risk management practices, enhancing decision-making processes, and ultimately contributing to a more stable and resilient financial system.