Machine Learning Applications for Credit Risk Assessment in Banking

Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in the domain of credit risk assessment within the banking industry. The growing complexity of financial transactions and the increasing volume of data have made traditional credit risk assessment methods less effective. Machine learning algorithms have shown promise in improving the accuracy and efficiency of credit risk assessment processes. This research aims to investigate the effectiveness of machine learning models in predicting credit risk and enhancing decision-making in banking institutions. The study begins with an introduction that outlines the background of credit risk assessment in banking, highlighting the limitations of traditional methods and the need for more advanced techniques. The problem statement identifies the challenges faced by banks in accurately assessing credit risk and the potential benefits of leveraging machine learning algorithms. The objectives of the study include evaluating the performance of machine learning models in credit risk assessment, identifying key factors influencing credit risk, and exploring the implications for banking practices. The literature review in Chapter Two provides a comprehensive overview of existing research on credit risk assessment, machine learning applications in finance, and relevant theoretical frameworks. Key themes explored in the literature review include the types of credit risk, traditional credit scoring models, the evolution of machine learning in banking, and the advantages and limitations of using machine learning for credit risk assessment. Chapter Three outlines the research methodology, detailing the data collection process, model selection criteria, feature engineering techniques, and evaluation metrics used to assess the performance of machine learning algorithms. The study employs a dataset of historical credit data from a financial institution to train and test different machine learning models, such as logistic regression, random forest, and neural networks. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of each machine learning model is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results show that certain machine learning algorithms outperform traditional credit scoring models in predicting credit risk, demonstrating the potential for improved risk assessment practices in banking. Finally, Chapter Five summarizes the key findings of the study, discusses the implications for banking institutions, and offers recommendations for future research in the field of credit risk assessment using machine learning. The study contributes to advancing the understanding of the applications of machine learning in banking and provides valuable insights for practitioners and researchers seeking to enhance credit risk management practices.