Application of Machine Learning in Credit Risk Assessment for Commercial Banks

Thesis Abstract

Abstract
The rapid advancement in the field of artificial intelligence and machine learning has revolutionized various industries, including banking and finance. One of the critical areas where machine learning techniques have shown significant promise is in credit risk assessment for commercial banks. This thesis explores the application of machine learning algorithms in enhancing the accuracy and efficiency of credit risk assessment processes within commercial banks. The introduction provides an overview of the study, highlighting the importance of credit risk assessment in the banking sector and the potential benefits of leveraging machine learning techniques. The background of the study delves into the historical context of credit risk assessment and the traditional methods employed by commercial banks. The problem statement identifies the limitations and challenges faced by banks in accurately assessing credit risk using conventional methods, thus necessitating the adoption of machine learning. The objectives of the study are to evaluate the effectiveness of machine learning algorithms in credit risk assessment, compare their performance with traditional methods, and propose a framework for integrating machine learning into existing credit risk assessment processes. The study also outlines the limitations and scope of the research, acknowledging potential constraints and focusing on commercial banks as the primary target for implementation. The literature review presents a comprehensive analysis of existing research on machine learning in credit risk assessment, highlighting the different algorithms, models, and methodologies employed in previous studies. Key themes explored include the advantages of machine learning over traditional methods, challenges in implementation, and best practices for integrating machine learning into credit risk assessment frameworks. The research methodology section outlines the approach taken to achieve the study objectives, including data collection methods, model development, and evaluation metrics. The study adopts a quantitative research design, utilizing historical credit data from commercial banks to train and test machine learning models for credit risk assessment. The discussion of findings chapter presents the results of the empirical analysis, comparing the performance of machine learning algorithms with traditional credit risk assessment methods. Key findings include the improved accuracy, speed, and scalability of machine learning models in predicting credit risk, highlighting their potential to enhance decision-making processes within commercial banks. In conclusion, this thesis summarizes the key findings, implications, and recommendations for commercial banks looking to adopt machine learning in credit risk assessment. The study underscores the transformative impact of machine learning on enhancing risk management practices and improving the overall efficiency and effectiveness of credit risk assessment processes within commercial banks.