Project Abstract

Abstract
The banking sector plays a crucial role in the economy by providing financial services to individuals and businesses. However, one of the significant challenges faced by banks is the assessment of credit risk to make informed lending decisions. Traditional methods of credit risk assessment are often time-consuming and may not effectively capture the dynamic nature of credit risk. In recent years, advancements in machine learning algorithms have revolutionized the field of credit risk assessment by enabling banks to leverage large volumes of data to predict credit risk more accurately and efficiently. This research project aims to explore the application of machine learning algorithms in predictive analysis of credit risk in banking. The study will focus on developing a predictive model that can assess the creditworthiness of borrowers based on various financial and non-financial factors. By utilizing historical data on loan applicants and their credit performance, the research will train machine learning algorithms to identify patterns and predict the likelihood of default. Chapter one provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. Chapter two presents a comprehensive literature review on credit risk assessment in banking, highlighting the evolution of machine learning techniques and their application in credit risk prediction. Chapter three details the research methodology, including the selection of data sources, data preprocessing techniques, feature selection, model selection, and evaluation criteria. The chapter also discusses the implementation of machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks for credit risk prediction. In chapter four, the research findings are presented and discussed in detail. The chapter analyzes the performance of the predictive model developed using machine learning algorithms and compares it with traditional credit risk assessment methods. The findings highlight the effectiveness of machine learning in improving the accuracy and efficiency of credit risk prediction in banking. Finally, chapter five provides a summary of the research findings, conclusions drawn from the study, implications for banking practices, and recommendations for future research. The research contributes to the existing body of knowledge on credit risk assessment in banking and provides valuable insights into the application of machine learning algorithms for predictive analysis of credit risk. In conclusion, this research project demonstrates the potential of machine learning algorithms in enhancing credit risk assessment in the banking sector. By leveraging advanced analytical techniques, banks can make more informed lending decisions, mitigate credit risk, and improve overall portfolio performance. The findings of this study have implications for risk management practices in banking and underscore the importance of adopting innovative technologies for effective credit risk management.

Project Overview