Project Abstract

Abstract
The banking sector plays a crucial role in the global economy by providing financial services, including loans, to individuals and businesses. However, one of the main challenges faced by banks is the issue of loan defaults, which can have significant financial implications. In recent years, advancements in technology, particularly in the field of machine learning, have provided banks with new tools to better predict and manage loan defaults. This research project aims to explore the use of machine learning algorithms in predicting loan defaults in the banking sector. Chapter One Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Research 1.9 Definition of Terms Chapter Two Literature Review 2.1 Overview of Loan Defaults in Banking Sector 2.2 Traditional Methods of Predicting Loan Defaults 2.3 Machine Learning Algorithms in Banking 2.4 Previous Studies on Loan Default Prediction 2.5 Factors Influencing Loan Defaults 2.6 Importance of Predicting Loan Defaults 2.7 Challenges in Loan Default Prediction 2.8 Comparison of Machine Learning Algorithms 2.9 Evaluation Metrics for Loan Default Prediction 2.10 Future Trends in Loan Default Prediction Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection 3.3 Data Preprocessing 3.4 Feature Selection 3.5 Model Selection 3.6 Model Training 3.7 Model Evaluation 3.8 Validation Techniques Chapter Four Discussion of Findings 4.1 Descriptive Analysis of Dataset 4.2 Performance Comparison of Machine Learning Algorithms 4.3 Feature Importance Analysis 4.4 Interpretation of Model Results 4.5 Recommendations for Improving Loan Default Prediction 4.6 Implications for Banking Sector 4.7 Future Research Directions Chapter Five Conclusion and Summary In conclusion, this research project sheds light on the use of machine learning algorithms in predicting loan defaults in the banking sector. By leveraging advanced analytical techniques, banks can enhance their risk management processes and improve decision-making regarding loan approvals. The findings of this study have practical implications for banks seeking to minimize loan defaults and optimize their lending practices. Overall, the research contributes to the growing body of knowledge on the application of machine learning in the banking sector and provides valuable insights for future research in this area.

Project Overview