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Introduction

The management of credit risk is vital in the banking industry, as loan defaults can have significant financial implications for financial institutions. In recent years, there has been a growing interest in using predictive modelling techniques to assess the creditworthiness of borrowers and predict the likelihood of loan defaults. This research project aims to investigate the use of financial data to predict loan defaults, with a focus on improving the accuracy and efficiency of credit risk assessment.

Chapter 1: 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 Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Credit Risk Management
2.2 Traditional Approaches to Credit Risk Assessment
2.3 Predictive Modelling in Credit Risk Assessment
2.4 Factors Influencing Loan Defaults
2.5 Machine Learning Techniques in Credit Risk Assessment
2.6 Big Data Analytics in Credit Risk Assessment
2.7 Data Preprocessing Techniques
2.8 Feature Selection Methods
2.9 Evaluation Metrics for Model Performance
2.10 Challenges and Limitations in Predicting Loan Defaults

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Engineering
3.5 Model Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Validation Techniques

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Feature Importance Analysis
4.3 Model Performance Evaluation
4.4 Comparison of Different Predictive Models
4.5 Interpretation of Results
4.6 Implications for Credit Risk Management

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Research
5.4 Conclusion

Thesis Overview:

Credit risk management is a critical aspect of banking operations, as loan defaults can have a significant impact on the financial stability of financial institutions. With the advancement of technology and the availability of vast amounts of financial data, there is a growing interest in using predictive modelling techniques to assess the creditworthiness of borrowers and predict the likelihood of loan defaults. This research project focuses on predicting loan defaults using financial data, with the aim of improving the accuracy and efficiency of credit risk assessment in the banking industry.

The thesis begins with an introduction that provides an overview of the research topic, followed by a discussion of the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. The literature review in Chapter 2 covers traditional approaches to credit risk assessment, predictive modelling techniques, factors influencing loan defaults, machine learning methods, big data analytics, data preprocessing techniques, feature selection methods, and evaluation metrics for model performance.

Chapter 3 outlines the research methodology, including research design, data collection, data preprocessing, feature engineering, model selection, training, evaluation, and validation techniques. Chapter 4 presents a detailed discussion of the findings, including descriptive analysis of data, feature importance analysis, model performance evaluation, comparison of different predictive models, interpretation of results, and implications for credit risk management. The thesis concludes with Chapter 5, which provides a summary of findings, contributions to the field, recommendations for future research, and a conclusion.

Overall, this thesis aims to contribute to the existing body of knowledge on credit risk management by exploring the use of financial data to predict loan defaults. By improving the accuracy and efficiency of credit risk assessment, financial institutions can make more informed decisions and mitigate the potential financial risks associated with loan defaults.

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