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Introduction

Financial fraud is a pervasive issue that continues to challenge financial institutions worldwide. The advent of advanced Machine Learning and Deep Learning techniques has provided novel solutions to tackle this problem. Graph neural networks (GNNs) have shown promise in identifying fraudulent activities in financial networks due to their ability to model complex relationships and dependencies among entities. This thesis aims to explore the use of GNNs for fraud detection in financial networks, with a focus on improving the accuracy and efficiency of current fraud detection systems.

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 Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Introduction to Fraud Detection in Financial Networks
2.2 Traditional Approaches to Fraud Detection
2.3 Machine Learning Techniques for Fraud Detection
2.4 Deep Learning Techniques for Fraud Detection
2.5 Graph Neural Networks
2.6 Applications of GNNs in Financial Networks
2.7 Challenges and Limitations of GNNs
2.8 Gaps in Existing Literature
2.9 Future Directions in Fraud Detection Research
2.10 Summary of Literature Review

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection and Preprocessing
3.3 Feature Selection and Engineering
3.4 Model Selection and Training
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Validation Techniques
3.8 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Model Performance Evaluation
4.2 Comparison with Baseline Models
4.3 Interpretation of Results
4.4 Feature Importance Analysis
4.5 Robustness and Generalization of Model
4.6 Real-World Application and Implications
4.7 Recommendations for Future Research
4.8 Practical Implementation Challenges

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Industry and Policy
5.4 Limitations and Caveats
5.5 Conclusion and Future Research Directions

Thesis Overview on Graph Neural Networks for Fraud Detection in Financial Networks

Graph neural networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities in financial networks. By leveraging the complex relationships and dependencies among entities in financial transactions, GNNs can accurately identify suspicious patterns and anomalies. This thesis aims to investigate the effectiveness of GNNs for fraud detection in financial networks and propose novel approaches to enhance the performance of existing fraud detection systems.

In Chapter One, the introduction provides a comprehensive overview of the research topic, including the background of study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Additionally, key terms and definitions related to fraud detection and GNNs are provided to establish a common understanding of the topic.

Chapter Two presents a thorough literature review on fraud detection in financial networks, traditional approaches, machine learning and deep learning techniques, and the application of GNNs in financial fraud detection. This chapter also identifies gaps in existing literature and suggests future directions for research in the field.

Chapter Three outlines the research methodology, including research design, data collection and preprocessing, feature selection, model selection, evaluation metrics, experimental setup, validation techniques, and ethical considerations. The methodology section provides a detailed explanation of the experimental process and procedures used in the study.

In Chapter Four, the discussion of findings analyzes the performance of the GNN model, compares it with baseline models, interprets the results, assesses feature importance, evaluates model robustness and generalization, discusses real-world applications, proposes recommendations for future research, and addresses practical implementation challenges.

Chapter Five concludes the thesis by summarizing the findings, highlighting contributions to the field, discussing implications for industry and policy, acknowledging limitations and caveats, and proposing future research directions. The conclusion provides a comprehensive overview of the key findings and insights obtained from the study on GNNs for fraud detection in financial networks.

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