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**Introduction**

In the realm of financial transactions, anomaly detection plays a crucial role in identifying fraudulent activities and protecting the integrity of financial systems. Traditional fraud detection methods often rely on rule-based systems that are limited in their ability to adapt to evolving fraudulent tactics. In recent years, machine learning techniques, particularly unsupervised learning, have shown promise in enhancing the accuracy and efficiency of anomaly detection in financial transactions.

This thesis aims to explore the application of unsupervised learning algorithms in detecting anomalies in financial transactions. Specifically, the study will focus on identifying outliers that deviate significantly from the normal patterns of legitimate transactions. By leveraging the power of unsupervised learning, this research seeks to improve the detection of fraudulent activities and enhance the overall security of financial systems.

**Table of Contents**

**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 Introduction to Anomaly Detection in Financial Transactions
2.2 Traditional Methods vs. Machine Learning Techniques
2.3 Unsupervised Learning Algorithms
2.4 Applications of Unsupervised Learning in Anomaly Detection
2.5 Challenges in Anomaly Detection in Financial Transactions
2.6 Current Trends and Developments in the Field
2.7 Evaluation Metrics for Anomaly Detection
2.8 Case Studies in Anomaly Detection using Unsupervised Learning
2.9 Future Directions in Anomaly Detection Research
2.10 Summary of Literature Review

**Chapter 3: Research Methodology**
3.1 Research Design
3.2 Data Collection and Preprocessing
3.3 Feature Selection and Engineering
3.4 Unsupervised Learning Algorithms Selection
3.5 Model Training and Evaluation
3.6 Performance Metrics
3.7 Experimental Setup
3.8 Validation Techniques
3.9 Ethical Considerations
3.10 Data Privacy and Security Measures

**Chapter 4: Discussion of Findings**
4.1 Model Performance Evaluation
4.2 Comparison with Traditional Methods
4.3 Interpretation of Results
4.4 Limitations and Challenges
4.5 Insights and Implications
4.6 Recommendations for Future Research
4.7 Practical Applications and Implementation Strategies
4.8 Conclusion

**Chapter 5: Conclusion and Summary**
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations and Future Directions
5.5 Conclusion

This thesis aims to provide a comprehensive overview of anomaly detection in financial transactions using unsupervised learning. By examining the current state of the art, exploring innovative methodologies, and discussing practical implications, this research seeks to advance the field of fraud detection and contribute to the overall security of financial systems.

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