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Introduction

In recent years, the increase in online transactions has led to a rise in credit card fraud cases. This has necessitated the development of more sophisticated fraud detection systems to protect consumers and businesses from financial losses. Machine learning algorithms have been leveraged for credit card fraud detection due to their ability to detect patterns and anomalies in large datasets. However, the black-box nature of traditional machine learning models limits their interpretability, which can make it challenging for stakeholders to understand and trust the decisions made by these models.

Interpretable machine learning techniques offer a solution to this problem by providing transparent and understandable models that can be easily interpreted by humans. This thesis focuses on the application of interpretable machine learning for credit card fraud detection, with the goal of developing a model that not only accurately detects fraud but also provides explanations for its predictions.

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 card fraud detection
2.2 Machine learning applications in fraud detection
2.3 Interpretable machine learning techniques
2.4 Previous studies on interpretable machine learning for fraud detection
2.5 Explainable AI (XAI) in fraud detection
2.6 Comparison of interpretable and black-box models
2.7 Challenges in implementing interpretable machine learning for fraud detection
2.8 Evaluation metrics for fraud detection models
2.9 Regulatory requirements for fraud detection systems
2.10 Future trends in interpretable machine learning for fraud detection

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Model selection and validation
3.4 Interpretable machine learning techniques
3.5 Explainability methods
3.6 Performance evaluation
3.7 Ethical considerations
3.8 Implementation and deployment

Chapter 4: Discussion of Findings
4.1 Performance comparison of interpretable and black-box models
4.2 Interpretability of the models
4.3 Feature importance analysis
4.4 Case studies of fraud detection using interpretable models
4.5 Impact on business decisions
4.6 Limitations and challenges
4.7 Recommendations for future research
4.8 Implications for industry and policy

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Limitations and future research directions
5.5 Conclusion

Thesis Overview

Interpretable machine learning for credit card fraud detection is a critical area of research that aims to address the limitations of traditional black-box models in fraud detection systems. This thesis explores the application of interpretable machine learning techniques in the context of credit card fraud detection, with a focus on developing transparent and understandable models that provide explanations for their predictions.

The literature review discusses the current state of credit card fraud detection, machine learning applications in fraud detection, interpretable machine learning techniques, and previous studies on interpretable machine learning for fraud detection. It also examines challenges in implementing interpretable machine learning for fraud detection, evaluation metrics for fraud detection models, and future trends in the field.

The research methodology outlines the data collection and preprocessing process, feature selection and engineering techniques, model selection and validation methods, interpretable machine learning techniques, explainability methods, performance evaluation metrics, ethical considerations, and implementation and deployment strategies.

The discussion of findings chapter presents the performance comparison of interpretable and black-box models, the interpretability of the models, feature importance analysis, case studies of fraud detection using interpretable models, the impact on business decisions, limitations and challenges, recommendations for future research, and implications for industry and policy.

In conclusion, this thesis contributes to the field of interpretable machine learning for credit card fraud detection by proposing transparent and understandable models that provide explanations for their predictions. It also discusses the practical implications of these models, limitations, and future research directions.

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