Human-AI Collaboration for Explainable Decision-Making in Financial Fraud Detection
Table Of Contents
<p><br>Table of Contents:<br><br>1. Introduction<br> - 1.1 Background and Motivation<br> - 1.2 Objectives of the Study<br> - 1.3 Scope and Significance<br> - 1.4 Research Questions<br> - 1.5 Methodology<br> - 1.6 Literature Review Overview<br> - 1.7 Structure of the Thesis<br><br>2. Literature Review<br> - 2.1 Evolution of AI in Financial Fraud Detection<br> - 2.2 Explainability in AI and Machine Learning<br> - 2.3 Human-AI Collaboration in Decision-Making<br> - 2.4 Ethical Implications in Financial AI Systems<br> - 2.5 Previous Approaches to Explainable Fraud Detection<br> - 2.6 Challenges and Opportunities in Explainable AI<br> - 2.7 Regulatory Compliance in Financial AI Systems<br><br>3. Financial Fraud Detection Techniques<br> - 3.1 Overview of Fraud Detection Models<br> - 3.2 Machine Learning Algorithms for Anomaly Detection<br> - 3.3 Feature Engineering for Financial Data<br> - 3.4 Real-time Monitoring and Adaptive Learning<br> - 3.5 Case Studies on Successful Fraud Detection Implementations<br> - 3.6 Integration with Fraud Prevention Systems<br> - 3.7 Future Trends in Financial Fraud Detection<br><br>4. Human-AI Collaboration Framework<br> - 4.1 Design Principles of Human-AI Collaboration<br> - 4.2 Interpretable Machine Learning Models<br> - 4.3 Visualization Techniques for Model Outputs<br> - 4.4 User Feedback and Iterative Model Improvement<br> - 4.5 Cognitive Ergonomics in Human-AI Interaction<br> - 4.6 Decision Support Systems for Fraud Analysts<br> - 4.7 Comparative Analysis of Explainable Models<br><br>5. Implementation and Evaluation<br> - 5.1 Development of Human-AI Collaborative System<br> - 5.2 Integration with Financial Institutions<br> - 5.3 Performance Metrics for Fraud Detection Accuracy<br> - 5.4 User Experience and Analyst Feedback<br> - 5.5 Ethical Considerations and Bias Analysis<br> - 5.6 Regulatory Compliance and Security Measures<br> - 5.7 Recommendations for Further Enhancements and Deployment<br><br><br></p>
Thesis Abstract
<p>Abstract<br>In response to the growing complexity of financial fraud landscapes, this research addresses the critical need for human-AI collaboration in decision-making for fraud detection. The study investigates the evolution of AI in financial fraud detection, the pivotal role of explainability in machine learning, and the ethical considerations inherent in financial AI systems. The literature review provides insights into previous approaches and challenges in creating explainable models. The core of the research involves the development and evaluation of a collaborative system, uniting human expertise with machine learning capabilities for enhanced fraud detection accuracy. The implementation and evaluation phases encompass integration with financial institutions, performance metrics, user experience, ethical considerations, and compliance with regulatory standards. The outcomes contribute to the ongoing discourse on designing effective and ethical human-AI partnerships in the complex realm of financial fraud detection.<br></p>
Thesis Overview
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