Fraudsheild AI Lab Course

Module 1: Day 1 – Fundamentals of Fraud in FinTech and Banking

• Explore common fraud types including identity theft, payment fraud, and money‑laundering.
• Identify digital transaction and API vulnerabilities within modern banking systems.
• Trace the evolution from rule‑based detection to AI‑driven solutions.

Module 2: Day 2 – Introduction to AI and Machine Learning in Fraud Prevention

• Understand supervised vs. unsupervised learning and key algorithms.
• Apply AI to real‑time transaction monitoring using behavioral biometrics.
• Review case studies such as JPMorgan’s credit‑card fraud reduction.

Module 3: Day 3 – Advanced AI Techniques for Anomaly Detection

• Implement autoencoders, isolation forests, and graph‑based models.
• Leverage NLP for anti‑money‑laundering sentiment analysis.
• Integrate blockchain and federated learning for secure data sharing.

Module 4: Day 4 – Implementation and Ethical Considerations

• Deploy models via APIs and cloud services such as AWS Fraud Detector.
• Mitigate bias, manage false positives, and ensure regulatory compliance.
• Adopt best practices for data privacy, model auditing, and scaling.

Module 5: Day 5 – Emerging Trends & Future Outlook

• Explore quantum‑resistant AI and generative‑AI fraud simulation.
• Assess AI’s role in DeFi, cross‑border fraud prevention, and zero‑trust architectures.
• Identify career pathways and ongoing research opportunities.