Fraudsheild AI Lab Workshop

This course is designed for professionals in finance, tech, and security, providing a comprehensive overview of AI’s role in combating fraud. Each day consists of a 90-minute session (60 minutes of lecture/discussion + 30 minutes of lab assessment). Sessions assume basic knowledge of banking and tech; no advanced coding required, but labs use accessible tools like Python notebooks or simulation platforms (e.g., via Google Colab or pre-set environments). Labs include assessments via quizzes, practical exercises, and self-evaluations to reinforce learning.

FraudShield AI Lab is a hands-on learning platform for the “AI in Fraud Prevention” workshop. It lets participants experiment with AI-powered tools for transaction anomaly detection, phishing classification, identity verification, and voice biometrics, while following a structured curriculum supported by an AI tutor. Through interactive quizzes, real-world challenges, and rich visualizations (including 3D transaction network graphs), financial professionals, security analysts, developers, and students can build practical skills to design more secure, AI-driven financial systems.

📅 Day 1 – Fundamentals of Fraud in FinTech and Banking

• Introduction to fraud types: Identity theft, payment fraud, money laundering, and emerging threats like deepfakes
• Overview of FinTech and banking vulnerabilities: Digital transactions, APIs, and regulatory challenges (e.g., PSD2, GDPR)
• Historical evolution of fraud prevention: From rule-based systems to AI-driven approaches

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

• Basics of AI/ML: Supervised vs. unsupervised learning, key algorithms (e.g., decision trees, neural networks)
• AI in transaction monitoring: Real-time detection using features like behavioral biometrics and geolocation
• Case studies: How banks like JPMorgan use ML for credit card fraud reduction

📅 Day 3 – Advanced AI Techniques for Anomaly Detection

• Anomaly detection: Autoencoders, isolation forests, and graph-based analysis for network fraud
• NLP and AI in anti-money laundering: Sentiment analysis on communications and entity recognition
• Integration with blockchain and federated learning for secure data sharing

📅 Day 4 – Implementation and Ethical Considerations in AI Fraud Systems

• System integration: APIs, cloud deployment (e.g., AWS Fraud Detector), and hybrid human-AI workflows
• Challenges: Bias in AI models, false positives, and regulatory compliance (e.g., explainable AI under EU AI Act)
• Best practices: Data privacy, model auditing, and scaling for high-volume transactions

📅 Day 5 – Emerging Trends and the Future of AI in Banking Security

• Future trends: Quantum-resistant AI, generative AI for fraud simulation, and zero-trust architectures
• Global perspectives: AI in decentralized finance (DeFi) and cross-border fraud prevention
• Wrap-up: Career opportunities, ongoing research, and building resilient systems