AI in Treasury Compliance & Fraud Monitoring

In an age of increasingly sophisticated financial crime, AI is essential for effective treasury compliance and fraud monitoring. This intensive course equips participants with advanced systems—covering AML screening, transaction anomaly detection, sanctions filtering, and identity verification. Using global best practices and real case scenarios, delegates will design AI enabled workflows that strengthen internal controls, regulatory compliance, and corporate resilience.

TRAINING TOPICS

• AI enabled AML and sanctions screening with KYC/KYD pipelines
• Machine learning for fraud detection: anomaly, check, and payment monitoring
• Transaction monitoring with real time alerting platforms (Feedzai, ThetaRay)
• Identity fraud detection: document scanning and deep identity verification (AU10TIX)
• Federated learning and Explainable AI for privacy preserving detection
• Sanctions & PEP screening via NLP and global watchlist matching
• Regulatory AI compliance and governance frameworks
• Voice deepfake and generative AI frauds: detection and mitigation
• Ethical considerations and systemic AI risk management

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Configure AI driven AML and sanctions monitoring systems
• Develop anomaly detection pipelines for transaction and payment fraud
• Evaluate identity verification platforms using machine learning
• Incorporate federated learning and XAI for compliant privacy solutions
• Implement AI sanction screening against global watchlists
• Use real time alert systems to monitor emerging fraud trends
• Apply frameworks for AI governance and compliance traceability
• Recognize and mitigate ethical and systemic AI risks

TRAINING IS TAILORED TO

• Treasury compliance officers and AML analysts
• Risk and operations managers in corporate treasury
• Financial crime, security, and internal audit teams
• RegTech and compliance solution leads
• Digital transformation and data science teams
• Legal and governance professionals in finance

TRAINING METHODOLOGY

• Expert demos of Feedzai, ThetaRay, AU10TIX, ComplyAdvantage
• Hands on labs: anomaly model building, federated learning setup
• Interpretability: XAI workshops with SHAP/LIME for auditability
• Case simulations: sanction screening, document fraud, deepfake voice
• Ethical roundtables: bias, explainability, and systemic controls
• Governance clinics: policy design, AI oversight frameworks

Day One: FOUNDATIONS OF AI IN COMPLIANCE & FRAUD DETECTION

• Overview: AML, sanctions, and fraud risk in treasury context
• Case studies: US Treasury fraud recovery, UK fraud surge
• Lab: simple anomaly detector using transaction data
• Tools demo: Feedzai & ThetaRay use cases

Day Two: IDENTITY & SANCTIONS RISK CONTROL

• Document fraud: scanning with AU10TIX
• Watchlist screening: ComplyAdvantage, global PEP lists
• Lab: build an identity and sanctions screening flow
• Discussion: balancing accuracy with privacy

Day Three: FEDERATED LEARNING & XAI

• Privacy first fraud detection: federated learning
• Introduce SHAP/LIME for model explainability
• Lab: federated model with XAI auditing
• Governance: audit trail and compliance documentation

Day Four: ADVANCED FRAUD: DEEPFAKE & VOICE PROTECTION

• Emerging threats: deepfake voice scams, AI fraud attacks
• Lab: detect anomalies in audio transaction streams
• Case study: multi channel fraud event simulation
• Strategies: cross team incident response protocols

Day Five: GOVERNANCE, ETHICS & SYSTEMIC RISK

• AI governance frameworks and risk registers
• Systemic AI risk: model concentration & bias
• Lab: design an AI governance schema with policy checks
• Final review: draft AI oversight roadmap & compliance plan