A structured, high‑rigour programme for enterprise teams, risk functions, and leadership.
AI has changed the fraud landscape. Criminals now use AI to scale social engineering, identity theft, impersonation, and financial fraud. At the same time, organisations can use AI to detect anomalies, strengthen authentication, and reduce fraud losses. This course provides a comprehensive understanding of both sides.
Audience:
Enterprise staff, risk teams, compliance officers, fraud analysts, customer‑facing teams, IT/security teams, and senior leadership.
Duration:
4 hours (or 2 × 2‑hour sessions)
Learning Outcomes:
Participants will be able to:
Understand how AI is used in modern fraud attacks.
Recognise AI‑enabled threats such as deepfakes, synthetic identities, and automated scams.
Apply AI‑driven fraud detection and prevention techniques.
Strengthen organisational controls, governance, and reporting.
Build a culture of vigilance and resilience across the enterprise.
Four modules build from threat awareness to defensive strategy to governance.
This module explains how AI has changed the fraud landscape.
Key Topics
How AI accelerates fraud: speed, scale, automation
AI‑generated phishing and spear‑phishing
Deepfake audio and video impersonation
Synthetic identity creation
Automated credential‑stuffing and bot‑driven attacks
AI‑powered social engineering
Fraud‑as‑a‑service marketplaces
Enterprise Examples
Deepfake CEO voice used to authorise fraudulent payments
AI‑generated invoices mimicking suppliers
Synthetic identities used to open accounts
AI‑driven romance scams targeting customers
Automated refund fraud in retail
Learning Activities
Threat spotting: Identify AI‑generated phishing emails.
Case study: Analyse a real enterprise deepfake fraud incident.
Group discussion: “Where is our organisation most vulnerable?”
Take‑Home Actions
Review internal fraud awareness materials
Strengthen verification steps for high‑risk transactions
Encourage staff to report suspicious interactions early
This module focuses on how organisations can use AI to detect and prevent fraud.
Core Defensive Capabilities
Behavioural analytics for anomaly detection
Machine‑learning fraud scoring
Real‑time transaction monitoring
Device fingerprinting and risk‑based authentication
AI‑driven identity verification
Pattern recognition for insider threats
Automated alerting and triage
Enterprise Use Cases
Detecting unusual login patterns
Identifying account takeover attempts
Flagging anomalous financial transactions
Monitoring employee access behaviour
Preventing synthetic identity onboarding
Learning Activities
Hands‑on demo: Explore a sample fraud‑scoring dashboard.
Scenario workshop: Respond to a flagged high‑risk transaction.
Group challenge: Map AI tools to existing fraud controls.
Take‑Home Actions
Review fraud detection thresholds
Identify gaps in current monitoring systems
Strengthen cross‑team communication (fraud, IT, compliance)
This module ensures AI‑driven fraud prevention aligns with legal, ethical, and regulatory expectations.
Governance Principles
Accountability: humans remain responsible for decisions
Transparency: explainable AI in fraud scoring
Fairness: avoid discriminatory outcomes
Privacy: protect customer and employee data
Security: safeguard AI models from manipulation
Auditability: maintain logs and evidence trails
Regulatory Considerations
Data protection and privacy obligations
Financial crime compliance
AI governance frameworks
Model risk management
Reporting obligations for fraud incidents
Enterprise Risks
Over‑reliance on automated decisions
False positives harming customer experience
Model drift reducing accuracy
Data quality issues
Lack of human oversight
Learning Activities
Policy review: Evaluate an AI fraud policy for gaps.
Risk mapping: Identify governance risks in AI‑enabled fraud detection.
Scenario: Respond to a regulator’s request for explainability.
Take‑Home Actions
Add AI fraud models to risk registers
Review model governance documentation
Strengthen human‑in‑the‑loop controls
This module focuses on people, processes, and organisational readiness.
Cultural Priorities
Continuous fraud awareness training
Encouraging early reporting
Reducing stigma around “falling for scams”
Cross‑department collaboration
Leadership modelling vigilance
Operational Controls
Multi‑factor authentication
Segregation of duties
High‑risk transaction verification
Supplier and vendor validation
Incident response playbooks
Enterprise Scenarios
A staff member receives a deepfake call from a “senior leader”
A customer reports an AI‑generated scam
A supplier invoice is spoofed using AI
A synthetic identity bypasses onboarding checks
Learning Activities
Role‑play: Handling a deepfake impersonation attempt.
Tabletop exercise: Responding to a multi‑channel fraud attack.
Action planning: Build a fraud‑resilience roadmap.
Take‑Home Actions
Update fraud response playbooks
Strengthen staff verification protocols
Conduct regular fraud simulations
A structured assessment reinforces enterprise‑level competence.
15 multiple‑choice questions
3 scenario‑based questions
Group reflection on organisational vulnerabilities
Full attendance
Active participation
Completion of assessment
This training aims to:
Reduce fraud losses and operational risk
Strengthen fraud detection and prevention capabilities
Improve staff awareness and vigilance
Support compliance with regulatory expectations
Build a culture of fraud resilience
Enhance customer trust and organisational integrity