Role-Based Programme
RB1698

AI-Powered Fraud Risk & Financial Crime

Smarter Detection, Investigation & Control Monitoring

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Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Apply AI across Fraud Risk and Financial Crime activities including risk assessment, monitoring, anomaly identification, investigation support, control review, and reporting.
  • Use AI-assisted techniques to analyse transactions, documents, alerts, behavioural patterns, and case information more efficiently.
  • Develop structured workflows for fraud-risk identification, alert triage, investigation, evidence review, escalation, and remediation tracking.
  • Distinguish unusual activity and risk indicators from verified misconduct or confirmed fraud.
  • Apply responsible AI practices covering confidentiality, privacy, evidence integrity, legal escalation, bias, explainability, and human oversight.

Tools covered

Generative AI AssistantsFraud Risk AnalyticsTransaction AnalysisAnomaly DetectionDocument IntelligenceSpreadsheet AnalysisCase DocumentationRisk ScoringResearch AssistanceWorkflow Automation

Who should attend

  • Fraud Risk Professionals
  • Fraud Analysts
  • Financial Crime Professionals
  • Internal Auditors
  • Risk Analysts
  • Operational Risk Professionals
  • Financial Crime Investigators
  • AML & Financial Crime Support Professionals
  • Controls & Assurance Professionals
  • Governance, Risk & Compliance Professionals
  • Compliance Monitoring Professionals
  • Forensic Audit Professionals
  • Fraud Control Managers
  • Risk & Internal Audit Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of fraud risk, financial crime, audit, compliance, or control activities
  • Familiarity with transactional data, alerts, investigations, or risk-control processes is helpful
  • Basic spreadsheet and data-analysis skills
  • Basic awareness of Generative AI is helpful
  • No programming knowledge required

TOC Modules

Concepts
  • Understanding Generative AI, analytics, anomaly detection, and automation in fraud-risk management
  • Identifying AI applications across monitoring, investigation, documentation, and control assurance
  • Understanding AI assistance versus investigator, auditor, compliance, and management judgement
  • Recognising the difference between a risk signal, suspicious pattern, allegation, evidence, and confirmed misconduct
Practical activities
  • Mapping a Fraud Risk and Financial Crime lifecycle to AI-assisted activities
  • Identifying manual analytical and documentation activities suitable for AI support
  • Comparing traditional and AI-assisted fraud-risk workflows

Scenarios

Suspicious Transaction Pattern to Investigation Case

Transaction Data → AI-Assisted Anomaly Detection → Alert Triage → Evidence Review → Investigation Questions → Case Analysis → Escalation Decision → Case Report

Participants analyse sample transaction data, identify unusual patterns, prioritise alerts, organise supporting evidence, and prepare a neutral investigation summary without treating anomalies as proof of wrongdoing.

Fraud Incident to Control Improvement Plan

Fraud-Risk Event → Evidence → Root-Cause Review → Control Gap Analysis → Corrective Actions → Ownership → Monitoring → Management Report

Participants examine a simulated fraud-risk event, identify contributing control weaknesses, develop corrective actions, and prepare a structured management report focused on risk reduction and stronger preventive and detective controls.

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