Role-Based Programme
RB1699

AI-Powered Fraud Risk & Financial Crime

Intelligent Detection, Investigation & Compliance Analytics

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Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced capability to apply AI across fraud-risk management, financial-crime monitoring, investigation support, control assessment, and compliance reporting.
  • Use AI to analyse transaction data, alerts, customer information, case records, policies, investigation notes, and supporting documentation.
  • Apply AI-assisted analytics to identify unusual patterns, anomalies, relationships, behavioural changes, and potential risk indicators requiring further investigation.
  • Build repeatable AI-assisted workflows for alert triage, case documentation, evidence organisation, investigation support, escalation, and management reporting.
  • Design responsible AI-enabled Fraud Risk and Financial Crime workflows with appropriate controls for privacy, explainability, confidentiality, evidence integrity, regulatory compliance, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisFraud AnalyticsTransaction Monitoring AnalyticsAnomaly DetectionEntity & Relationship AnalysisCase Management AIReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Fraud Risk Managers
  • Fraud Analysts
  • Financial Crime Professionals
  • AML Professionals
  • Transaction Monitoring Analysts
  • Fraud Investigation Professionals
  • Risk Managers
  • Internal Auditors
  • Compliance Professionals
  • Financial Crime Investigators
  • KYC / CDD Professionals
  • Operational Risk Professionals
  • Risk & Control Analysts
  • Fraud Operations Professionals
  • Financial Crime Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of fraud risk, financial crime, AML, compliance, audit, or operational risk activities
  • Familiarity with transaction records, alerts, investigations, customer due diligence, or financial-crime controls
  • Basic proficiency with spreadsheets, data interpretation, documents, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, anomaly detection, and intelligent investigation support
  • Mapping AI opportunities across fraud prevention, monitoring, investigation, AML, and financial-crime controls
  • Understanding AI assistance versus investigator, Compliance, Risk, and management judgement
  • Recognising false positives, false negatives, bias, explainability, and model limitations
Practical activities
  • Mapping a Fraud Risk and Financial Crime workflow and identifying appropriate AI use cases
  • Comparing manual and AI-assisted monitoring and investigation activities
  • Creating an AI opportunity matrix based on value, risk, explainability, and feasibility

Scenarios

Suspicious Activity Alert to Investigation Case

Monitoring Alert → Transaction Analysis → Customer / Entity Review → Relationship Analysis → Evidence Collection → Investigation Questions → Case Summary → Escalation

Participants use AI-assisted analytics to investigate a simulated alert, organise transaction and entity information, identify anomalies and relationships, and prepare an evidence-based case summary for authorised review.

Fraud Data to Risk & Control Improvement Plan

Fraud Cases + Alerts + Loss Data + Control Information → AI Analysis → Recurring Patterns → Control Gaps → Priority Risks → Corrective Actions → Management Dashboard

Participants analyse sample fraud and financial-crime information to identify recurring risk patterns, evaluate control coverage, and prepare a structured risk-reduction and monitoring-improvement plan while retaining investigative and compliance decisions with authorised professionals.

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