Role-Based Programme
RB1675

AI-Powered Operational Risk Management

Intelligent Risk Assessment, Control Monitoring & Resilience 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 operational risk identification, assessment, monitoring, control evaluation, incident analysis, and management reporting.
  • Use AI to analyse risk registers, process documentation, incidents, loss events, controls, KRIs, audit findings, business continuity information, and management reports.
  • Apply AI-assisted analytics to identify recurring operational failures, emerging risk patterns, control weaknesses, process vulnerabilities, and areas requiring further investigation.
  • Build repeatable AI-assisted workflows for risk assessments, incident reviews, control monitoring, remediation tracking, scenario analysis, and executive reporting.
  • Design responsible AI-enabled Operational Risk workflows with appropriate controls for confidentiality, accuracy, explainability, auditability, approvals, and human oversight.

Tools covered

Generative AI AssistantsAI Search & ResearchDocument AISpreadsheet & Data AnalysisOperational Risk AnalyticsControl Assessment ToolsIncident & Loss Event AnalyticsScenario AnalysisReporting & Dashboard ToolsWorkflow Automation

Who should attend

  • Operational Risk Managers
  • Operational Risk Analysts
  • Enterprise Risk Managers
  • Risk & Control Analysts
  • Risk Managers
  • Business Risk Professionals
  • Internal Auditors
  • Control Assurance Professionals
  • Business Continuity Professionals
  • Process Risk Professionals
  • Governance Professionals
  • Risk Reporting Professionals
  • Compliance & Risk Professionals
  • Operational Resilience Professionals
  • Operational Risk Team Leads

Prerequisites & Participant Readiness

  • Working knowledge of operational risk, enterprise risk, internal controls, audit, or business processes
  • Familiarity with risk registers, incidents, controls, KRIs, loss events, process documentation, or remediation tracking
  • Basic proficiency with spreadsheets, documents, data interpretation, and workplace productivity applications
  • No previous AI course attendance required
  • No programming background required

TOC Modules

Concepts
  • Understanding Generative AI, analytical AI, Document AI, and workflow automation
  • Mapping AI opportunities across operational risk identification, assessment, monitoring, and reporting
  • Understanding AI assistance versus risk-owner and management judgement
  • Recognising model limitations, data-quality concerns, and explainability requirements
Practical activities
  • Mapping an existing Operational Risk lifecycle and identifying AI opportunities
  • Comparing manual and AI-assisted risk-management activities
  • Creating an AI opportunity matrix based on value, complexity, risk, and feasibility

Scenarios

Business Process to Operational Risk Assessment

Business Process → Risk Identification → RCSA → Control Mapping → Incident & Loss Analysis → KRI Design → Remediation Actions → Management Report

Participants use AI-assisted techniques to analyse a simulated business process, identify operational risks, assess controls, review incident information, define indicators, and prepare a structured risk-management report.

Operational Risk Data to Continuous Monitoring Workflow

Incidents + Loss Events + KRIs + RCSA Results + Third-Party Risks + Remediation Data → AI Analysis → Emerging Themes → Threshold Breaches → Automated Follow-Up → Dashboard → Risk Committee Reporting

Participants design an AI-enabled continuous Operational Risk monitoring workflow that consolidates risk information, highlights recurring issues and deteriorating indicators, improves action tracking, and supports executive reporting while retaining material risk decisions with authorised professionals.

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