Training positioning:
This programme is designed for AFCM member institutions that operate, regulate, supervise, intermediate or support capital markets. It therefore addresses both market infrastructure perspectives and broker/dealer compliance, conduct, supervisory and operational-risk perspectives. Agentic AI use cases are included as a distinct theme, with appropriate control boundaries.
• Understand practical AI and agentic AI applications in market surveillance, compliance, investigations and broker/dealer supervision.
• Assess where AI can reduce false positives, detect complex patterns, support case investigation and improve regulatory reporting.
• Identify AI-specific risks, including model risk, explainability limits, hallucination, bias, data leakage, cyber risk and over-reliance.
• Design practical AI governance arrangements aligned with capital markets expectations, three-lines-of-defense models and responsible AI principles.
• Apply an AI risk assessment template to real surveillance and broker/dealer compliance use cases.
• Develop a realistic implementation roadmap from pilot to controlled production deployment.
• Exchange/regulator/CSD perspectives versus broker/dealer perspectives
• From rules-based alerts to AI-assisted supervisory intelligence
• Key opportunity areas: market abuse, AML, communications surveillance, regulatory reporting, conduct risk and internal controls
2. AI Fundamentals for Surveillance & Compliance Professionals
• AI, machine learning, generative AI and agentic AI: practical distinctions
• Supervised, unsupervised and semi-supervised learning
• Anomaly detection, pattern recognition and predictive analytics
• Natural language processing for investigations, policies, alerts and communications
• Why AI supports, but does not replace, supervisory judgment
3. Core Use Cases: AI in Market Surveillance
• Detection of abnormal trading patterns and outlier behavior
• Spoofing, layering, wash trading and marking-the-close indicators
• Insider trading, information leakage and pre-announcement activity
• Pump-and-dump, coordinated activity and social-media driven manipulation
• Cross-market, cross-asset, cross-broker and cross-account correlation
• Alert prioritization and false-positive reduction
4. Broker/Dealer Compliance and Conduct Supervision
• AI for trade surveillance within broker/dealers and securities firms
• Suitability, best execution, churning, unauthorized trading and sales-practice risk indicators
• Client risk profiling, beneficial ownership signals and KYC refresh prioritization
• AML transaction monitoring, sanctions screening and suspicious activity triage
• Communications surveillance across email, chat, recorded calls and collaboration platforms
• Supervisory controls for branches, relationship managers, traders and sales teams
5. Agentic AI Use Cases in Surveillance & Compliance
• AI investigation copilot: summarizes alerts, drafts case chronologies and identifies missing evidence
• Regulatory reporting agent: prepares first drafts of recurring reports and exception explanations
• Policy and rule-mapping agent: maps new regulatory obligations to internal procedures and controls
• Member/broker supervision agent: compiles broker risk profiles from surveillance, compliance and operational data
• Evidence-gathering workflow agent: requests data, checks completeness, builds timelines and flags inconsistencies
• Control boundary: agentic AI should recommend, draft and route — not make enforcement decisions or execute irreversible actions without human approval
6. Data Foundations for Effective Surveillance AI
• Order book, trade, client, account, beneficial ownership, communications and market news data
• Data quality, lineage, timestamps, entity resolution and audit trails
• Local language, Arabic/English/Turkish/French data and regional market-structure challenges
• Confidentiality, privacy and cross-border data-transfer constraints
7. Interactive Case Study: Market Abuse and Broker Linkages
• Scenario: unusual price rise before a material announcement across multiple brokers and related accounts
• What rule-based systems detect versus what AI may add
• How to separate suspicious indicators from legitimate trading activity
• When to escalate, what evidence is needed and what human review must confirm
8. Day 1 Wrap-Up
• AI is strongest when it improves prioritization, correlation, investigation speed and control coverage
• Human accountability remains central for judgment, escalation and enforcement
Day 2 - Detailed Agenda
Day 2 theme: AI governance, risk management and responsible AI for exchanges, regulators, CSDs and broker/dealers.
1. Recap and Governance Transition
• Review of Day 1 use cases and where governance must be embedded
• AI risk as legal, conduct, operational, technology, cyber, model, reputational and regulatory risk
2. AI Risk Taxonomy for Capital Markets Institutions
• Model risk, data risk, bias, drift, explainability and validation limits
• False positives, false negatives and enforcement-quality evidence risk
• Generative AI hallucination, prompt-injection and confidential-data leakage
• Agentic AI risks: tool misuse, unauthorized actions, escalation failure and automated error propagation
• Vendor, outsourcing, cyber, resilience and business-continuity risks
• Special broker/dealer risks: client harm, unsuitable advice, unfair treatment, unauthorized trading and sales-practice issues
3. Responsible AI Principles
• Human accountability and clear decision ownership
• Transparency, explainability and contestability
• Fairness, non-discrimination and proportionality
• Data protection, confidentiality and lawful use
• Robustness, accuracy, resilience and auditability
• Human-in-the-loop, human-on-the-loop and human-in-command control models
4. AI Governance Framework for Exchanges, Regulators and Broker/Dealers
• Board and senior management accountability for AI adoption
• AI use-case inventory and risk classification
• Three lines of defense: business ownership, independent risk/compliance oversight and internal audit assurance
• Model approval, testing, validation, monitoring and periodic review
• Data governance, access control, retention and lineage
• Agentic AI approval framework: permitted tools, action limits, logs, escalation rules and kill-switches
• Vendor due diligence, service-level commitments, audit rights and exit planning
• Incident management, breach notification and regulatory engagement
5. Global Regulatory Landscape and Standards
• IOSCO perspectives on AI, market intermediaries and market conduct
• OECD AI Principles and risk-based governance
• EU AI Act relevance for financial services and high-risk systems
• NIST AI Risk Management Framework
• ISO/IEC 42001 AI Management System
• Model-risk-management references and implications for capital market institutions
• MENA/AFCM regional considerations: market maturity, language, data availability and supervisory capacity
6. Practical AI Risk Assessment Exercise
• Scenario: an exchange or broker/dealer implements an AI system to prioritize suspicious alerts and generate preliminary investigation summaries
• Participants assess purpose, data inputs, key risks, human oversight, controls, documentation and monitoring
• Outputs: short AI risk assessment summary and minimum governance checklist
7. Implementation Roadmap for AFCM Members
• Start with AI readiness, data readiness and governance readiness
• Prioritize high-value, controlled-risk use cases
• Build AI literacy for surveillance, compliance, legal, risk and technology teams
• Pilot before production; measure false-positive reduction, investigation time saved and control effectiveness
• Scale responsibly through governance gates, model monitoring and independent assurance
8. Closing Discussion and Key Takeaways
• AI can enhance market integrity, supervisory effectiveness and compliance efficiency
• Agentic AI creates powerful workflow opportunities but requires stronger guardrails
• Responsible adoption requires governance before scale
For registration and inquiries:
Sally Yassin
Sally.yassin@arab-exchanges.org