FutureCentral > Compliance, Risk, and AI Governance: A Guide for Compliance Professionals in Financial Services
Compliance, Risk, and AI Governance book cover
Finance, Investment & Risk · Working Professionals

Compliance, Risk, and AI Governance

A Guide for Compliance Professionals in Financial Services

Austin PM · FutureCentral Press

AI governance for financial-services compliance professionals.

Working ProfessionalsManuscript chapters available
Explore the bookInquire about this bookView contents
AudienceWorking Professionals
Structure33 chapters listed
StatusManuscript chapters available

About the book

Compliance teams interpret obligations, monitor activity, and document decisions. The book examines the data architecture and regulatory workflows behind that work, including the opportunities and limits of generative and agentic AI.

What you will learn

  • Map compliance and risk workflows
  • Evaluate compliance data architecture
  • Assess generative and agentic tools
  • Design documentation and accountability controls
Read before you decide

Explore the book

Chapter overview

Inside Chapter 1

Chapter topics, summarized from “The Inflection Point: Compliance and Risk in 2030”:

  • Map the responsibilities, regulators, and documentation of a financial-services compliance function.
  • Trace statistics, predictive models, and generative and agentic AI in compliance work.
  • Explain how each analytical approach changes regulatory workflows.

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Frameworks and decision tools

The Compliance and Risk Inflection Map

Introduced in Chapter 1.

The Cross-Wave AI Governance Stack

Introduced in Chapter 2.

The Compliance Data Architecture Stack

Introduced in Chapter 3.

The Build-Buy-Partner Decision Stack

Introduced in Chapter 4.

The Transaction Monitoring AI Stack

Introduced in Chapter 5.

The Screening-and-Surveillance AI Stack

Introduced in Chapter 6.

Table of contents

View all 33 chapters

Contents reflect available source documents. Final publication details remain unconfirmed.

  1. The Inflection Point: Compliance and Risk in 2030
  2. What AI Actually Does in Compliance and Risk: A Working Professional’s Field Guide
  3. Data Infrastructure and Data Governance for the Compliance Function
  4. The Strategic Logic of AI Adoption in Compliance and Risk: Build, Buy, Partner
  5. AI in Transaction Monitoring: From Rule-Based to Machine-Learning Systems
  6. AI in Sanctions Screening and Trade Surveillance
  7. AI in Trade-Based Money Laundering Detection
  8. AI in Suspicious Transaction Reporting and the FIU-IND Interface
  9. AI in AML for Virtual Digital Assets and the IFSCA Architecture
  10. AI in Identity Verification, Liveness Detection, and Video KYC
  11. AI in Beneficial Ownership Identification, PEP Screening, and Perpetual KYC
  12. AI in Market Surveillance and Insider-Trading Detection
  13. AI in Conduct Risk, Mis-Selling Detection, and Suitability Monitoring
  14. Cross-Vertical Conduct: Bancassurance, Integrated Wealth Platforms, and the Conduct Interface
  15. The Global AI Governance Landscape: EU AI Act, NIST AI RMF, ISO 42001, OECD, FSB/BIS/IOSCO/IAIS
  16. The Indian AI Governance Architecture: FREE-AI, SEBI June 2025 Consultation Paper on AI/ML, IRDAI Information and Cyber Security Guidelines, DPDP, CERT-In, IFSCA
  17. AI Policies, Ethics Committees, and the Operational Governance Machinery
  18. Algorithmic Fairness, Bias Testing, Explainability, and the Conduct Interface
  19. Cross-Border AI Governance: Global Subsidiaries in India, Indian Institutions Serving Global Customers, GIFT City
  20. Audit Committee and Board AI Oversight: The Governance-Layer-Above-the-Operational-Layer
  21. Model Risk Management for ML, Generative AI, and Agentic AI
  22. The MRM Function: Organizational Design, the Second Line of Defence, Model Inventory, and Challenger Models
  23. AI Audit and Assurance: Internal Audit’s New Mandate, the External Assurance Market, and the Supervisory Inspection of the AI Estate
  24. Model Validation and Performance Monitoring: Independent Validation, Materiality Tiering, and the Operating Cycle
  25. Generative AI Model Risk Management: Foundation Models, Retrieval Architectures, and the Discipline of Non-Determinism
  26. Agentic AI Model Risk Management: Autonomy Boundaries, Tool Use, and the Discipline of Bounded Action
  27. AI in Regulatory Reporting: From Automated Data Flow to CIMS, SupTech, and the Generative Narrative
  28. AI Operational Risk: Integration with the Operational Risk Discipline, AI-Specific Loss Events, and the Operating Architecture
  29. Third-Party and Vendor Risk Architecture for AI: Foundation Model Dependencies, Concentration Risk, and the Contractual Architecture
  30. Cyber Risk Architecture for AI-Enabled Institutions: Model Attack Surface, Supply Chain Risk, and the AI-Specific Incident Discipline
  31. Operational Resilience and Business Continuity for AI-Enabled Institutions: Recovery Architecture, Critical Operations Discipline, and the Resilience Operating Cycle
  32. The Working Professional’s Career Architecture in the AI Era: Skill Stack, Institutional Positioning, and the Trajectories of the Profession
  33. Looking Forward: Institutional Position-Taking on the Future of AI in Compliance and Risk Management
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