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AI for Financial Risk, Compliance and Regulatory Reporting: Production Systems and Architecture for Modern Banking
Paperback
Publisher: Independently Published
Published: Jul 25 2026
Pages: 586
Weight: 2.53
Height: 1.19 Width: 8.00 Depth: 10.00
Language: English
Models now move money, approve credit, flag financial crime, and file regulatory returns. When they fail, the cost is measured in enforcement actions, capital add-ons, and headlines. Yet most AI books for finance stop at concepts and demos. This one shows you how to build, govern, and run production AI systems inside a regulated bank - with working architecture, code, and the supervisory frameworks that make deployment survivable.
Through a complete running case study - Avon & Wessex Bank, a fictional UK mid-tier bank - you follow one institution's AI programme from first business case to an integrated risk platform: credit document analysis, agentic credit decisioning, regulatory knowledge systems, and the governance machinery that keeps supervisors satisfied.
INSIDE THIS BOOK- Production architectures, not toy examples: LLM pipelines with structured output and hallucination controls, multi-agent systems with LangGraph, RAG and GraphRAG for regulatory intelligence, and a companion GitHub repository.
- Regulator-ready governance: PRA SS1/23 model risk management, EU AI Act high-risk obligations and compliance calendar, DORA operational resilience, FCA Consumer Duty, Basel III/CRR3 - mapped to concrete controls, model cards, and deployment gates.
- Transparent ROI: a six-step methodology with every assumption stated - no black-box numbers - including full programme business cases and per-system NPV.
- Agentic AI done safely: autonomy levels, human-in-the-loop reviewer design, kill-switches as governed controls, token budgets, circuit breakers, and agent evaluation frameworks.
- Reading paths by persona: targeted routes for risk executives, engineers, compliance officers, and architects.
Part I - Foundations: the AI landscape for financial services, GenAI and LLMs, AI agents, RAG and knowledge systems, and AI governance.
Part II - Risk Domains: credit risk, market and trading risk, operational risk, liquidity risk, and model risk management for AI/ML.
Part III - Compliance: regulatory reporting automation, AML, KYC, and financial crime prevention.
Part IV - Enterprise Implementation: AI architecture, MLOps and LLMOps, and data infrastructure for risk analytics.
Part V - Integration: building the complete AI risk platform, end to end.
Chief risk officers, heads of model risk and compliance, risk and regtech engineers, ML practitioners in financial services, consultants, and supervisors who need AI systems that survive both production and inspection.
If AI is entering your risk function, this book shows you how to deploy it - and defend it.
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