AI for Financial Risk, Compliance and Regulatory Reporting

Part I — Chapters 1–5

Foundations

Every production AI system rests on three pillars: a compelling business case, a well-governed LLM layer, and a shared technical foundation. Part I builds all three for AWB, ending with four registered models and a reusable awb_commons library used in every chapter that follows.

01AI Transformation

Foundations

AWB faces three converging pressures — regulatory complexity, cost, and AI-native competitors — that make this the decisive moment for AI adoption in UK banking. This chapter lays the foundation: the £3.2M AWB-AI-2026 programme business case, the six-step ROI methodology used throughout the book, and the UK/EU regulatory map every later system is built against.

📁 Folder pipeline.py

02GenAI & LLMs

MEDIUM

Introduces the transformer architecture and prompt-engineering patterns behind every LLM in the book, then builds the Credit Document Analyser (MR-2026-035) — cutting financial data extraction from up to 90 minutes to 18 minutes per document at 97.3% accuracy, fully PRA SS1/23 and EU AI Act compliant.

📁 Folder extractor.py

03AI Agents

HIGH

Extends the Document Analyser into a full agentic workflow — the Credit Decision Agent (MR-2026-037) — orchestrating four sub-agents into a Basel III IRB/CRR3-compliant credit recommendation. A parallel Treasury Operations Agent cuts the morning cash-positioning cycle from 6 hours to 25 minutes, for £2.1M/year combined saving.

📁 Folder agentic_ai_patterns.py

04RAG Systems

LOW

Builds the Regulatory Knowledge Assistant (MR-2026-038), grounding answers across 847 PRA, FCA, and EBA documents after a near-miss where an ungrounded query nearly caused a £12M capital miscalculation. Introduces the RAGAS evaluation harness and hybrid retrieval that make regulatory RAG auditable.

📁 Folder agentic_rag.py

05AI Governance

LOW

Builds the AI Governance Platform that registers, validates, and monitors every model in the book under PRA SS1/23's three pillars — registration, independent validation, and ongoing monitoring. Cost £48.5K to build against the £2.8M s166 skilled-person review it protects AWB from.

📁 Folder operational_mrm.py

Part II — Chapters 6–10

Risk Management Domains

Part II applies the Part I foundations to the five core risk domains every UK bank must manage — credit, market, operational, liquidity, and model risk — each delivered as a production-grade system with a full ROI derivation.

06Credit Risk

HIGH

Replaces AWB's fragmented credit monitoring with the Credit Intelligence Monitor — five modules spanning covenant compliance, adverse news, PSI drift, IFRS 9 staging, and CFO narrative — on one Airflow DAG and one governance registration. Its credit API becomes the PD source for Chapter 7's CVA and the staging feed for Chapter 11's regulatory reporting.

📁 Folder agentic_cim.py

07Market Risk

HIGH

Applies the same platform discipline to the trading book — Monte Carlo VaR, expected shortfall, FRTB SA/IMA, and a CVA calculator consuming Chapter 6's PD term structure. Cut FRTB back-test exceptions from ~10/year to 3, avoiding a capital multiplier that would have raised SA-FRTB capital up to 4×.

📁 Folder agentic_market_risk.py

08Operational Risk

MEDIUM

Builds AWB's payment fraud detector, NLP loss-event classifier, and application fraud scorer inside the same governance frame. The fraud model scores 2.4M daily transactions at P99 54ms, targeting a reduction from £7.2M to under £1.5M/year in fraud losses.

📁 Folder agentic_op_risk.py

09Liquidity Risk

HIGH

Moves to the tightest time constraints in the book — a miscalculated intraday position can trigger a CHAPS settlement failure within hours. Builds real-time LCR/NSFR engines and an intraday monitor (BCBS 248) that fixed the architectural gap behind a March 2025 CLAR breach, for £2.75M net annual benefit.

📁 Folder agentic_liquidity_risk.py

10Model Risk

HIGH

Builds the enterprise Model Risk Management platform — validation, monitoring, and governance for every AI/ML model built through Chapter 9 — answering not "can we build a better model" but "can we prove it can be trusted." Governs all registered systems under one Model Risk Committee.

📁 Folder agentic_model_risk.py

Part III — Chapters 11–12

Compliance and Regulatory

Having mastered the five core risk domains, Part III turns to the regulatory and financial crime obligations that sit across every domain — where AI becomes a compliance accelerator, monitoring obligations in real time and generating the machine-readable evidence regulators increasingly require.

11Regulatory Compliance

Mixed

Automates what previously took 2.4 FTE per quarter — COREP capital returns, LCR/NSFR calculations, and the CRR3 Art.429 leverage ratio return — cutting the quarterly cycle from 340 analyst-hours to 45 (94% reduction). AWB's Q4 2025 leverage ratio of 4.2% clears the 3.0% Pillar 1 minimum by 120bps.

📁 Folder agentic_regulatory_compliance.py

12AML & KYC

HIGH

Builds AWB's AML, KYC, and financial crime prevention platform — digital identity verification, ML transaction monitoring, and borrower KYC — under POCA 2002, FCA SYSC 6.3, and JMLSG guidance. Cut false-positive alerts 80% (8,400 to 1,680/month) while maintaining 340 SARs/year, saving £1.15M/year.

📁 Folder agentic_aml_kyc.py

Part IV — Chapters 13–15

Enterprise Implementation

Building individual AI models is straightforward; running them reliably in a regulated bank is a different engineering challenge. Part IV covers the microservices architecture, MLOps/LLMOps pipeline, and BCBS 239-compliant data platform that make everything else sustainable.

13Enterprise Architecture

HIGH

Connects everything built in Parts I–III into one enterprise platform — microservices, multi-cloud DORA resilience, and Temenos T24 integration — after three independently designed systems that couldn't integrate cost AWB eleven weeks of unplanned rework and a DORA incident. The shared awb_commons library now underpins all 23 AI services.

📁 Folder agentic_enterprise_architecture.py

14MLOps & LLMOps

LOW

Builds the operational discipline that keeps 23 AI models reliable in production — CI/CD retraining pipelines, prompt versioning, and drift monitoring — making it structurally impossible to deploy a model that hasn't passed the four-gate pipeline. If Chapter 13 is the motorway network, this is the traffic management system.

📁 Folder agentic_mlops_llmops.py

15Data Infrastructure

MEDIUM

Builds the least visible, most load-bearing layer of the platform — a BCBS 239-aligned risk data warehouse, a point-in-time feature store, and GDPR-compliant governance feeding 20+ AI systems. Data quality is scored across 11 BCBS 239 principles at a 9.2/10 target.

📁 Folder agentic_data_infrastructure.py

Part V — Chapter 16

Integration

The capstone: taking fifteen chapters of individually validated AI systems and integrating them into one coherent, production-ready platform the CRO can rely on at 07:30 on a Monday morning.

16Integrated Platform

HIGH

The capstone — unifies Customer 360, the risk data warehouse, the feature store, and all systems built across Chapters 1–15 into one CRO/CFO dashboard with real-time risk position and BCBS 239 compliance views, closing AWB's final data-distribution gap in a single orchestration layer.

📁 Folder agentic_integrated_platform.py

AI for Financial Risk, Compliance and Regulatory Reporting

Chapters & code

Foundations

Risk Management Domains

Compliance and Regulatory

Enterprise Implementation

Integration

Companion documentation

Regulatory framework covered