This book provides a practitioner-focused framework for managing model risk in large language model applications across regulated financial services. It shows how classical model risk management must be extended for systems that are non-deterministic, composition-based, vendor-dependent and often embedded in consequential customer- or market-facing processes.

The book develops an operational discipline for validating, inventorying, monitoring and governing LLM applications in banks and other regulated firms. It introduces an artefact taxonomy for decomposing LLM systems into governable components, an inventory schema covering prompt templates, retrieval indexes, foundation models and agent tooling, and a fingerprint-bound validation approach for systems whose outputs cannot be reproduced in the classical sense. Chapters cover prompt engineering, third-party model governance, design-time and independent validation, metric portfolios, fairness and bias, Consumer Duty, agent governance, red-teaming, monitoring and incident response.

Sector chapters apply the framework to retail and commercial banking, insurance and capital markets, with use cases including customer correspondence, credit memo drafting, claims classification, research summarisation and regulatory reporting. Accompanied by runnable Python notebooks, it is written for model risk managers, validators, CROs, Chief AI and Data Officers, regulators, data scientists and technology leaders seeking practical governance tools for generative AI.

John Christiansen is Chief Risk Officer and Head of Data Science at ezbob Ltd, a UK lending technology firm providing loan origination, AI-powered credit decisioning and portfolio analytics to regulated lenders in the UK, the EU and beyond. He has more than 25 years of banking and financial services experience in credit risk, model risk, data science and regulatory engagement. Previous employers include Lloyds Banking Group, ING Direct, ABN AMRO, Deutsche Bank, Bank of China Hong Kong and FICO. His expertise spans retail and SME lending, prudential regulation, IFRS 9, Basel capital regimes and the FCA conduct framework.

He holds an MSc in Artificial Intelligence with Distinction from the University of Stirling, where his research addressed explainable AI for credit-default prediction in government-guaranteed SME lending and is now a postgraduate researcher at the University of Edinburgh, pursuing a doctorate in statistics on detecting AI-generated passages within human-authored documents, framed as a sequential anomaly and change-point detection problem. His wider research applies operations research to lending, including the MORAI (Multi-Objective Responsible AI) framework. He has contributed to UK FCA working groups on SME capital access and Smart Data.