AI Act in Practice for Financial Institutions: Risk Classification, Governance, Compliance Controls and Audit Readiness (Paperback) (ISBN-13: 9798184829838)

AI Act in Practice for Financial Institutions is a practical guide for banks, insurers, investment firms, fintech companies, payment institutions, asset managers, credit providers and market infrastructure organisations preparing for the European Union Artificial Intelligence Act.

Artificial intelligence now supports credit scoring, lending, customer onboarding, fraud detection, anti-money laundering, trading, investment advice, portfolio management, complaints handling, customer service and operational risk. These systems influence financial access, conduct risk, consumer protection, market integrity, data governance and supervisory accountability. The AI Act adds a new regulatory layer across the full AI lifecycle, from system identification and risk classification to governance, monitoring, documentation, incident response, vendor control and audit evidence.

This book translates the AI Act into practical operating controls for financial institutions. It explains how to identify AI systems, classify use cases, assign accountability, document decisions, test model behaviour, design human oversight, manage third-party AI, prepare supervisory evidence and integrate AI governance into existing risk management frameworks.

The book is structured around four practical areas. The first section explains the purpose, scope and definitions of the AI Act in the financial sector. It links AI Act duties with model risk management, outsourcing rules, operational resilience, consumer protection, data protection and financial crime compliance. The second section focuses on AI use cases in creditworthiness, lending, onboarding, fraud detection, AML, trading, investment advice, customer service and complaints. The third section addresses governance, board oversight, data quality, documentation, transparency, explainability, testing, monitoring, incident management and remediation. The fourth section converts compliance into readiness planning, gap assessment, vendor safeguards, staff training, audits and evidence preparation.

Written for practitioners, this book helps readers move from regulatory interpretation to implementation. It gives financial institutions a method for building an AI inventory, classifying AI systems, assessing high-risk use cases, mapping obligations, defining control owners, preparing evidence files and supporting internal audit review.

The book is relevant for compliance officers, risk managers, internal auditors, legal teams, data governance teams, AI model owners, fintech founders, consultants, board members, senior managers and professionals working on AI governance in regulated financial services.

For readers seeking a clear and operational guide to the EU AI Act, financial services AI regulation, responsible AI, AI risk management, AI governance, AI compliance controls and audit readiness, this book provides a direct route from legal duties to practical action.