# FSB 2017 AI/ML report: Artificial intelligence and machine learning in financial services: Market developments and financial stability implications Source: https://www.bankingnewsai.com/ai-regulation/documents/fsb-ai-ml-financial-services-2017 Last updated: Aug 26, 2026 The FSB's 1 November 2017 report was the first systematic international survey of AI and machine learning in finance. It mapped use cases in credit scoring, insurance pricing, client interaction, capital optimisation, trading and compliance/fraud detection, and flagged five financial-stability themes: efficiency gains, unexpected interconnectedness through shared data, third-party dependencies that could create systemically important unregulated players, opaque and uninterpretable models, and operational risks including data privacy and cyber. It remains the baseline every later FSB AI report builds on. ## At a glance | Field | Value | | --- | --- | | Authority | [FSB](https://www.bankingnewsai.com/ai-regulation/fsb) | | Type | Report | | Status | Final | | Published | Nov 1, 2017 | | Applies to | FSB member authorities; descriptive of banks, insurers, asset managers and trading firms using AI/ML | | Official text | https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/ | ## Key points - Published 1 November 2017, before generative AI; covers 'traditional' ML such as credit scoring, trading signals and anti-fraud. - Use cases examined: credit quality assessment, insurance pricing and marketing, automated client interaction, capital optimisation, trading signal generation and execution, compliance and fraud detection. - Warns that shared data sources and similar models across firms can create unexpected interconnectedness and correlated behaviour. - Identifies reliance on a small number of third-party AI/data providers as a potential source of new systemically important players outside the regulatory perimeter. - Highlights model opacity: lack of interpretability could make it hard for firms and supervisors to understand how AI affects markets at a macro level. - Calls for adequate testing on unbiased data, feedback mechanisms, and continued monitoring as adoption expands. ## What changed for banks It established the vocabulary supervisors still use for AI risk — third-party concentration, model correlation, opacity — nearly a decade before the 2024–2026 workstream, and is the reference point for the FSB's claim that many AI vulnerabilities are not new. ## Use cases it governs - [Credit scoring & underwriting](https://www.bankingnewsai.com/ai-regulation/by-use-case#credit-underwriting) - [Trading & capital markets](https://www.bankingnewsai.com/ai-regulation/by-use-case#trading-markets) - [Fraud detection](https://www.bankingnewsai.com/ai-regulation/by-use-case#fraud) - [Model risk management](https://www.bankingnewsai.com/ai-regulation/by-use-case#model-risk) - [Third-party & vendor AI](https://www.bankingnewsai.com/ai-regulation/by-use-case#third-party-vendors) - [AI governance (general)](https://www.bankingnewsai.com/ai-regulation/by-use-case#governance-general) ## FAQ ### What did the FSB's 2017 AI report conclude? That AI/ML could improve efficiency but also create interconnectedness, third-party dependencies, opaque models and operational risks, and that adoption should be monitored and models adequately tested. ### Is the FSB 2017 AI report still relevant? Yes as a baseline: the FSB's November 2024 and October 2025 reports and the June 2026 sound practices explicitly build on its risk categories. ## Related documents - [FSB Chair's letter to G20 (Aug 2026)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-chair-letter-g20-august-2026) — FSB Chair's letter to G20 Finance Ministers and Central Bank Governors: August 2026 — risks arising from frontier artificial intelligence models (Aug 31, 2026) - [Responses to FSB AI sound practices consultation (Aug 2026)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-ai-sound-practices-consultation-responses-2026) — Public responses to consultation on Sound Practices for Responsible Adoption of Artificial Intelligence (AI) (Aug 6, 2026) - [Bowman remarks at FSB AI outreach (July 2026)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-bowman-remarks-ai-sound-practices-2026) — Opening remarks on sound practices for artificial intelligence (FSB virtual outreach event) (Jul 7, 2026) - [FSB AI sound practices consultation (June 2026)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-ai-sound-practices-consultation-2026) — Sound Practices for Responsible Adoption of Artificial Intelligence (AI): Consultation report (Jun 10, 2026) - [FSB AI monitoring report (Oct 2025)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-monitoring-ai-adoption-vulnerabilities-2025) — Monitoring Adoption of Artificial Intelligence and Related Vulnerabilities in the Financial Sector (Oct 10, 2025) - [FSB next steps on AI monitoring (Oct 2025)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-next-steps-authorities-ai-monitoring-2025) — FSB outlines next steps for authorities on AI monitoring (Oct 10, 2025) - [FSB AI financial stability report (Nov 2024)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-financial-stability-implications-of-ai-2024) — The Financial Stability Implications of Artificial Intelligence (Nov 14, 2024) - [FSB third-party risk toolkit (2023)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-third-party-risk-toolkit-2023) — Final Report on Enhancing Third-party Risk Management and Oversight: A toolkit for financial institutions and financial authorities (Dec 4, 2023) Last reviewed Aug 26, 2026. Cite the official text (https://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/) for the rule and this page for the summary and dates. --- Canonical page: https://www.bankingnewsai.com/ai-regulation/documents/fsb-ai-ml-financial-services-2017 Part of [BankingNewsAI](https://www.bankingnewsai.com/) — a free daily brief on AI in banking, an AI regulation tracker (41 authorities, 237 documents) and AI-strategy profiles of the 120 largest US banks. Markdown versions of every reference page: append `.md` to the page URL; index at https://www.bankingnewsai.com/llms.txt.