Artificial intelligence and machine learning in financial services: Market developments and financial stability implications
Report published by the Financial Stability Board as its own text. Dated 2017-11-01.
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- Artificial intelligence and machine learning in financial services: Market developments and financial stability implications Financial Stability Board · published 2017-11-01 · Retrieved for the candidate pack on 2026-11-02 (SHA-256 25c70dafbd365900355dc39968f5c0be52117f52e246d29bb91c395d82701e93). Retrieved again at admission on 2026-11-06 (same file). PDF dated 1 November 2017 as served by fsb.org on 2026-11-02 (requested at the wp-content address, redirected to /uploads/P011117.pdf)
- Earliest located in AI and money: what central banks and financial authorities have said, 1997–2026 for J02 Reliance on a few AI providers creates concentration risk →
- Earliest located in AI and money: what central banks and financial authorities have said, 1997–2026 for J03 AI increases cyber, fraud and operational risks in finance →
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- AI and money: what central banks and financial authorities have said, 1997–2026 →8 records on this document
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Articulates · 7
Propositions whose criterion a passage of this document meets. The record says what the text states, in its register; it is not evidence that the proposition is true.
- AI raises financial-stability risks through correlated behaviourThe FSB report of 1 November 2017 on artificial intelligence and machine learning in financial services states that if many market participants use similar AI and machine-learning programmes, the resulting correlated risks may entail financial stability risks and, as with any herding behaviour, could amplify financial shocks.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗AI raises financial-stability risks through correlated behaviour: propositions page →
- Reliance on a few AI providers creates concentration riskThe FSB report of 1 November 2017 states that AI and machine-learning services are increasingly offered by a few large technology firms, that third-party dependencies could give rise to new systemically important players, and that these dependencies could have systemic effects if such a firm faced a major disruption or insolvency.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗Reliance on a few AI providers creates concentration risk: propositions page →
- AI increases cyber, fraud and operational risks in financeThe FSB report of 1 November 2017 states that predictable patterns in the behaviour of automated trading strategies could be used by insiders or cybercriminals to manipulate market prices, and that uses of AI and machine learning should be assessed for risks including cybersecurity.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗AI increases cyber, fraud and operational risks in finance: propositions page →
- The opacity of AI models complicates risk management and supervisionThe FSB report of 1 November 2017 states that the lack of interpretability or auditability of AI and machine-learning methods could become a macro-level risk if not appropriately supervised, and that widespread use of opaque models would make it very difficult for firms and supervisors to predict how model-directed actions affect markets.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗The opacity of AI models complicates risk management and supervision: propositions page →
- AI can improve nowcasting and policy analysisThe FSB report of 1 November 2017 states, in its section on uses by central banks and prudential authorities, that natural language processing tools may help authorities detect, measure, predict and anticipate market volatility, liquidity risks, financial stress, housing prices and unemployment.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗AI can improve nowcasting and policy analysis: propositions page →
- Supervisors should use AIThe FSB report of 1 November 2017 defines SupTech as the use of these technologies by public sector regulators and supervisors and states that RegTech and SupTech applications of AI and machine learning can help increase supervisory effectiveness.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗Supervisors should use AI: propositions page →
- AI can improve financial firms’ risk managementThe FSB report of 1 November 2017 states that AI and machine learning can be used for risk management through earlier and more accurate estimation of risks and for detecting fraud, suspicious transactions, default and cyber-attack risk, while noting that such tools remain untested under shifting financial conditions.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗AI can improve financial firms’ risk management: propositions page →
Bears on · 1
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- Existing financial rules broadly cover AIThe FSB report of 1 November 2017 states that there were no known dedicated international standards on AI and machine learning, and that, as the current regulatory framework was not designed with such tools in mind, some regulatory practices may need revising for their benefits to be harnessed.2017-11-01 · publication · Stated in source · Supported Inspect claim & evidence ↗Existing financial rules broadly cover AI: propositions page →
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