Claim · 2017-11-01 · publication · Stated in source · Supported

The 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.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Artificial intelligence and machine learning in financial services: Market developments and financial stability implications → articulates → AI can improve nowcasting and policy analysis

Where it appears

AI and money: what central banks and financial authorities have said, 1997–2026 →

Claim and evidence

Artificial intelligence and machine learning in financial services: Market developments and financial stability implicationsarticulates ↓AI can improve nowcasting and policy analysis

Machine-checkedStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2017-11-01 · publication Records dated 2017 →
Role: Articulation
Register (kind of text): Nonfiction

Scope and limitsA statement of what the text says, not evidence that the risk has materialised or that any authority has adopted a policy. The report cites studies and a Banca d’Italia study described as in progress.

The evidence 01

primary sourceSupports the claim

Artificial intelligence and machine learning in financial services: Market developments and financial stability implications

Financial Stability Board · 2017-11-01

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“Machine learning can be applied to systemic risk identification and risk propagation channels. Specifically, NLP tools may help authorities to detect, measure, predict, and anticipate, among other things, market volatility, liquidity risks, financial stress, housing prices, and unemployment.”

Location in source Section 3.4.3 “SupTech: uses and potential uses by central banks and prudential authorities”, PDF p. 25 (printed p. 21)

How it was read Retrieved directly from the publisher

Machine-checked · 2026-11-06

Source details and check notes

Authors: Financial Stability Board
Version: 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)
Canonical address: Open the canonical source ↗
Accessed: 2026-11-06
Language: English (en)
Attribution: Financial Stability Board, “Artificial intelligence and machine learning in financial services: Market developments and financial stability implications”, 2017-11-01.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, AI and money (research/ai-money). The quotation (38 words) matched a fresh retrieval of 2026-11-06 (the same file the candidate pack read). Matching ignored differences of whitespace, case, quotation marks, dashes, line-break hyphens and Unicode compatibility forms. No human editorial review.

Status and meaning

Editorial status. Machine-checked. Machines found this record and checked it against its sources; no person accepted it individually. Each evidence record above shows how it was checked.

Relation. A passage of the document meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.

Date. Date of publication, as the text gives it.

Record ID am-u05-fsb-2017-ai-ml