Claim and evidence Close ×
The 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. Copy claim link
Copy claim reference
Machine-checked Stated in source Supported
Supporting evidence: primary · 1 source
Date 2017-11-01 · publication Records dated 2017 → Role Articulation Register (kind of text) Nonfiction Scope and limits A statement of what the text says, not evidence that the risk has materialised or that any authority has adopted a policy. The June 2017 FSB report uses the word “SupTech” only in its glossary, without reference to AI. This November 2017 report uses the term together with AI. The earliest located use of the name is the June 2017 FSB report.
The evidence 02 primary source Supports the claim
Artificial intelligence and machine learning in financial services: Market developments and financial stability implications ↗ Financial Stability Board · 2017-11-01
Copy source reference
Cite the original source Cite the original document, not Selekoa. Check these details against it.
Copy APA
Download BibTeX Download RIS Download CSL-JSON
Source excerpt “The RegTech and SupTech applications of AI and machine learning can help improve regulatory compliance and increase supervisory effectiveness.” Location in source Executive summary, PDF p. 5 (printed p. 1)
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 (19 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. primary source Supports the claim
Artificial intelligence and machine learning in financial services: Market developments and financial stability implications ↗ Financial Stability Board · 2017-11-01
Copy source reference
Cite the original source Cite the original document, not Selekoa. Check these details against it.
Copy APA
Download BibTeX Download RIS Download CSL-JSON
Source excerpt “SupTech is the use of these technologies by public sector regulators and supervisors. Within SupTech, the objective of AI and machine learning applications is to enhance efficiency and effectiveness of supervision and surveillance.” Location in source Section 3.4, PDF p. 24 (printed p. 20)
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 (33 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-u09-fsb-2017-ai-ml