[{"data":1,"prerenderedAt":619},["ShallowReactive",2],{"uc-suspicious-activity-report-drafting":3,"uc-regulations":415},{"useCase":4,"evidence":202,"blitsAiDeployments":318,"benchmarks":319,"indicative":320,"related":323,"indexability":413,"includeUnpublished":208},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":18,"patterns":21,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":40,"indicativeValue":46,"macroEstimates":81,"feasibility":82,"implementation":95,"risk":135,"blitsAi":175,"faq":177,"related":190,"datePublished":196,"dateModified":196,"lastVerified":197,"changelog":198,"slug":201},"AI copilot for SAR and STR narrative drafting","SAR and STR drafting","Generative AI drafts SAR and STR narratives from the case file for investigators to verify. At Nexo, Unit21 reports 57% of alert reviews automated by AI agents.","published","Generative AI that drafts the narrative of a single suspicious activity or suspicious transaction report from the investigation file (who, what, when, where, why and how), with every fact linked to its source record, so the investigator verifies, edits and files instead of starting from a blank page. It works case by case, unlike the periodic data returns of regulatory reporting.",[11,12,13,14],"SAR narrative generation","STR drafting assistant","suspicious transaction report copilot","AML investigation summary",[16,17],"banking","payments",[19,20],"financial-crime-compliance","case-management",[22,23,24,25],"content-generation","summarization","rag-knowledge-assistant","agentic-workflow",[27,28],"internal-tools","agent-desktop","employee-facing","copilot","emerging","middle-office","When an investigation concludes that activity is suspicious, the investigator must write a\nreport to the financial intelligence unit. The narrative is the only free text part of the\nreport, and it has to explain who was involved, which accounts and transactions, over what\nperiod, what typology it resembles and why it is suspicious, clearly and completely. FinCEN's\nnarrative guidance warns that incomplete, incorrect or disorganized narratives make further\nanalysis by law enforcement difficult, if not impossible.\n\nWriting it is slow. Investigators copy transaction tables, reconstruct timelines from statements\nand case notes, and write prose under deadline pressure, since reports must be filed within a\nfixed period after detection (in the United States, as FinCEN's guidance restates, no later than\n30 calendar days after initial detection). Quality varies between investigators, and quality assurance\nteams send drafts back for missing facts or unclear reasoning. Time spent writing is time not\nspent investigating.",[],"1. **Assemble the facts.** The agent gathers the case file: alerts, customer due diligence,\n   transactions, counterparties, previous reports, investigator notes and any external requests.\n2. **Build the timeline.** It orders the relevant transactions and events and computes totals,\n   date ranges and counterparties, using data queries rather than the language model for numbers.\n3. **Draft the narrative.** It writes the narrative in the structure the financial intelligence\n   unit expects, with each statement referencing the record it comes from.\n4. **Check completeness.** It checks the draft against the filing guidance (subjects,\n   instruments, dates, amounts, locations, reason for suspicion) and flags anything missing.\n5. **Investigator attests.** The investigator verifies each fact, edits the reasoning, and files.\n   The draft, the edits and the final version are retained.",[37,38,39],"employee-productivity","compliance","speed",[41,42,43,44,45],"time-saved-per-task","processing-time-reduction","productivity-gain","error-reduction","hours-saved",{"referenceOrg":47,"inputs":48,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A bank filing 5,000 suspicious activity reports a year",[49,55,62,69],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"reports","Reports filed per year",5000,"reports per year","The reference bank.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"hoursPerNarrative","Investigator hours spent drafting each narrative today",1.5,4,"hours per report","Editorial assumption. Replace with your own time study.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"timeSaved","Share of drafting time saved",0.3,0.5,"fraction of drafting time","Editorial assumption. Verification and editing time remain with the investigator, and public results so far are vendor reported and measure alert review rather than drafting alone (for example Uphold's 44% faster median alert review in a pilot); replace with results from your own pilot.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"costPerHour","Fully loaded investigator cost per hour",45,80,"USD per hour","Editorial assumption. Replace with your own.","reports * hoursPerNarrative * timeSaved * costPerHour","USD","per year","Investigator capacity released","Counts drafting time only. It leaves out quality assurance rework avoided, better reports for law enforcement, and the cost of validating and running the drafting system.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":90},"medium","Drafting from a well structured case file is within reach of current models. The work is in grounding every fact, computing numbers outside the model, protecting highly sensitive data and keeping the investigator accountable for the content.",[86,87,88,89],"Structured case files with transactions, subjects and investigator notes","Examples of high quality narratives approved by quality assurance","The financial intelligence unit's filing guidance and field definitions","Access controls that match SAR confidentiality requirements",[91,92,93,94],"AML case management system","Transaction and customer data stores","Financial intelligence unit filing system or e filing format","Quality assurance workflow",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[97,100,103,106,109],{"title":98,"detail":99},"Start from the filing guidance","Turn the financial intelligence unit's guidance and your quality assurance checklist into a structure and a completeness check the draft must satisfy.",{"title":101,"detail":102},"Compute, then write","Calculate totals, date ranges and counts with queries, and give them to the model as facts. Never let the model do arithmetic in the narrative.",{"title":104,"detail":105},"Require citations","Make every statement in the draft reference a record in the case file, and show the references to the investigator in the editing view.",{"title":107,"detail":108},"Measure against quality assurance","Compare drafts, edited versions and quality assurance outcomes on a sample of cases before rolling out, and track edit distance and rework rates afterwards.",{"title":110,"detail":111},"Lock down confidentiality","Run the model in an environment approved for SAR data, with no retention by third parties and access limited to the investigation team.",[113,114,115,116,117],"The investigator verifies every fact and is the named author of the filed report","Numbers come from data queries, not from the language model","Every statement references a source record; unsupported statements are flagged","SAR confidentiality, with access limited to the investigation team and no data retained by model providers","Draft, edits and final version retained for audit","The system drafts; the investigator decides whether to file, verifies the facts, rewrites the reasoning where needed and attests. Quality assurance reviews a sample as today, and the money laundering reporting officer owns the use of the tool.",[120,121,122,123,124],"Drafting time per report, before and after","Quality assurance rework rate and reasons","Share of draft statements changed or removed by investigators","Completeness check failures per draft","Time from case conclusion to filing",[126,129,132],{"title":127,"detail":128},"Confident errors in facts","A wrong amount or date in a filed report undermines the bank and the investigation. Compute numbers outside the model and require citations.",{"title":130,"detail":131},"Boilerplate reasoning","Drafts converge on generic typology language that tells law enforcement little. Measure how much investigators rewrite the reasoning section and coach the prompts on good examples.",{"title":133,"detail":134},"Automation of the decision","The draft makes filing look like the default. Keep the decision to file separate from the drafting step and record it explicitly.",{"euAiAct":136,"regulations":139,"guidance":150,"controls":168,"incidents":174},{"tier":137,"basis":138},"minimal","Drafting internal reports for a human investigator is not listed in Annex III (the law enforcement uses in point 6 cover systems used by or for law enforcement authorities, not a bank's own reporting), and the text is not published to inform the public, so the deployer disclosure duty for generated text in Article 50(4) does not apply. Confidentiality rules for suspicious activity reports and GDPR apply in full.",[140,141,142,143,144,145,146,147,148,149],"eu-ai-act","gdpr","fatf-recommendations","dora","mas-ai-risk-management","nist-ai-rmf","iso-42001","eu-amlr","us-bsa","mas-notice-626",[151,157,163],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"Guidance on Preparing a Complete and Sufficient Suspicious Activity Report Narrative","Financial Crimes Enforcement Network (FinCEN)","north-america","https://www.fincen.gov/system/files/shared/sarnarrcompletguidfinal_112003.pdf","Guidance of November 2003 that explains the five essential elements a narrative must cover (who, what, when, where and why, plus how) and the 30 day filing deadline, with examples of sufficient and insufficient narratives; a useful completeness checklist for any drafting tool.",{"title":158,"issuer":159,"region":160,"url":161,"note":162},"Supporting Artificial Intelligence Adoption in AML/CFT","Hong Kong Monetary Authority","asia-pacific","https://brdr.hkma.gov.hk/eng/doc-ldg/docId/getPdf/20251118-3-EN/20251118-3-EN.pdf","Circular of 19 November 2025. It reports that more than 30% of authorized institutions already use AI in transaction monitoring and announces supervisory workshops that include the use of generative AI to compile suspicious transaction reports.",{"title":164,"issuer":165,"region":154,"url":166,"note":167},"Joint Statement Encouraging Innovative Industry Approaches to AML Compliance","FinCEN and the US federal banking agencies","https://www.fincen.gov/news/news-releases/treasurys-fincen-and-federal-banking-agencies-issue-joint-statement-encouraging","Statement of 3 December 2018. Innovative pilot programs should not in themselves subject banks to supervisory criticism, even if they ultimately prove unsuccessful.",[169,170,171,172,173],"Named investigator attests every filed report","Environment and model provider terms approved for SAR confidentiality","Citation and completeness checks on every draft","Retention of draft, edits and final version","Inventory entry with owner, prompts under change control and periodic quality review",[],{"howToBuild":176},"On Blits.ai the drafting runs as an **agentic workflow**, triggered from the case management\nsystem through the API. The agent reads the case through **custom functions** and **SQL\nknowledge bases**, so totals, dates and counts come from queries, and it follows the filing\nguidance and approved example narratives stored in a **knowledge base** with hybrid retrieval.\nIt returns **structured output**: the narrative with record references, and a completeness\nchecklist.\n\nThe investigator reviews the draft and approves or rejects it through **human in the loop\napproval** before anything goes back to the case system, where the investigator edits and\nfiles; every run keeps a full audit trail. **PII masking**\nand **tenant isolation** protect the case data, **prompt versioning** and **test suites** keep\nchanges controlled, and the platform is model agnostic with EU and UAE data residency, so the\nbank can choose a model and region its SAR confidentiality rules allow.",[178,181,184,187],{"question":179,"answer":180},"Can generative AI write a suspicious activity report?","It can draft the narrative from the case file, but the investigator remains responsible for the decision to file and for every fact in it. Treat it as a drafting copilot with citations, not as an adjudicator.",{"question":182,"answer":183},"Do regulators allow AI drafted SAR narratives?","No rule we know of forbids them, and none removes the filer's accountability. In November 2025 the Hong Kong Monetary Authority announced workshops on using generative AI to compile suspicious transaction reports, and in 2018 FinCEN and the US federal banking agencies encouraged innovative approaches to AML compliance, including pilot programs.",{"question":185,"answer":186},"Who is already using AI to write investigation narratives?","Mostly fintech, crypto and payment firms so far, through vendor platforms. Unit21 reports that by automating alert narratives and dispositions its AI agents automate 57% of alert reviews at Nexo, and FIS announced in May 2026 that BMO and Amalgamated Bank are developing with its Financial Crimes AI Agent. Public results from large banks are still scarce.",{"question":188,"answer":189},"How do you stop the model inventing facts?","Compute every number with data queries, require a source reference for every statement, flag anything unsupported, and have the investigator verify the facts before filing.",[191,192,193,194,195],"aml-alert-triage","mule-network-detection","regulatory-report-assembly","trade-finance-crime-screening","market-abuse-surveillance-triage","2026-09-27","2026-09-26",[199],{"date":196,"note":200},"First published","suspicious-activity-report-drafting",[203,244,269,293],{"title":204,"useCases":205,"organization":206,"vendors":210,"summary":214,"stage":215,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":230,"sources":231,"verification":239,"grade":241,"id":242,"organizationSlug":243},"Nexo: AI agents that write alert narratives and propose dispositions with Unit21",[201,191],{"name":207,"anonymized":208,"region":209,"industry":17},"Nexo",false,"europe",[211],{"name":212,"role":213},"Unit21","platform","Digital asset services company Nexo uses Unit21's transaction monitoring and case management with AI agents that automate alert narratives and dispositions. Analysts work in a supervisory role, verifying the AI generated output, investigating anomalies and applying judgment. The vendor reports that a majority of alert reviews are now automated, with further automation projected.","production",2026,[27],[219],"en",[221],{"kpi":222,"value":223,"unit":224,"qualifier":225,"period":226,"claimant":227,"quote":228,"sourceUrl":229},"automation-rate",57,"percent","exact","share of alert reviews automated","vendor","By automating alert narratives and dispositions, Unit21’s AI Agents have enabled Nexo to achieve 57% automation in alert reviews, with projections to reach up to 80% as the models continue to evolve.","https://www.unit21.ai/customers/nexo",true,[232,234],{"url":229,"title":233,"publisher":212},"Nexo Case Study",{"url":235,"title":236,"publisher":237,"date":238},"https://baytobaynews.com/daily-state-news/stories/unit21-awarded-two-2026-datos-impact-awards-for-ai-innovation-cryptodigital-asset-aml-innovation,346520","Unit21 Awarded Two 2026 Datos Impact Awards for AI Innovation & Crypto/Digital Asset AML Innovation","Business Wire (via Bay to Bay News)","2026-09-14",{"level":240,"checkedAt":196},"source-verified","B","nexo-unit21-ai-alert-narratives",null,{"title":245,"useCases":246,"organization":247,"vendors":250,"summary":252,"stage":253,"year":216,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":230,"sources":263,"verification":267,"grade":241,"id":268,"organizationSlug":243},"Uphold: pilot of an AI agent for alert review and regulatory filing preparation with Unit21",[201,191],{"name":248,"anonymized":208,"country":249,"region":154,"industry":17},"Uphold","US",[251],{"name":212,"role":213},"Crypto platform Uphold unified alerts, cases and regulatory filings with FinCEN and FINTRAC in Unit21, and piloted Unit21's AI agent to help analysts review alerts faster and more consistently. The vendor reports a drop in median alert review time from the pilot and predicts much faster suspicious transaction report preparation, which has not yet been measured.","pilot",[27],[219],[257],{"kpi":258,"value":259,"unit":224,"qualifier":225,"period":260,"claimant":227,"quote":261,"sourceUrl":262},"handling-time-reduction",44,"median alert review time during the pilot","Median alert review time has dropped by 44% thanks to a pilot of Unit21’s AI Agent, which helps analysts process alerts faster and more consistently.","https://www.unit21.ai/customers/uphold",[264,266],{"url":262,"title":265,"publisher":212},"Uphold Case Study",{"url":235,"title":236,"publisher":237,"date":238},{"level":240,"checkedAt":196},"uphold-unit21-ai-agent-pilot",{"title":270,"useCases":271,"organization":272,"vendors":274,"summary":280,"stage":281,"year":216,"channels":282,"languages":283,"metrics":284,"outcomeDisclosed":208,"sources":285,"verification":290,"grade":291,"id":292,"organizationSlug":243},"FIS with Anthropic: Financial Crimes AI Agent in development at BMO and Amalgamated Bank",[191,201],{"name":273,"anonymized":208,"region":154,"industry":16},"BMO and Amalgamated Bank",[275,277],{"name":276,"role":213},"FIS",{"name":278,"role":279},"Anthropic","model-provider","FIS announced in May 2026 that it is building a Financial Crimes AI Agent with Anthropic that assembles evidence across a bank's core systems for anti money laundering alert and case investigations and supports suspicious activity report narratives. BMO and Amalgamated Bank are developing with the agent, and FIS plans general availability in the second half of 2026. The release states aims for investigation time and narrative quality but no measured results.","announced",[27],[219],[],[286],{"url":287,"title":288,"publisher":276,"date":289},"https://www.fisglobal.com/about-us/media-room/press-release/2026/fis-brings-agentic-ai-to-banking-with-anthropic-starting-with-financial-crimes","FIS Brings Agentic AI to Banking with Anthropic, Starting with Financial Crimes","2026-05-04",{"level":240,"checkedAt":197},"C","fis-financial-crimes-ai-agent",{"title":294,"useCases":295,"organization":296,"vendors":299,"summary":302,"stage":215,"year":303,"channels":304,"languages":305,"metrics":307,"outcomeDisclosed":208,"sources":308,"verification":316,"grade":291,"id":317,"organizationSlug":243},"Finshark: AI assisted financial crime investigations and reporting with Lucinity's Luci agent",[201],{"name":297,"anonymized":208,"country":298,"region":209,"industry":17},"Finshark","SE",[300],{"name":301,"role":213},"Lucinity","Swedish open banking and instant payments company Finshark uses Lucinity's case manager with the Luci AI copilot, which adds case summaries, report writing and customer research to investigations, together with Lucinity's regulatory reporting module. The case study says the administrative manual work in case investigations has been significantly reduced but gives no figures.",2024,[27],[219,306],"sv",[],[309,312],{"url":310,"title":311,"publisher":301},"https://lucinity.com/casestudy-finshark","Lucinity and Finshark Case Study",{"url":313,"title":314,"publisher":301,"date":315},"https://lucinity.com/blog/finshark-enhances-financial-crime-prevention-with-lucinity-ai-powered-platform","Finshark Enhances Financial Crime Prevention with Lucinity's AI-Powered Platform","2024-10-24",{"level":240,"checkedAt":196},"finshark-lucinity-luci-investigations",0,[],{"low":321,"high":322},101250,800000,[324,348,362,376,391],{"slug":191,"title":325,"shortTitle":326,"definition":327,"status":8,"industries":328,"functions":329,"patterns":330,"audience":29,"autonomy":333,"adoptionStage":334,"segment":32,"evidenceCount":335,"publicEvidenceCount":335,"organizations":336,"bestGrade":241,"headline":342,"lastVerified":196,"indexable":230},"AI for AML transaction monitoring alert triage","AML alert triage","Machine learning and AI agents that score anti money laundering alerts for genuine risk, close clear false positives with a written and stored rationale, and hand investigators the remaining alerts already enriched with the customer, counterparty and transaction context.",[16,17],[19],[331,332,25,23],"prediction-and-scoring","anomaly-detection","supervised-agent","early-adopters",8,[337,273,338,207,339,340,341,248],"Australia Post","HSBC","Ratepay","Shift4","United Overseas Bank (UOB)",{"kpi":343,"label":344,"unit":224,"n":345,"nUpTo":318,"kind":346,"value":347,"qualifier":225,"claimant":227,"organization":340,"vendorReported":230},"false-positive-reduction","False positive reduction",2,"reported",86,{"slug":192,"title":349,"shortTitle":350,"definition":351,"status":8,"industries":352,"functions":353,"patterns":355,"audience":356,"autonomy":30,"adoptionStage":334,"segment":32,"evidenceCount":357,"publicEvidenceCount":357,"organizations":358,"bestGrade":241,"headline":243,"lastVerified":196,"indexable":230},"AI for money mule account and network detection","Mule network detection","Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.",[16,17],[354,19],"fraud-prevention",[332,331,25,23],"back-office",3,[359,360,361],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":193,"title":363,"shortTitle":364,"definition":365,"status":8,"industries":366,"functions":369,"patterns":372,"audience":29,"autonomy":30,"adoptionStage":31,"segment":356,"evidenceCount":345,"publicEvidenceCount":345,"organizations":373,"bestGrade":241,"headline":243,"lastVerified":196,"indexable":230},"AI for regulatory report assembly","Regulatory report assembly","AI that assembles periodic and data driven regulatory filings and returns, such as prudential and statistical returns, threshold and transaction reports and disclosure packs, by pulling data into the regulator's schema, validating it, reconciling figures to source, explaining movements against prior periods and drafting commentary, before a named officer reviews and submits. Narratives for individual suspicious activity cases are a separate use case.",[16,367,368,17],"insurance","capital-markets",[370,371,19],"regulatory-compliance","finance-and-accounting",[25,332,22,23],[374,375],"Board of Governors of the Federal Reserve System","National Credit Union Administration",{"slug":194,"title":377,"shortTitle":378,"definition":379,"status":8,"industries":380,"functions":381,"patterns":383,"audience":356,"autonomy":333,"adoptionStage":31,"segment":386,"evidenceCount":357,"publicEvidenceCount":357,"organizations":387,"bestGrade":291,"headline":243,"lastVerified":196,"indexable":230},"AI screening of trade finance transactions for trade based money laundering","Trade crime screening","AI that screens every trade finance transaction for financial crime risk: it checks parties, vessels and ports against sanctions and watchlists, tests goods descriptions against dual use and controlled goods lists, compares unit prices with benchmarks for over or under invoicing, and reads trade documents and messages for laundering red flags, then prepares a case narrative for a human investigator.",[16],[19,382],"operations",[384,332,385,23],"document-processing","classification-and-routing","specialized-businesses",[388,389,390],"ANZ, HSBC and Lloyds Banking Group","Stanbic Bank Uganda","United Bank Limited",{"slug":195,"title":392,"shortTitle":393,"definition":394,"status":8,"industries":395,"functions":397,"patterns":398,"audience":29,"autonomy":30,"adoptionStage":334,"segment":399,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":241,"headline":407,"lastVerified":197,"indexable":230},"AI for market abuse surveillance alert triage","Market abuse surveillance","AI that helps surveillance analysts triage market abuse and conduct alerts, such as spoofing, layering, wash trades, ramping and insider dealing, by gathering the trade, order, news and communications context, explaining in plain language what triggered each alert and drafting the investigation narrative for the analyst to disposition.",[368,16,396],"wealth-and-asset-management",[370,19],[332,25,23,385],"second-line",5,[402,403,404,405,406],"Commodity Futures Trading Commission","Deutsche Bank","Japan Exchange Group","Nasdaq","U.S. Securities and Exchange Commission",{"kpi":258,"label":408,"unit":224,"n":409,"nUpTo":318,"kind":346,"value":410,"qualifier":411,"claimant":412,"organization":405,"vendorReported":208},"Handling time reduction",1,33,"approximately","organization",{"indexable":230,"reasons":414},[],[416,422,427,434,440,445,452,459,465,472,479,485,492,498,503,508,514,520,526,532,538,544,549,554,559,566,572,577,583,590,596,602,608,613],{"id":140,"label":417,"issuer":418,"region":209,"url":419,"description":420,"useCases":421,"indexable":230},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":141,"label":423,"issuer":418,"region":209,"url":424,"description":425,"useCases":426,"indexable":230},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":146,"label":428,"issuer":429,"region":430,"url":431,"description":432,"useCases":433,"indexable":230},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":145,"label":435,"issuer":436,"region":154,"url":437,"description":438,"useCases":439,"indexable":230},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":143,"label":441,"issuer":418,"region":209,"url":442,"description":443,"useCases":444,"indexable":230},"DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":446,"label":447,"issuer":448,"region":209,"url":449,"description":450,"useCases":451,"indexable":230},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":453,"label":454,"issuer":455,"region":209,"url":456,"description":457,"useCases":458,"indexable":230},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":144,"label":460,"issuer":461,"region":160,"url":462,"description":463,"useCases":464,"indexable":230},"MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":466,"label":467,"issuer":468,"region":160,"url":469,"description":470,"useCases":471,"indexable":230},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":473,"label":474,"issuer":475,"region":430,"url":476,"description":477,"useCases":478,"indexable":230},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":480,"label":481,"issuer":482,"region":154,"url":483,"description":484,"useCases":478,"indexable":230},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":486,"label":487,"issuer":488,"region":209,"url":489,"description":490,"useCases":491,"indexable":230},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":142,"label":493,"issuer":494,"region":430,"url":495,"description":496,"useCases":497,"indexable":230},"FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":147,"label":499,"issuer":418,"region":209,"url":500,"description":501,"useCases":502,"indexable":230},"EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":504,"label":505,"issuer":418,"region":209,"url":506,"description":507,"useCases":502,"indexable":230},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":148,"label":509,"issuer":510,"region":154,"url":511,"description":512,"useCases":513,"indexable":230},"Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":515,"label":516,"issuer":418,"region":209,"url":517,"description":518,"useCases":519,"indexable":230},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":521,"label":522,"issuer":523,"region":154,"url":524,"description":525,"useCases":519,"indexable":230},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":527,"label":528,"issuer":529,"region":430,"url":530,"description":531,"useCases":519,"indexable":230},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":533,"label":534,"issuer":418,"region":209,"url":535,"description":536,"useCases":537,"indexable":230},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":539,"label":540,"issuer":541,"region":154,"url":542,"description":543,"useCases":537,"indexable":230},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":149,"label":545,"issuer":461,"region":160,"url":546,"description":547,"useCases":548,"indexable":230},"MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":550,"label":551,"issuer":418,"region":209,"url":552,"description":553,"useCases":548,"indexable":230},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":555,"label":556,"issuer":418,"region":209,"url":557,"description":558,"useCases":548,"indexable":230},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":560,"label":561,"issuer":562,"region":209,"url":563,"description":564,"useCases":565,"indexable":230},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":567,"label":568,"issuer":569,"region":154,"url":570,"description":571,"useCases":335,"indexable":230},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":573,"label":574,"issuer":418,"region":209,"url":575,"description":576,"useCases":335,"indexable":230},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":578,"label":579,"issuer":418,"region":209,"url":580,"description":581,"useCases":582,"indexable":230},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":584,"label":585,"issuer":586,"region":587,"url":588,"description":589,"useCases":400,"indexable":230},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":591,"label":592,"issuer":593,"region":209,"url":594,"description":595,"useCases":59,"indexable":230},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":597,"label":598,"issuer":599,"region":209,"url":600,"description":601,"useCases":59,"indexable":230},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":603,"label":604,"issuer":605,"region":160,"url":606,"description":607,"useCases":357,"indexable":230},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":609,"label":610,"issuer":418,"region":209,"url":611,"description":612,"useCases":357,"indexable":230},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":614,"label":615,"issuer":616,"region":154,"url":617,"description":618,"useCases":357,"indexable":230},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598298330]