[{"data":1,"prerenderedAt":513},["ShallowReactive",2],{"uc-estate-and-trust-document-drafting":3,"uc-regulations":287},{"useCase":4,"evidence":117,"blitsAiDeployments":181,"benchmarks":182,"indicative":157,"related":189,"indexability":285,"includeUnpublished":123},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":22,"audience":24,"autonomy":25,"adoptionStage":26,"segment":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":34,"macroEstimates":38,"feasibility":39,"implementation":51,"risk":81,"blitsAi":93,"faq":95,"related":108,"datePublished":112,"dateModified":112,"lastVerified":112,"changelog":113,"slug":116},"AI review and extraction of existing estate and trust documents","Estate document review","AI review of existing estate and trust documents","AI powered document review can extract and check a client's existing estate documents: a Wealth.com case study reports it catching a typo in a trust document.","published","An AI assisted workflow for wealth advisors in which a system extracts and summarizes what a client's existing will, trust and power of attorney documents say, and flags where documents disagree or contain an error, for an advisor to review. Some platforms in this category also fill an attorney reviewed, jurisdiction specific template with the client's facts to produce a new draft estate document; whether that filling step itself runs on a model, or is document assembly outside the AI feature, is a vendor by vendor question the evidence on this page does not settle.",[12,13,14],"estate planning document review assistant","AI review of wills and trusts","estate document extraction assistant",[16],"wealth-and-asset-management",[18,19],"legal","sales",[21],"document-processing",[23],"internal-tools","employee-facing","copilot","emerging","front-office","Clients know they need a will and, often, a trust, but the plan stalls. A Wealth.com case study\ndescribes the friction of referring a client out to an estate planner bluntly: \"High fees,\nunfamiliar relationships, and a complicated process meant many clients hesitated to move forward\nwith estate planning.\" Many clients never finish, so the client stays without a current will,\nand the firm's own record of the client's wishes goes stale.\n\nWealth advisors often notice when the plan is missing, out of date, or full of instructions that\nno longer make sense, for example after a divorce, a death in the family or a move to a new\nstate, but they are not licensed to draft the legal documents themselves. The gap between\nspotting the problem and getting a compliant fix into place is where estate plans fail to happen.",[],"1. **Read what already exists.** If the client has a prior will, trust or power of attorney, the\n   assistant extracts who is named, what each document says and where two documents disagree,\n   so the advisor sees the actual state of the plan before proposing anything new.\n2. **Capture the facts once.** A guided conversation or intake form collects the client's family\n   structure, assets, beneficiaries and wishes, either directly from the client or from the\n   advisor's notes and the firm's CRM, so the client does not repeat the same story to an\n   attorney later.\n3. **Template filling, where it exists, stays a separate, non AI step.** Some platforms in this\n   category go on to fill an attorney maintained, jurisdiction specific template (a revocable\n   trust, a pour over will, financial and healthcare powers of attorney) with the client's\n   facts, producing a new draft. That is document assembly, not the AI feature this page\n   covers: it completes clauses a lawyer has already written and approved for that state or\n   country, and the evidence on this page does not show a model doing the filling itself. This\n   page's evidence supports the review and extraction step only.\n4. **Review, then sign and file if a new document is produced.** The advisor, and an attorney\n   where the firm's policy requires one, checks the extraction and any template filling output\n   against the facts. If the flow produces a new document, the client reviews and signs it, and\n   the signed documents and beneficiary designations are filed with the firm's own records and\n   the relevant custodian.\n5. **Watch for the next trigger.** The advisor, prompted by what the review surfaced or by the\n   firm's own process, revisits the plan when a life event, a change of state, or a change in\n   the law means it should be updated, rather than waiting for the client to ask.",[32,33],"risk-reduction","employee-productivity",[35,36,37],"error-reduction","search-time-reduction","hours-saved",[],{"complexity":40,"complexityNote":41,"dataPrerequisites":42,"integrations":46},"medium","The drafting engine and the jurisdiction specific legal templates are almost always bought from a specialist vendor, not built in house. The integration work is pulling household, asset and beneficiary data out of the CRM or financial planning tool and building the review and electronic signing workflow around the draft.",[43,44,45],"Client household, asset and beneficiary data from the CRM or financial planning system","Jurisdiction specific legal document templates, kept current by qualified counsel","The client's existing will, trust and beneficiary documents, when a plan already exists",[47,48,49,50],"CRM or financial planning platform (for example Salesforce, eMoney, Orion or Addepar)","A digital estate planning or document assembly platform","Electronic signing and a secure client facing portal","Custodian or account opening systems, to update beneficiary designations once signed",{"steps":52,"guardrails":65,"humanInTheLoop":69,"kpisToInstrument":70,"failureModes":74},[53,56,59,62],{"title":54,"detail":55},"Start with the extraction, not the drafting","Before generating anything new, have the assistant read the client's existing will, trust and beneficiary designations and show what is named, what is outdated and where documents conflict. This alone often surfaces the more urgent problem.",{"title":57,"detail":58},"Keep any legal templates under qualified control","Where a platform also fills a template, clause libraries come from a licensed attorney or the vendor's own legal team, per jurisdiction, and that filling stays a separate step from the AI review: it does not draft new legal language on its own.",{"title":60,"detail":61},"Build the advisor into the workflow, not around it","The advisor runs the intake conversation and reviews every extraction, and any generated document, before it reaches the client or an attorney. Nothing goes out client ready without that step.",{"title":63,"detail":64},"Wire in the life event triggers","Connect the plan to the events that make it stale: marriage, divorce, a birth, a move to a new state, a large liquidity event, so the firm proactively offers a refresh instead of waiting for the client to ask.",[66,67,68],"Every extraction is shown to the advisor next to the source passage in the original document, so a misread is caught before the advisor acts on it or shares it further","No finding or summary reaches a client without an advisor, and where policy requires it an attorney, reviewing it first","Where a platform also fills an estate document template with the client's facts, that step is kept separate from the AI review and traces to an attorney reviewed, jurisdiction specific template; the review itself does not draft new legal language","An advisor reviews every extraction and summary against the source document before acting on it or sharing it with a client, and brings in a licensed estate planning attorney for anything beyond straightforward fact checking. The assistant reads and summarizes what the client's existing documents say and where they disagree; it does not decide what the client's plan should say, and where a platform's template filling produces a new draft, an advisor, and where policy requires it an attorney, reviews that draft before it goes anywhere near a client signature.",[71,72,73],"Share of offered clients who complete a plan, and the time from first conversation to signed documents","Revenue or assets retained or grown from clients who complete a plan, against a comparable group who were not offered it","Rate of factual errors caught in advisor or attorney review before a document reaches the client",[75,78],{"title":76,"detail":77},"Template drift by jurisdiction","Estate law changes by state or country. A template that goes stale after a law change produces a document that is not valid where the client lives. Keep a named attorney owner and a review date on every jurisdiction's template.",{"title":79,"detail":80},"Misreading the client's less common family situation","Blended families, prior marriages, special needs beneficiaries and non citizen spouses are the natural language edge cases most likely to be misread from source documents. Route these situations to an attorney rather than leaving them with the advisor alone.",{"euAiAct":82,"regulations":85,"guidance":87,"controls":88,"incidents":92},{"tier":83,"basis":84},"context-dependent","AI review and extraction of a client's existing estate and trust documents is not itself listed in Annex III. Article 50 transparency governs the design instead, and its tier depends on how directly the client interacts with the system. The review and extraction step the evidence on this page documents is advisor facing: the assistant reads the client's existing documents for the advisor, and the client never interacts with it directly, which sits closer to minimal risk. When the guided intake conversation that captures facts talks to the client directly, Article 50(1) requires telling them they are dealing with AI, a limited risk duty. Where a platform's own template filling step also generates new text handed to a client, for example a draft document, Article 50(2) requires the provider to mark that output as AI generated when a model produced it; the evidence on this page does not establish that a model does the filling. Recorded as context-dependent because the actual tier follows each deployment's design, not a fixed property of the use case.",[86],"gdpr",[],[89,90,91],"Advisor or attorney sign off recorded before any document is sent to a client","Version control and jurisdiction tagging on every legal template, with a named legal owner and review date","Audit trail of what the assistant extracted from source documents versus what a human changed before signature",[],{"howToBuild":94},"On Blits.ai this starts as a **knowledge base**: the client's existing will, trust and\nbeneficiary documents are ingested (PDF and DOCX support out of the box) and retrieved with\nhybrid search so an **agent** can extract named parties, dates and provisions with structured\noutput. A **flow** drives the guided intake conversation with the advisor or client, and a\n**custom function** pulls household, asset and beneficiary data from the firm's CRM through\nits REST API so nothing has to be typed twice.\n\nBlits.ai's job here is the review and extraction: reading the client's existing documents,\nsummarizing what they say and flagging where they disagree, and running the guided intake\nthat captures the client's facts. Any legal template library, and the step that fills a\ntemplate with those facts, stays outside the platform, owned by the firm's own counsel or\nestate planning vendor. Where a firm wants that filled draft to reach a client through\nBlits.ai too, an **agentic workflow** with **human in the loop approval** holds it for an\nadvisor, or an attorney where the firm requires one, to approve first. **Guardrails** and\n**PII masking** protect the family and financial data involved, and **test suites** run the\nintake conversation against edge cases such as blended families before any change goes live.",[96,99,102,105],{"question":97,"answer":98},"Can AI actually draft a legal document like a trust or a will?","The public evidence found so far shows AI reading and reviewing documents, not drafting new legal language. Wealth.com's own homepage describes its Ester engine as letting advisors \"extract, summarize, and analyze existing documents,\" and elsewhere on the same page describes Ester more broadly as letting advisors \"extract, summarize, and visualize complete estate plans and tax documents.\" Neither description credits Ester with writing new legal clauses; document generation is listed as a separate feature. A Wealth.com case study on Sedai Wealth gives a concrete example of the review role: advisor Jared Tanimoto says Ester, named as the AI powered document review, caught a typo in a client's trust document. A Vanilla case study on BOK Financial's trust administration team describes the same pattern from the trust side: the AI helps a trust officer find and verify what an existing trust says, citing the exact article and section, rather than writing new provisions. As editorial guidance: treat any AI output that goes beyond reading and reviewing an existing document as a claim to verify with the vendor, not an established capability.",{"question":100,"answer":101},"Does a lawyer still need to be involved?","Usually yes, either as the firm's own counsel who owns any templates or as the reviewer of what the AI extracted, depending on the firm's policy and the jurisdiction. The advisor almost always checks the extraction against the source document before acting on it or sharing it further, and BOK Financial's Randy Kimmel is explicit that the technology helps verify a trust, it does not decide on it.",{"question":103,"answer":104},"What results have firms actually reported?","Two public, AI specific results have been found so far. A Wealth.com case study on Sedai Wealth quotes advisor Jared Tanimoto saying Ester, the platform's AI powered document review, caught a typo in a client's trust document, which he calls a good layer of quality control. A Vanilla case study on BOK Financial's Advisor Trust Services team quotes National Trust Consultant Randy Kimmel saying the team is saving roughly four hours on average on the initial review of a trust, because information is found and verified faster than before. Other case studies from both vendors describe advisors completing estate plans faster or growing revenue after adopting the platform, but those do not mention AI as the reason, so they are not used as evidence on this page. Treat both figures as small, vendor published data points, not measured rates across a firm.",{"question":106,"answer":107},"How is this different from a wealth advisor's knowledge assistant?","A knowledge assistant answers the advisor's questions from research and policy documents. This use case reads a specific client's existing legal documents and reports what they say, where they name each party and where two documents disagree, and that extraction becomes part of the record the advisor works from for that client.",[109,110,111],"goal-based-financial-planning-assistant","client-meeting-notes-and-crm-update","wealth-advisor-knowledge-assistant","2026-09-30",[114],{"date":112,"note":115},"First published","estate-and-trust-document-drafting",[118,158],{"title":119,"useCases":120,"organization":121,"vendors":125,"summary":129,"stage":130,"year":131,"channels":132,"languages":133,"metrics":135,"outcomeDisclosed":144,"sources":145,"verification":153,"grade":155,"id":156,"organizationSlug":157},"BOK Financial: Vanilla AI document review on trust files",[116],{"name":122,"anonymized":123,"region":124,"industry":16},"BOK Financial",false,"north-america",[126],{"name":127,"role":128},"Vanilla","platform","BOK Financial's Advisor Trust Services team, which administers trusts on behalf of financial advisors, brought in Vanilla's AI to speed up how a trust officer reviews an existing trust document: finding named parties, provisions and amendments and citing the exact article and section behind an answer. National Trust Consultant Randy Kimmel says the team is saving roughly four hours on average on the initial review of a trust, because information is found and verified faster than before. He is explicit that a human stays in the loop: the technology helps verify what is in a trust, not decide on it.","production",2025,[23],[134],"en",[136],{"kpi":37,"value":137,"unit":138,"qualifier":139,"period":140,"claimant":141,"quote":142,"sourceUrl":143},4,"hours","approximately","per initial trust review","organization","We're saving about four hours on average in our initial review, because we're finding information faster and verifying everything so much more quickly than what I was doing even last year.","https://www.justvanilla.com/customer-stories/how-bok-financial-uses-vanilla-to-review-trusts-faster",true,[146,149],{"url":143,"title":147,"publisher":127,"date":148},"How BOK Financial Uses Vanilla to Review Trusts Faster and Keep Advisors at the Center","2026-08-13",{"url":150,"title":151,"publisher":127,"date":152},"https://www.justvanilla.com/product-vai","Meet VAI, Vanilla AI for estate planning","2026-09-10",{"level":154,"checkedAt":112},"source-verified","C","bok-financial-vanilla-trust-review",null,{"title":159,"useCases":160,"organization":161,"vendors":163,"summary":166,"stage":130,"year":167,"channels":168,"languages":169,"metrics":170,"outcomeDisclosed":144,"sources":171,"verification":179,"grade":155,"id":180,"organizationSlug":157},"Sedai Wealth: Ester AI document review on a client's trust",[116],{"name":162,"anonymized":123,"region":124,"industry":16},"Sedai Wealth",[164],{"name":165,"role":128},"Wealth.com","Sedai Wealth, a Savvy Wealth affiliate led by certified financial planner Jared Tanimoto, runs estate planning inside its own flat fee planning model instead of referring clients to an outside attorney. A Wealth.com case study quotes Tanimoto saying Wealth.com's AI powered document review, Ester, caught a typo in a client's trust document, which he called a good layer of quality control. The same case study says about 35% of his clients have completed or updated an estate plan through the platform over the roughly two years he has used it.",2024,[23],[134],[],[172,176],{"url":173,"title":174,"publisher":165,"date":175},"https://www.wealth.com/resources/case-studies/how-cfp-jared-tanimoto-uses-wealth-com-to-deepen-client-relationships-and-deliver-comprehensive-planning/","How CFP Jared Tanimoto Uses Wealth.com to Deepen Client Relationships and Deliver Comprehensive Planning","2026-07-31",{"url":177,"title":178,"publisher":165},"https://www.wealth.com/","Wealth.com: Ester AI estate and tax planning engine",{"level":154,"checkedAt":112},"sedai-wealth-ester-document-review",0,[183],{"kpi":37,"label":184,"unit":138,"aggregate":123,"higherIsBetter":144,"n":185,"nUpTo":181,"median":137,"min":137,"max":137,"byClaimant":186,"vendorOnly":123,"points":187},"Hours saved",1,{"organization":185,"vendor":181,"regulator":181,"independent":181},[188],{"evidenceId":156,"organization":122,"value":137,"qualifier":139,"claimant":141,"grade":155,"pooled":144},[190,209,240,259],{"slug":109,"title":191,"shortTitle":192,"definition":193,"status":9,"industries":194,"functions":196,"patterns":199,"audience":24,"autonomy":25,"adoptionStage":26,"segment":27,"evidenceCount":203,"publicEvidenceCount":203,"organizations":204,"bestGrade":207,"headline":157,"lastVerified":208,"indexable":144},"AI assistant for goal based financial planning","Goal based planning","An AI assistant that turns a client's goals into projections and what if scenarios using a rules based planning engine, explains the trade offs in plain language and prepares the plan for an advisor to validate, with every assumption disclosed and reproducible.",[16,195],"banking",[19,197,198],"customer-service","product-and-pricing",[200,201,202],"conversational-agent","content-generation","agentic-workflow",2,[205,206],"CIMB Niaga","Vanguard","B","2026-09-27",{"slug":110,"title":210,"shortTitle":211,"definition":212,"status":9,"industries":213,"functions":214,"patterns":217,"audience":24,"autonomy":25,"adoptionStage":220,"segment":27,"evidenceCount":221,"publicEvidenceCount":221,"organizations":222,"bestGrade":207,"headline":232,"lastVerified":208,"indexable":144},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[16,195],[19,215,216],"regulatory-compliance","operations",[218,219,202,201],"summarization","speech-analytics","mainstream",9,[223,224,225,226,227,228,229,230,231],"Bank of America","Commerzbank","Korhorn Financial Group","Morgan Stanley","Osaic Advisors Fort Lauderdale","Quilter","RFG Advisory","SEB","UniSuper",{"kpi":233,"label":234,"unit":235,"n":185,"nUpTo":181,"kind":236,"value":237,"qualifier":238,"claimant":239,"organization":230,"vendorReported":144},"productivity-gain","Productivity gain","percent","reported",15,"exact","vendor",{"slug":111,"title":241,"shortTitle":242,"definition":243,"status":9,"industries":244,"functions":245,"patterns":247,"audience":24,"autonomy":249,"adoptionStage":220,"segment":27,"evidenceCount":250,"publicEvidenceCount":250,"organizations":251,"bestGrade":207,"headline":157,"lastVerified":258,"indexable":144},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","A conversational assistant that answers a wealth advisor's or relationship manager's questions in seconds from the firm's own research, house view, product documentation and policies, with every answer linked to the source document so the advisor can check it before using it with a client.",[16,195],[246,19,197],"knowledge-management",[248,200],"rag-knowledge-assistant","assist",8,[223,252,253,254,226,255,256,257],"Choreo","Citi","JPMorgan Chase","PIMCO","UBS","Yes Bank","2026-09-26",{"slug":260,"title":261,"shortTitle":262,"definition":263,"status":9,"industries":264,"functions":267,"patterns":270,"audience":24,"autonomy":25,"adoptionStage":272,"segment":27,"evidenceCount":273,"publicEvidenceCount":274,"organizations":275,"bestGrade":207,"headline":282,"lastVerified":208,"indexable":144},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[265,16,266],"capital-markets","professional-services",[268,18,269],"analytics-and-reporting","risk-management",[21,218,248,202,271],"prediction-and-scoring","early-adopters",7,6,[276,277,278,279,280,281],"Datasite","EQT","Freshfields","Ice Miller","Orrick","Rogo",{"kpi":233,"label":234,"unit":235,"n":181,"nUpTo":185,"kind":236,"value":283,"qualifier":284,"claimant":239,"organization":276,"vendorReported":144},80,"up-to",{"indexable":144,"reasons":286},[],[288,296,301,309,316,323,329,336,344,351,358,364,370,376,383,389,395,402,407,413,420,427,432,437,442,449,454,459,465,470,478,484,490,497,502,507],{"id":289,"label":290,"issuer":291,"region":292,"url":293,"description":294,"useCases":295,"indexable":144},"eu-ai-act","EU AI Act","European Union","europe","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.",250,{"id":86,"label":297,"issuer":291,"region":292,"url":298,"description":299,"useCases":300,"indexable":144},"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.",223,{"id":302,"label":303,"issuer":304,"region":305,"url":306,"description":307,"useCases":308,"indexable":144},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":310,"label":311,"issuer":312,"region":124,"url":313,"description":314,"useCases":315,"indexable":144},"nist-ai-rmf","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.",95,{"id":317,"label":318,"issuer":319,"region":292,"url":320,"description":321,"useCases":322,"indexable":144},"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.",73,{"id":324,"label":325,"issuer":291,"region":292,"url":326,"description":327,"useCases":328,"indexable":144},"dora","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.",67,{"id":330,"label":331,"issuer":332,"region":292,"url":333,"description":334,"useCases":335,"indexable":144},"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.",50,{"id":337,"label":338,"issuer":339,"region":340,"url":341,"description":342,"useCases":343,"indexable":144},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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.",37,{"id":345,"label":346,"issuer":347,"region":340,"url":348,"description":349,"useCases":350,"indexable":144},"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":352,"label":353,"issuer":354,"region":305,"url":355,"description":356,"useCases":357,"indexable":144},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":359,"label":360,"issuer":361,"region":124,"url":362,"description":363,"useCases":357,"indexable":144},"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":365,"label":366,"issuer":291,"region":292,"url":367,"description":368,"useCases":369,"indexable":144},"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.",17,{"id":371,"label":372,"issuer":373,"region":292,"url":374,"description":375,"useCases":369,"indexable":144},"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.",{"id":377,"label":378,"issuer":379,"region":124,"url":380,"description":381,"useCases":382,"indexable":144},"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.",16,{"id":384,"label":385,"issuer":386,"region":305,"url":387,"description":388,"useCases":237,"indexable":144},"fatf-recommendations","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.",{"id":390,"label":391,"issuer":291,"region":292,"url":392,"description":393,"useCases":394,"indexable":144},"eu-amlr","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":396,"label":397,"issuer":398,"region":124,"url":399,"description":400,"useCases":401,"indexable":144},"us-bsa","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":403,"label":404,"issuer":291,"region":292,"url":405,"description":406,"useCases":401,"indexable":144},"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.",{"id":408,"label":409,"issuer":410,"region":124,"url":411,"description":412,"useCases":401,"indexable":144},"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":414,"label":415,"issuer":416,"region":305,"url":417,"description":418,"useCases":419,"indexable":144},"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.",12,{"id":421,"label":422,"issuer":423,"region":124,"url":424,"description":425,"useCases":426,"indexable":144},"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.",11,{"id":428,"label":429,"issuer":291,"region":292,"url":430,"description":431,"useCases":426,"indexable":144},"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.",{"id":433,"label":434,"issuer":291,"region":292,"url":435,"description":436,"useCases":426,"indexable":144},"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":438,"label":439,"issuer":291,"region":292,"url":440,"description":441,"useCases":426,"indexable":144},"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":443,"label":444,"issuer":445,"region":292,"url":446,"description":447,"useCases":448,"indexable":144},"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.",10,{"id":450,"label":451,"issuer":339,"region":340,"url":452,"description":453,"useCases":448,"indexable":144},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":455,"label":456,"issuer":291,"region":292,"url":457,"description":458,"useCases":448,"indexable":144},"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":460,"label":461,"issuer":462,"region":124,"url":463,"description":464,"useCases":273,"indexable":144},"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.",{"id":466,"label":467,"issuer":291,"region":292,"url":468,"description":469,"useCases":273,"indexable":144},"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.",{"id":471,"label":472,"issuer":473,"region":474,"url":475,"description":476,"useCases":477,"indexable":144},"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.",5,{"id":479,"label":480,"issuer":481,"region":292,"url":482,"description":483,"useCases":137,"indexable":144},"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":485,"label":486,"issuer":487,"region":292,"url":488,"description":489,"useCases":137,"indexable":144},"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":491,"label":492,"issuer":493,"region":340,"url":494,"description":495,"useCases":496,"indexable":144},"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.",3,{"id":498,"label":499,"issuer":291,"region":292,"url":500,"description":501,"useCases":496,"indexable":144},"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":503,"label":504,"issuer":291,"region":292,"url":505,"description":506,"useCases":496,"indexable":144},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":508,"label":509,"issuer":510,"region":124,"url":511,"description":512,"useCases":496,"indexable":144},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783085461]