[{"data":1,"prerenderedAt":608},["ShallowReactive",2],{"uc-source-of-wealth-diligence":3,"uc-regulations":407},{"useCase":4,"evidence":206,"blitsAiDeployments":293,"benchmarks":294,"indicative":305,"related":308,"indexability":405,"includeUnpublished":212},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":27,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":101,"risk":144,"blitsAi":182,"faq":184,"related":194,"datePublished":200,"dateModified":200,"lastVerified":201,"changelog":202,"slug":205},"AI agent for source of wealth due diligence in private banking","Source of wealth diligence","AI agents for source of wealth due diligence","AI agents draft source of wealth reports from client documents for human review. Bank of Singapore cut report writing from 10 days to one hour.","published","An AI agent that reads a prospective private client's documents, extracts and corroborates how their wealth was built, checks plausibility against benchmarks and external sources, and drafts the source of wealth and enhanced due diligence narrative for the relationship manager and compliance analyst, who decide on the risk rating and the relationship.",[12,13,14,15],"source of wealth report drafting","SoW assessment assistant","enhanced due diligence narrative drafting","private banking KYC agent",[17,18],"wealth-and-asset-management","banking",[20,21],"onboarding-and-kyc","financial-crime-compliance",[23,24,25,26],"document-processing","agentic-workflow","content-generation","summarization",[28],"internal-tools","employee-facing","copilot","early-adopters","front-office","Private banks must understand and document how a client acquired their total wealth, not only the\nfunds that arrive in the first account. For entrepreneurs, heirs and executives this means reading\nhundreds of pages of financial statements, tax notices, corporate filings, property valuations and\npayslips, reconciling them into a coherent story and writing it up. Bank of\nSingapore described this as work that took its relationship managers about ten days per report.\n\nThe work is subjective and varies with the experience of the person writing it, so reports are\ninconsistent, and gaps that surface in compliance review send the file back and delay the account\nopening. Regulators have pushed in both directions: after major money laundering cases they\nexpect more rigorous source of wealth work, and in Singapore the regulator has also asked private\nbanks to shorten account opening times.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"The Monetary Authority of Singapore asked private banks in May 2026 to cut account opening times to within one month by the end of 2026, from an average of six weeks or more, as reported by Global Business Outlook.","Bank of Singapore, DBS take AI route to accelerate wealth client onboarding (Global Business Outlook)","https://globalbusinessoutlook.com/banking-and-finance/bank-of-singapore-dbs-take-ai-route-to-accelerate-wealth-client-onboarding/",2026,"1. **Collect documents.** The relationship manager uploads the client's documents and the fact\n   find; the agent classifies them and lists what is missing for the client's profile.\n2. **Extract and reconcile.** Income, business sales, inheritances, investment gains and assets\n   are extracted with dates and amounts and reconciled into a wealth timeline.\n3. **Corroborate.** The agent checks plausibility against benchmarks (for example typical salary\n   for a role, company revenue) and approved external sources such as company registries and news,\n   alongside the separate PEP, sanctions and adverse media screening results.\n4. **Draft the narrative.** A structured source of wealth report is drafted in the bank's template,\n   citing the document and page behind each statement and flagging gaps and inconsistencies.\n5. **Human decision.** The relationship manager verifies and refines the draft; the compliance\n   analyst challenges it, requests more evidence if needed and decides the risk rating and whether\n   to proceed.",[42,43,44,45],"speed","compliance","employee-productivity","customer-experience",[47,48,49,50,51],"cycle-time-days","processing-time-reduction","time-saved-per-task","accuracy","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A private bank onboarding 2,000 new clients a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"newClients","New private clients needing a source of wealth report per year",2000,"clients per year","The reference bank.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"hoursPerReport","Staff hours per source of wealth report today",8,16,"hours per report","Editorial assumption for effort, not elapsed time. Bank of Singapore reports elapsed writing time fell from 10 days to one hour; replace with your own effort data.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"shareSaved","Share of effort saved with an AI drafted report",0.5,0.8,"fraction of effort","Conservative against the benchmark on this page, because verification and compliance review remain.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyCost","Blended hourly cost of relationship managers and analysts",80,150,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","newClients * hoursPerReport * shareSaved * hourlyCost","USD","per year","Value of staff time released from source of wealth reports","Leaves out the larger commercial effect of faster account opening (assets that arrive sooner, fewer abandoned onboardings) and the cost of tooling and external data.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"high","It touches a regulated financial crime control. It needs reliable document extraction across many formats and languages, approved external data sources, a clear template agreed with compliance, strict data security and a model risk review.",[92,93,94,95],"Source of wealth policy and report template per client type and jurisdiction","Client documents in digital form, with a secure upload path","Benchmark data for plausibility checks (salaries, company financials)","Access to company registries, adverse media and screening results",[97,98,99,100],"Client onboarding or KYC case management system","Document management and secure upload","Screening providers for PEP, sanctions and adverse media","Company registry and financial data providers",{"steps":102,"guardrails":118,"humanInTheLoop":124,"kpisToInstrument":125,"failureModes":131},[103,106,109,112,115],{"title":104,"detail":105},"Agree the report standard with compliance","Write down what a complete source of wealth report contains for each client type (entrepreneur, heir, executive, investor) and which evidence each statement needs.",{"title":107,"detail":108},"Start with extraction and drafting","Let the agent extract, reconcile and draft with citations to document pages, while all judgments stay with the relationship manager and analyst.",{"title":110,"detail":111},"Add corroboration sources carefully","Connect approved external sources one at a time and record which source supported which statement; never let the model rely on its own general knowledge as evidence.",{"title":113,"detail":114},"Measure quality, not only speed","Track compliance send backs, missing evidence and analyst edits alongside turnaround time, and compare with manually written reports.",{"title":116,"detail":117},"Keep data inside a controlled environment","Client wealth documents are highly sensitive; process them in a private or dedicated environment with strict access control, as Bank of Singapore does on its private cloud.",[119,120,121,122,123],"Every statement in the report cites a document page or an approved external source","The model never decides the risk rating or the onboarding outcome","Gaps and inconsistencies are flagged, not smoothed over","Client documents processed in a controlled environment with access limited to the case team","No use of the model's general knowledge as evidence of wealth","The relationship manager verifies and refines every draft before submission; the compliance analyst challenges it and decides the risk rating; senior management approval applies for PEPs and other high risk clients as the bank's policy requires.",[126,127,128,129,130],"Elapsed time from complete document set to submitted report","Compliance send back rate and reasons","Share of report statements with a valid citation","Analyst edit rate per report section","Time from first contact to account opening",[132,135,138,141],{"title":133,"detail":134},"Plausible but unsupported narrative","The draft reads well but a key wealth event has no evidence. Require a citation per statement and flag uncited text.",{"title":136,"detail":137},"Extraction errors in complex documents","Amounts or dates are misread from scanned statements or foreign language filings. Show source snippets next to extracted values for verification.",{"title":139,"detail":140},"Over reliance by reviewers","Analysts approve well written drafts with less challenge. Sample reports for independent review and track challenge rates.",{"title":142,"detail":143},"Data leakage","Sensitive documents reach systems or models outside the controlled environment. Enforce data residency, masking and access control.",{"euAiAct":145,"regulations":148,"guidance":158,"controls":175,"incidents":181},{"tier":146,"basis":147},"context-dependent","Anti money laundering due diligence is not listed in Annex III, so an assistant that drafts source of wealth reports for a human decision is not high risk by default. It becomes high risk if it adds remote biometric identification of the client (Annex III point 1(a); verification that only confirms a claimed identity is excluded) or feeds an assessment of a natural person's creditworthiness, for example for lending to the client (Annex III point 5(b)). GDPR Article 22 on solely automated decisions applies if it ever refused a client on its own.",[149,150,151,152,153,154,155,156,157],"eu-ai-act","gdpr","fatf-recommendations","mas-ai-risk-management","dora","iso-42001","eu-amlr","us-bsa","mas-notice-626",[159,165,171],{"title":160,"issuer":161,"region":162,"url":163,"note":164},"Notice 626 on Prevention of Money Laundering and Countering the Financing of Terrorism (Banks)","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering requirements for banks, including enhanced customer due diligence for politically exposed persons and other higher risk customers, such as establishing their source of wealth and source of funds.",{"title":166,"issuer":167,"region":168,"url":169,"note":170},"FG17/6: The treatment of politically exposed persons for anti-money laundering purposes","Financial Conduct Authority","europe","https://www.fca.org.uk/publications/finalised-guidance/fg17-6-treatment-politically-exposed-persons-peps-money-laundering","UK guidance on applying enhanced due diligence to politically exposed persons in proportion to the risk they present.",{"title":172,"issuer":161,"region":162,"url":173,"note":174},"Artificial Intelligence Model Risk Management (information paper)","https://www.mas.gov.sg/publications/monographs-or-information-paper/2024/artificial-intelligence-model-risk-management","Good practices for AI and generative AI model risk management observed in a MAS thematic review of banks, covering governance and oversight, risk management systems and processes, and development and deployment.",[176,177,178,179,180],"Inventory entry and model risk review for the drafting and extraction components","Report template and evidence standard approved by financial crime compliance","Citation coverage check before a report can be submitted","Independent sampling of AI drafted reports by a second line reviewer","Data residency, encryption and access controls on client documents",[],{"howToBuild":183},"On Blits.ai this is an **agentic workflow**: documents uploaded to the case are ingested (PDF,\nWord, Excel, images and Outlook email files), an **AI agent** extracts and reconciles wealth events with\n**structured output**, and **custom functions** call the bank's registry, benchmark and screening\nproviders through REST. The bank's source of wealth policy and templates sit in a **knowledge\nbase**, and the agent drafts the report with a citation per statement.\n\nThe workflow pauses for **human in the loop** review by the relationship manager and the analyst,\nand its **audit trail** keeps every run. **PII masking**, **tenant isolation** and deployment in\nthe **EU or UAE region** keep documents under the bank's control, and the **model agnostic**\nplatform lets the bank choose a model, or a regional one, per step. **Test suites** check\nextraction and citation quality on reference cases before each change goes live.",[185,188,191],{"question":186,"answer":187},"How much faster can source of wealth reports be?","Bank of Singapore says the time to write a report fell from 10 days to one hour with its Source of Wealth Assistant, with relationship managers verifying and refining each draft. Deutsche Bank has also put an agentic source of wealth solution live in Singapore and Hong Kong.",{"question":189,"answer":190},"Does the AI decide whether a client is accepted?","No. It drafts and flags; the relationship manager, the compliance analyst and, for high risk clients, senior management decide. Bank of Singapore has relationship managers verify each draft before internal review, and Deutsche Bank says that while tasks can be automated, accountability remains with its people.",{"question":192,"answer":193},"Can the AI verify wealth on its own?","It can check plausibility against benchmarks and approved sources and point out gaps, but every statement must be backed by a document or an approved external source, never by the model's general knowledge.",[195,196,197,198,199],"pep-and-adverse-media-screening","dynamic-customer-risk-rating","perpetual-kyc","digital-onboarding-assistant","business-onboarding-and-ubo-discovery","2026-09-27","2026-09-26",[203],{"date":200,"note":204},"First published","source-of-wealth-diligence",[207,249,273],{"title":208,"useCases":209,"organization":210,"vendors":214,"summary":218,"stage":219,"year":220,"channels":221,"languages":222,"metrics":224,"outcomeDisclosed":234,"sources":235,"verification":244,"grade":246,"id":247,"organizationSlug":248},"Bank of Singapore: Source of Wealth Assistant (SOWA)",[205],{"name":211,"anonymized":212,"country":213,"region":162,"industry":17},"Bank of Singapore",false,"SG",[215],{"name":216,"role":217},"OCBC","in-house","Bank of Singapore, the private bank of OCBC, rolled out an agentic AI tool that drafts source of wealth reports for know your customer due diligence. Relationship managers upload the client's documents (financial statements, tax notices, property valuations, corporate filings, payslips) and SOWA reviews them and generates a standardized report, checking plausibility against benchmarks such as salary and company revenue from Bank of Singapore and OCBC data. The relationship manager verifies and refines the draft before it goes to compliance. The bank says report writing time fell from 10 days to one hour, with fewer inconsistencies and omissions. The tool runs on the bank's private cloud.","production",2025,[28],[223],"en",[225],{"kpi":47,"value":226,"unit":227,"qualifier":228,"period":229,"baseline":230,"claimant":231,"quote":232,"sourceUrl":233},1,"hours","exact","per source of wealth report","10 days to write a source of wealth report before SOWA","organization","Time taken to write the report has been shortened from 10 days to one hour, with greater accuracy and consistency.","https://www.bankofsingapore.com/media-releases/2025/bank-of-singapore-deploys-agentic-ai-tool-to-automate-writing-of-source-of-wealth-reports.html",true,[236,239],{"url":233,"title":237,"publisher":211,"date":238},"Bank of Singapore deploys agentic AI tool to automate writing of source of wealth reports","2025-10-10",{"url":240,"title":241,"publisher":242,"date":243},"https://www.wealthbriefing.com/html/article.php/bank-of-singapore-deploys-ai-to-accelerate-source-of-wealth-verification","Bank of Singapore Deploys AI To Accelerate Source Of Wealth Verification","WealthBriefing","2025-10-13",{"level":245,"checkedAt":201},"source-verified","B","bank-of-singapore-source-of-wealth-assistant",null,{"title":250,"useCases":251,"organization":252,"vendors":253,"summary":254,"stage":255,"year":39,"channels":256,"languages":257,"metrics":258,"outcomeDisclosed":234,"sources":265,"verification":270,"grade":271,"id":272,"organizationSlug":248},"Bank of Singapore: HELIOS agentic AI platform for wealth client onboarding",[205],{"name":211,"anonymized":212,"country":213,"region":162,"industry":17},[],"In 2026 Bank of Singapore began using HELIOS, an agentic AI platform that streamlines due diligence and credit risk profiles when onboarding high net worth and ultra high net worth clients. More than 100 relationship managers (about a quarter) in Singapore, Hong Kong and Dubai had begun using it. Its chief executive said roughly 50 clients had been fully onboarded through it. The bank aims to cut account opening from more than 30 business days to 15, which is a target rather than a result. The move follows a request from the Monetary Authority of Singapore to shorten account opening times for private bank clients.","pilot",[28],[223],[259],{"kpi":51,"value":260,"unit":261,"qualifier":262,"period":263,"claimant":231,"quote":264,"sourceUrl":38},50,"count","approximately","clients fully onboarded at time of reporting","Jason Moo, Bank of Singapore's CEO, said that roughly 50 clients have been fully onboarded through the platform, taking the HNW and UHNW segments together.",[266],{"url":38,"title":267,"publisher":268,"date":269},"Bank of Singapore, DBS take AI route to accelerate wealth client onboarding","Global Business Outlook","2026-07-31",{"level":245,"checkedAt":201},"C","bank-of-singapore-agentic-wealth-onboarding",{"title":274,"useCases":275,"organization":276,"vendors":279,"summary":280,"stage":219,"year":39,"channels":281,"languages":282,"metrics":283,"outcomeDisclosed":212,"sources":284,"verification":290,"grade":271,"id":291,"organizationSlug":292},"Deutsche Bank: agentic AI for Source of Wealth checks in private bank onboarding",[198,205],{"name":277,"anonymized":212,"country":278,"region":162,"industry":18},"Deutsche Bank","DE",[],"Deutsche Bank Private Bank put an agentic AI solution live in its Singapore and Hong Kong booking centres that researches, documents and prepares Source of Wealth assessments, which the bank calls one of the most resource intensive parts of know your customer checks. It reads client documents alongside approved external data, flags gaps and inconsistencies, and hands the assessment to bank staff for review; relationship managers in Dubai use it for accounts booked in Singapore. The bank stresses that accountability stays with its people. No outcome figures are disclosed; the growth figures in the coverage are forecasts.",[28],[],[],[285],{"url":286,"title":287,"publisher":288,"date":289},"https://www.fstech.co.uk/fst/Deutsche_Bank_Rolls_Out_Agentic_AI_To_Streamline_KYC_Onboarding_In_Private_Banking.php","Deutsche Bank rolls out agentic AI to streamline KYC onboarding in private banking","FStech","2026-09-23",{"level":245,"checkedAt":201},"deutsche-bank-source-of-wealth-kyc-agent","deutsche-bank",0,[295,300],{"kpi":47,"label":296,"unit":227,"aggregate":212,"higherIsBetter":212,"n":226,"nUpTo":293,"median":226,"min":226,"max":226,"byClaimant":297,"vendorOnly":212,"points":298},"Cycle time",{"organization":226,"vendor":293,"regulator":293,"independent":293},[299],{"evidenceId":247,"organization":211,"value":226,"qualifier":228,"claimant":231,"grade":246,"pooled":234},{"kpi":51,"label":301,"unit":261,"aggregate":212,"higherIsBetter":234,"n":226,"nUpTo":293,"median":260,"min":260,"max":260,"byClaimant":302,"vendorOnly":212,"points":303},"Interactions handled",{"organization":226,"vendor":293,"regulator":293,"independent":293},[304],{"evidenceId":272,"organization":211,"value":260,"qualifier":262,"claimant":231,"grade":271,"pooled":234},{"low":306,"high":307},640000,3840000,[309,336,350,367,389],{"slug":195,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":315,"patterns":316,"audience":29,"autonomy":30,"adoptionStage":31,"segment":320,"evidenceCount":321,"publicEvidenceCount":321,"organizations":322,"bestGrade":246,"headline":328,"lastVerified":200,"indexable":234},"AI for PEP and adverse media screening","PEP and adverse media screening","AI that continuously scans news, court records, registries and other open sources in many languages for negative information and political exposure linked to customers, counterparties and beneficial owners, discards look alikes, and summarises credible risk for the analyst with the sources attached.",[18,314,17],"payments",[21,20],[317,26,318,319],"rag-knowledge-assistant","classification-and-routing","translation","middle-office",7,[277,323,324,216,325,326,327],"HSBC","Mashreq","Santander UK","Save the Children","Scotiabank",{"kpi":329,"label":330,"unit":331,"n":226,"nUpTo":226,"kind":332,"value":333,"qualifier":334,"claimant":335,"organization":326,"vendorReported":234},"handling-time-reduction","Handling time reduction","percent","reported",60,"at-least","vendor",{"slug":196,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":341,"patterns":343,"audience":346,"autonomy":347,"adoptionStage":31,"segment":320,"evidenceCount":226,"publicEvidenceCount":226,"organizations":348,"bestGrade":246,"headline":248,"lastVerified":200,"indexable":234},"Dynamic AML customer risk rating with machine learning","Dynamic customer risk rating","Explainable machine learning that produces the money laundering risk rating itself: it computes and continuously updates each customer's rating from due diligence data, products, geography, behaviour and screening results, and shows which factors drive the rating and when enhanced due diligence is warranted.",[18,314,17],[21,342],"risk-management",[344,345],"prediction-and-scoring","anomaly-detection","back-office","supervised-agent",[349],"bunq",{"slug":197,"title":351,"shortTitle":352,"definition":353,"status":9,"industries":354,"functions":355,"patterns":356,"audience":346,"autonomy":347,"adoptionStage":357,"segment":320,"evidenceCount":358,"publicEvidenceCount":358,"organizations":359,"bestGrade":246,"headline":363,"lastVerified":201,"indexable":234},"AI for perpetual KYC and event driven customer due diligence","Perpetual KYC","AI that keeps each customer's due diligence file current by replacing calendar driven KYC reviews with continuous, event driven refreshes: it watches for trigger events such as a change of ownership, address, behaviour or a new adverse finding, refreshes the file automatically where it can, and involves an analyst only when something material has changed. The risk rating itself and the first file for a new business client are separate use cases.",[18,314,17],[20,21],[24,23,317,26],"emerging",5,[277,360,361,216,362],"First National Bank of Omaha (FNBO)","JPMorgan Chase","Origin Bank",{"kpi":364,"label":365,"unit":331,"n":226,"nUpTo":293,"kind":332,"value":366,"qualifier":228,"claimant":231,"organization":361,"vendorReported":212},"cost-reduction","Cost reduction",40,{"slug":198,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":375,"audience":378,"autonomy":347,"adoptionStage":31,"segment":32,"evidenceCount":379,"publicEvidenceCount":380,"organizations":381,"bestGrade":271,"headline":384,"lastVerified":200,"indexable":234},"AI assistant for digital account onboarding and KYC","Digital onboarding","A customer facing AI assistant that guides a new applicant, a person or a small merchant, through a digital account, card or relationship application: it collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[18,314,17],[20,373,374],"sales","customer-service",[376,23,377,24],"conversational-agent","computer-vision","customer-facing",6,3,[382,277,383],"Albo","M-DAQ Global",{"kpi":385,"label":386,"unit":387,"n":226,"nUpTo":293,"kind":332,"value":388,"qualifier":228,"claimant":335,"organization":383,"vendorReported":234},"productivity-gain","Productivity gain","multiplier",30,{"slug":199,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":395,"patterns":396,"audience":346,"autonomy":347,"adoptionStage":357,"segment":397,"evidenceCount":380,"publicEvidenceCount":380,"organizations":398,"bestGrade":271,"headline":401,"lastVerified":200,"indexable":234},"AI for business onboarding (KYB) and beneficial ownership discovery","Business onboarding and UBO","An AI agent that builds the know your business (KYB) due diligence file for a new or reviewed corporate client, before any account is opened: it collects registry, incorporation and ownership documents, resolves the entity across sources, maps the ownership chain through holding companies, nominees and trusts to the ultimate beneficial owners, screens the entity and its owners, and presents a risk scored case for a compliance analyst to decide.",[18,314,394],"capital-markets",[20,21],[23,24,318,26],"specialized-businesses",[399,400,383],"BNY","Incore Bank",{"kpi":402,"label":403,"unit":331,"n":226,"nUpTo":293,"kind":332,"value":404,"qualifier":228,"claimant":231,"organization":399,"vendorReported":212},"automation-rate","Automation rate",25,{"indexable":234,"reasons":406},[],[408,414,419,426,434,439,446,452,457,463,470,476,482,488,493,498,504,510,516,522,528,534,538,543,548,555,561,566,571,578,585,591,597,602],{"id":149,"label":409,"issuer":410,"region":168,"url":411,"description":412,"useCases":413,"indexable":234},"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":150,"label":415,"issuer":410,"region":168,"url":416,"description":417,"useCases":418,"indexable":234},"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":154,"label":420,"issuer":421,"region":422,"url":423,"description":424,"useCases":425,"indexable":234},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":427,"label":428,"issuer":429,"region":430,"url":431,"description":432,"useCases":433,"indexable":234},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":153,"label":435,"issuer":410,"region":168,"url":436,"description":437,"useCases":438,"indexable":234},"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":440,"label":441,"issuer":442,"region":168,"url":443,"description":444,"useCases":445,"indexable":234},"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":447,"label":448,"issuer":167,"region":168,"url":449,"description":450,"useCases":451,"indexable":234},"uk-consumer-duty","FCA Consumer Duty","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":152,"label":453,"issuer":161,"region":162,"url":454,"description":455,"useCases":456,"indexable":234},"MAS AI risk management guidelines","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":458,"label":459,"issuer":460,"region":162,"url":461,"description":462,"useCases":404,"indexable":234},"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.",{"id":464,"label":465,"issuer":466,"region":422,"url":467,"description":468,"useCases":469,"indexable":234},"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":471,"label":472,"issuer":473,"region":430,"url":474,"description":475,"useCases":469,"indexable":234},"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":477,"label":478,"issuer":479,"region":168,"url":480,"description":481,"useCases":65,"indexable":234},"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":151,"label":483,"issuer":484,"region":422,"url":485,"description":486,"useCases":487,"indexable":234},"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":155,"label":489,"issuer":410,"region":168,"url":490,"description":491,"useCases":492,"indexable":234},"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":494,"label":495,"issuer":410,"region":168,"url":496,"description":497,"useCases":492,"indexable":234},"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":156,"label":499,"issuer":500,"region":430,"url":501,"description":502,"useCases":503,"indexable":234},"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":505,"label":506,"issuer":410,"region":168,"url":507,"description":508,"useCases":509,"indexable":234},"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":511,"label":512,"issuer":513,"region":430,"url":514,"description":515,"useCases":509,"indexable":234},"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":517,"label":518,"issuer":519,"region":422,"url":520,"description":521,"useCases":509,"indexable":234},"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":523,"label":524,"issuer":410,"region":168,"url":525,"description":526,"useCases":527,"indexable":234},"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":529,"label":530,"issuer":531,"region":430,"url":532,"description":533,"useCases":527,"indexable":234},"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":157,"label":535,"issuer":161,"region":162,"url":163,"description":536,"useCases":537,"indexable":234},"MAS Notice 626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":539,"label":540,"issuer":410,"region":168,"url":541,"description":542,"useCases":537,"indexable":234},"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":544,"label":545,"issuer":410,"region":168,"url":546,"description":547,"useCases":537,"indexable":234},"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":549,"label":550,"issuer":551,"region":168,"url":552,"description":553,"useCases":554,"indexable":234},"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":556,"label":557,"issuer":558,"region":430,"url":559,"description":560,"useCases":64,"indexable":234},"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":562,"label":563,"issuer":410,"region":168,"url":564,"description":565,"useCases":64,"indexable":234},"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":567,"label":568,"issuer":410,"region":168,"url":569,"description":570,"useCases":379,"indexable":234},"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":572,"label":573,"issuer":574,"region":575,"url":576,"description":577,"useCases":358,"indexable":234},"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":579,"label":580,"issuer":581,"region":168,"url":582,"description":583,"useCases":584,"indexable":234},"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.",4,{"id":586,"label":587,"issuer":588,"region":168,"url":589,"description":590,"useCases":584,"indexable":234},"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":592,"label":593,"issuer":594,"region":162,"url":595,"description":596,"useCases":380,"indexable":234},"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":598,"label":599,"issuer":410,"region":168,"url":600,"description":601,"useCases":380,"indexable":234},"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":603,"label":604,"issuer":605,"region":430,"url":606,"description":607,"useCases":380,"indexable":234},"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.",1790598295964]