[{"data":1,"prerenderedAt":686},["ShallowReactive",2],{"uc-pep-and-adverse-media-screening":3,"uc-regulations":486},{"useCase":4,"evidence":192,"blitsAiDeployments":371,"benchmarks":372,"indicative":385,"related":388,"indexability":484,"includeUnpublished":198},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":19,"patterns":22,"channels":27,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":135,"blitsAi":168,"faq":170,"related":180,"datePublished":187,"dateModified":187,"lastVerified":187,"changelog":188,"slug":191},"AI for PEP and adverse media screening","PEP and adverse media screening","AI checks news and records for PEP links and adverse media, sets aside namesakes and cites sources. WorkFusion reports 95% fewer false positives at Scotiabank.","published","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.",[11,12,13,14],"adverse media screening","negative news screening","politically exposed person screening","AI powered media monitoring for KYC",[16,17,18],"banking","payments","wealth-and-asset-management",[20,21],"financial-crime-compliance","onboarding-and-kyc",[23,24,25,26],"rag-knowledge-assistant","summarization","classification-and-routing","translation",[28],"internal-tools","employee-facing","copilot","early-adopters","middle-office","Due diligence requires banks to know whether a customer, a director or a beneficial owner is a\npolitically exposed person or has been linked to crime, corruption or other serious wrongdoing.\nCurated databases cover only part of the world's news, and keyword searches on the open web\nreturn pages of irrelevant hits: people with the same name, old stories, opinion pieces.\n\nAnalysts read article after article to rule out namesakes, often in languages they do not speak,\nand the result is inconsistent. Real risk gets missed in the noise, while onboarding and periodic\nreviews slow down. The quality of the written conclusion, why a hit was or was not relevant, is\nwhat auditors check, and it often varies between analysts.",[],"1. **Search broadly.** For each subject the system queries curated risk databases, news\n   archives, court and regulatory records and the open web, in the languages that match the\n   subject's footprint.\n2. **Disambiguate.** Entity resolution compares each article's person or company with the\n   subject's known attributes (age, location, occupation, associated companies) and discards\n   look alikes with a stated reason.\n3. **Classify the risk.** Relevant articles are classified by risk category (fraud, corruption,\n   sanctions evasion, organised crime) and by credibility and recency of the source.\n4. **Summarise with citations.** The system writes a short summary of the credible findings,\n   translated where needed, with a link to every source article.\n5. **Analyst decides.** The analyst confirms relevance and source reliability, records the\n   disposition and decides whether it changes the customer's risk rating; monitoring continues\n   between reviews.",[37,38,39,40],"compliance","employee-productivity","speed","risk-reduction",[42,43,44,45,46,47,48],"false-positive-reduction","alert-volume-reduction","handling-time-reduction","processing-time-reduction","time-saved-per-task","productivity-gain","accuracy",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A bank running 40,000 adverse media reviews a year across onboarding and periodic reviews",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"reviews","Adverse media reviews per year",40000,"reviews per year","The reference bank.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"minutesPerReview","Analyst minutes per review today",15,40,"minutes per review","Editorial assumption. Replace with your own time study.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"timeSaved","Share of review time saved",0.3,0.5,"fraction of review time","Conservative against the benchmark on this page (Xapien reports that Save the Children cut donor due diligence review times by over 60% with its AI due diligence tool). Replace with results from your own pilot.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerHour","Fully loaded analyst cost per hour",35,60,"USD per hour","Editorial assumption. Replace with your own.","reviews * minutesPerReview / 60 * timeSaved * costPerHour","USD","per year","Analyst capacity released","Counts analyst time only. It leaves out faster onboarding, risk found that manual searches missed, data licence costs and the cost of the platform.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Retrieval and summarisation are mature. The hard parts are reliable disambiguation of common names, licensed access to news content, and keeping the analyst accountable for the conclusion.",[89,90,91,92],"Subject attributes for disambiguation (date of birth, nationality, addresses, related companies)","Licensed news and risk data sources, plus rules for which open web sources count as credible","The bank's adverse media risk taxonomy and materiality criteria","Historical dispositions to measure false positive rates",[94,95,96,97],"KYC and customer due diligence system","Screening engine and PEP database","News and risk data providers","Case management and customer risk rating",{"steps":99,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[100,103,106,109],{"title":101,"detail":102},"Define what counts as adverse","Write down the risk categories, how old a story may be, and which sources count as credible, with examples. The model can only be as consistent as the policy.",{"title":104,"detail":105},"Get disambiguation right","Measure how often the system wrongly matches or wrongly discards a namesake on a labelled sample, per language and naming culture, before analysts rely on it.",{"title":107,"detail":108},"Summaries with sources, never without","Require a link to every source in the summary and reject any claim that is not supported by a retrieved article.",{"title":110,"detail":111},"Pilot on periodic reviews","Start with periodic reviews of existing customers, where time pressure is lower, then extend to onboarding and continuous monitoring.",[113,114,115,116,117],"Every finding links to its source; unsupported statements are rejected","Adverse media changes a risk rating only after an analyst confirms relevance and reliability","Bias testing across names, nationalities and languages for both false hits and misses","Source articles and dispositions retained for audit","Licence terms respected for every news source","The system searches, filters and summarises; the analyst decides whether a finding is about the subject, whether it is credible and whether it matters. Changes to risk rating or relationship decisions stay with named people.",[120,121,122,123,124],"Hits per subject presented to analysts, before and after","Share of analyst overturned discards and matches on a labelled sample","Review time per subject","Material findings per thousand reviews","Miss rate on a known test set of adverse subjects",[126,129,132],{"title":127,"detail":128},"Namesake contamination","A common name links a customer to someone else's crimes. Require multiple matching attributes and show them in the summary.",{"title":130,"detail":131},"Language and culture bias","Disambiguation works well for some naming conventions and badly for others. Measure performance per language and naming culture.",{"title":133,"detail":134},"Summary replaces reading","Analysts stop opening sources. Sample decisions against the source articles and keep the analyst's conclusion in their own words.",{"euAiAct":136,"regulations":139,"guidance":149,"controls":162,"incidents":167},{"tier":137,"basis":138},"minimal","Adverse media and PEP screening for due diligence is not listed in Annex III. It processes personal data, including data about alleged offences, so GDPR Article 10 and national AML law govern what may be collected and how long it is kept.",[140,141,142,143,144,145,146,147,148],"eu-ai-act","gdpr","fatf-recommendations","mas-ai-risk-management","cbuae-ai-guidance","nist-ai-rmf","eu-amlr","us-bsa","mas-notice-626",[150,156],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"Principles for Using Artificial Intelligence and Machine Learning in Financial Crime Compliance","Wolfsberg Group","global","https://wolfsberg-group.org/resources/202/93","Industry principles for legitimate, proportionate and transparent use of AI in financial crime compliance.",{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Notice 626 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","Example of national rules on customer due diligence, PEP checks and ongoing monitoring.",[163,164,165,166],"Written adverse media policy with risk categories, recency and source credibility rules","Source links and analyst disposition retained for every finding","Bias and accuracy testing per language and naming culture","Human decision on any change to a risk rating or relationship",[],{"howToBuild":169},"On Blits.ai this is an **agentic workflow** with the **web search** and **web page browsing**\ntools, plus **custom functions** that call the bank's licensed news and risk data providers. The\nagent searches in the subject's languages, uses **language translation** where needed, compares\neach article with the subject's attributes and returns **structured output**: relevant findings,\ndiscarded look alikes with reasons, and a summary with a source link for every statement.\n\nThe bank's adverse media policy sits in a **knowledge base** with hybrid retrieval, so the agent\napplies the same categories and credibility rules every time. Changes to a risk rating go\nthrough **human in the loop approval**, runs keep a full audit trail, **test suites** hold known\nadverse subjects and namesakes, and the platform is model agnostic with EU and UAE data residency.",[171,174,177],{"question":172,"answer":173},"Is adverse media proof of risk?","No. It is an input to a risk decision. The analyst checks that the article is about the customer, that the source is credible and that the allegation is material before it changes a rating.",{"question":175,"answer":176},"How does AI reduce adverse media false positives?","Mostly through disambiguation: comparing ages, locations, occupations and related companies in the article with what the bank knows about the customer, and discarding namesakes with a stated reason. Measure it on a labelled sample in every language you screen.",{"question":178,"answer":179},"What about bias against certain names?","It is a real risk. Names that are common in a community, or that are transliterated from another script in several ways, can produce more false hits and so more manual scrutiny for some customers. Test false hit and miss rates per naming culture and language, and fix the gaps before scaling.",[181,182,183,184,185,186],"sanctions-screening-adjudication","perpetual-kyc","dynamic-customer-risk-rating","business-onboarding-and-ubo-discovery","source-of-wealth-diligence","vendor-due-diligence","2026-09-27",[189],{"date":187,"note":190},"First published","pep-and-adverse-media-screening",[193,220,251,274,300,327,351],{"title":194,"useCases":195,"organization":196,"vendors":200,"summary":203,"stage":204,"year":205,"channels":206,"languages":207,"metrics":209,"outcomeDisclosed":198,"sources":210,"verification":215,"grade":217,"id":218,"organizationSlug":219},"OCBC and Bank of Singapore: HELIOS agentic AI for customer due diligence in private banking",[191,182],{"name":197,"anonymized":198,"country":199,"region":159,"industry":16},"OCBC",false,"SG",[201],{"name":197,"role":202},"in-house","OCBC launched HELIOS in July 2026, an agentic AI platform that gathers intelligence on prospective private banking clients and completes most of the customer due diligence before a relationship manager engages them. OCBC says private banking accounts can now be opened in 15 business days, against an industry median of about six weeks, while relationship managers and review teams keep accountability for judgment and decisions. OCBC plans to extend HELIOS to ongoing monitoring of customer activity to detect changes in risk profiles. Bank of Singapore relationship managers use it in Singapore, Hong Kong and Dubai, with the rollout due to finish in the third quarter of 2026.","production",2026,[28],[208],"en",[],[211],{"url":212,"title":213,"publisher":197,"date":214},"https://www.ocbc.com/group/media/release/2026/ocbc-harnesses-agentic-ai-to-quicken-onboarding-customers.page","OCBC harnesses agentic AI to sharpen and quicken onboarding of wealthy customers","2026-07-29",{"level":216,"checkedAt":187},"source-verified","B","ocbc-helios-agentic-due-diligence",null,{"title":221,"useCases":222,"organization":223,"vendors":228,"summary":232,"stage":204,"year":205,"channels":233,"languages":234,"metrics":235,"outcomeDisclosed":243,"sources":244,"verification":248,"grade":249,"id":250,"organizationSlug":219},"Save the Children: AI due diligence reports on corporate donors with Xapien",[191],{"name":224,"anonymized":198,"country":225,"region":226,"industry":227},"Save the Children","GB","europe","cross-industry",[229],{"name":230,"role":231},"Xapien","platform","Save the Children uses Xapien, an AI supported due diligence platform that produces a report on a prospective donor and surfaces areas of concern early in the report, to vet corporate donors for alignment with its values and for reputational risk. The vendor reports that review times fell by more than 60%, with reports completed in as little as 15 minutes rather than over an afternoon, so the team can vet more donors. The platform is one part of a wider, human led review. It shows the same adverse media job outside banking.",[28],[208],[236],{"kpi":44,"value":76,"unit":237,"qualifier":238,"period":239,"claimant":240,"quote":241,"sourceUrl":242},"percent","at-least","analyst review time per corporate donor","vendor","Save the Children uses Xapien to accelerate corporate donor due diligence, cutting review times by over 60%.","https://xapien.com/case-studies/save-the-children-supporting-childrens-wellbeing-globally-one-xapien-report-at-a-time/",true,[245],{"url":242,"title":246,"publisher":230,"date":247},"Save the Children cuts donor due diligence time by 60%","2026-03-11",{"level":216,"checkedAt":187},"C","save-the-children-xapien-donor-due-diligence",{"title":252,"useCases":253,"organization":254,"vendors":256,"summary":259,"stage":204,"year":260,"channels":261,"languages":263,"metrics":264,"outcomeDisclosed":198,"sources":265,"verification":271,"grade":249,"id":273,"organizationSlug":219},"HSBC: name screening, adverse media and transaction screening alert automation with Silent Eight",[181,191],{"name":255,"anonymized":198,"country":225,"region":153,"industry":16},"HSBC",[257],{"name":258,"role":231},"Silent Eight","Silent Eight has supplied HSBC with automation for name screening and adverse media alerts, and in February 2024 the two expanded the partnership to automated alert closure for transaction screening, which investigates and resolves payment screening alerts in real time. No outcome figures are disclosed.",2024,[28,262],"api",[208],[],[266],{"url":267,"title":268,"publisher":269,"date":270},"https://www.prnewswire.com/news-releases/silent-eight-announces-expansion-of-partnership-with-hsbc-to-provide-transaction-screening-solutions-302067562.html","Silent Eight Announces Expansion of Partnership with HSBC To Provide Transaction Screening Solutions","Silent Eight via PR Newswire","2024-02-22",{"level":216,"checkedAt":272},"2026-09-26","hsbc-silent-eight-screening-automation",{"title":275,"useCases":276,"organization":277,"vendors":281,"summary":283,"stage":284,"year":260,"channels":285,"languages":286,"metrics":288,"outcomeDisclosed":198,"sources":289,"verification":298,"grade":249,"id":299,"organizationSlug":219},"Mashreq: name screening and adverse media alert adjudication with Silent Eight",[181,191],{"name":278,"anonymized":198,"country":279,"region":280,"industry":16},"Mashreq","AE","middle-east",[282],{"name":258,"role":231},"Mashreq selected Silent Eight in May 2024 to automate the adjudication of name screening and adverse media alerts related to sanctions and anti money laundering requirements. Under the plan, false positives are to be investigated and closed quickly and potential true positives escalated to Mashreq analysts. The announcement is a multi year partnership; no results are disclosed.","announced",[28],[208,287],"ar",[],[290,294],{"url":291,"title":292,"publisher":258,"date":293},"https://www.silenteight.com/blog/mashreq-partners-with-silent-eight-for-compliance-alert-adjudication","Mashreq Partners with Silent Eight for Compliance Alert Adjudication","2024-05-07",{"url":295,"title":292,"publisher":296,"date":297},"https://www.prnewswire.com/news-releases/mashreq-partners-with-silent-eight-for-compliance-alert-adjudication-302137038.html","PR Newswire","2024-05-08",{"level":216,"checkedAt":272},"mashreq-silent-eight-alert-adjudication",{"title":301,"useCases":302,"organization":303,"vendors":306,"summary":309,"stage":204,"year":310,"channels":311,"languages":312,"metrics":313,"outcomeDisclosed":243,"sources":320,"verification":324,"grade":249,"id":325,"organizationSlug":326},"Deutsche Bank: automated adverse media and PEP screening for new accounts and refreshes with WorkFusion",[191,182],{"name":304,"anonymized":198,"country":305,"region":153,"industry":16},"Deutsche Bank","DE",[307],{"name":308,"role":231},"WorkFusion","Deutsche Bank used WorkFusion's AI automation for screening work in anti money laundering, including adverse media monitoring and PEP checks for new accounts and refresh screenings, which had required large teams to scan news reports manually. For the KYC programme as a whole, the vendor reports shorter handling times, about 25,000 cases handled per quarter and tens of thousands of hours saved each year.",2020,[28],[208],[314],{"kpi":44,"value":315,"unit":237,"qualifier":316,"period":317,"claimant":240,"quote":318,"sourceUrl":319},50,"up-to","range of 25 to 50%, across the whole KYC programme (screening and document processing)","25–50% reduction in handling time","https://www.workfusion.com/customer-stories/deutsche-bank/",[321],{"url":319,"title":322,"publisher":308,"date":323},"Deutsche Bank Customer Story","2020-11-05",{"level":216,"checkedAt":187},"deutsche-bank-workfusion-screening-automation","deutsche-bank",{"title":328,"useCases":329,"organization":330,"vendors":334,"summary":336,"stage":204,"year":310,"channels":337,"languages":338,"metrics":339,"outcomeDisclosed":243,"sources":345,"verification":349,"grade":249,"id":350,"organizationSlug":219},"Scotiabank: AI adverse media monitoring for anti money laundering with WorkFusion",[191],{"name":331,"anonymized":198,"country":332,"region":333,"industry":16},"Scotiabank","CA","north-america",[335],{"name":308,"role":231},"Scotiabank automated its adverse media monitoring (negative news search) for anti money laundering with WorkFusion, applying the vendor's intelligent automation to the analysis and disposition of adverse media. The vendor reports a sharp fall in false positives, wider media search coverage (30 articles per name instead of 20) and the equivalent of more than a hundred compliance analysts freed for other work.",[28],[208],[340],{"kpi":42,"value":341,"unit":237,"qualifier":342,"claimant":240,"quote":343,"sourceUrl":344},95,"exact","95% reduction in false positives","https://www.workfusion.com/customer-stories/scotiabank/",[346],{"url":344,"title":347,"publisher":308,"date":348},"Scotiabank Customer Story","2020-11-12",{"level":216,"checkedAt":187},"scotiabank-workfusion-adverse-media-monitoring",{"title":352,"useCases":353,"organization":354,"vendors":356,"summary":359,"stage":204,"year":360,"channels":361,"languages":362,"metrics":363,"outcomeDisclosed":198,"sources":364,"verification":369,"grade":249,"id":370,"organizationSlug":219},"Santander UK: automated adverse media screening in digital onboarding with ComplyAdvantage",[191],{"name":355,"anonymized":198,"country":225,"region":226,"industry":16},"Santander UK",[357],{"name":358,"role":231},"ComplyAdvantage","Santander UK used ComplyAdvantage's adverse media screening, delivered through an API, as part of a digital onboarding proposition for corporate and SME customers, and screens every entity linked to an onboarding case. The vendor reports that the onboarding cycle fell from 12 days to 2 days on average; the figure covers the whole onboarding process, not the screening step alone.",2019,[262],[208],[],[365],{"url":366,"title":367,"publisher":358,"date":368},"https://complyadvantage.com/customer-stories/santander-case-study/","Santander: KYC/AML Case Study","2021-02-25",{"level":216,"checkedAt":187},"santander-uk-complyadvantage-onboarding-screening",0,[373,380],{"kpi":44,"label":374,"unit":237,"aggregate":243,"higherIsBetter":243,"n":375,"nUpTo":375,"median":76,"min":76,"max":76,"byClaimant":376,"vendorOnly":243,"points":377},"Handling time reduction",1,{"organization":371,"vendor":375,"regulator":371,"independent":371},[378,379],{"evidenceId":250,"organization":224,"value":76,"qualifier":238,"claimant":240,"grade":249,"pooled":243},{"evidenceId":325,"organization":304,"value":315,"qualifier":316,"claimant":240,"grade":249,"pooled":198},{"kpi":42,"label":381,"unit":237,"aggregate":243,"higherIsBetter":243,"n":375,"nUpTo":371,"median":341,"min":341,"max":341,"byClaimant":382,"vendorOnly":243,"points":383},"False positive reduction",{"organization":371,"vendor":375,"regulator":371,"independent":371},[384],{"evidenceId":350,"organization":331,"value":341,"qualifier":342,"claimant":240,"grade":249,"pooled":243},{"low":386,"high":387},105000,800000,[389,410,426,437,455,466],{"slug":181,"title":390,"shortTitle":391,"definition":392,"status":8,"industries":393,"functions":394,"patterns":395,"audience":398,"autonomy":399,"adoptionStage":31,"segment":32,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":217,"headline":407,"lastVerified":272,"indexable":243},"AI for sanctions screening alert adjudication","Sanctions screening adjudication","AI that works the alerts raised when customer, counterparty or payment names match sanctions and watchlists: it resolves fuzzy matches across transliterations, aliases and naming conventions, clears clear non matches with a documented reason, and escalates true or uncertain hits with the evidence attached.",[16,17],[20],[25,396,397],"prediction-and-scoring","agentic-workflow","back-office","supervised-agent",7,[402,403,255,278,404,405,406],"AJ Bell","First National Bank of Omaha (FNBO)","Ratepay","Standard Chartered","United Overseas Bank (UOB)",{"kpi":42,"label":381,"unit":237,"n":375,"nUpTo":371,"kind":408,"value":76,"qualifier":342,"claimant":409,"organization":406,"vendorReported":198},"reported","organization",{"slug":182,"title":411,"shortTitle":412,"definition":413,"status":8,"industries":414,"functions":415,"patterns":416,"audience":398,"autonomy":399,"adoptionStage":418,"segment":32,"evidenceCount":419,"publicEvidenceCount":419,"organizations":420,"bestGrade":217,"headline":423,"lastVerified":272,"indexable":243},"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.",[16,17,18],[21,20],[397,417,23,24],"document-processing","emerging",5,[304,403,421,197,422],"JPMorgan Chase","Origin Bank",{"kpi":424,"label":425,"unit":237,"n":375,"nUpTo":371,"kind":408,"value":62,"qualifier":342,"claimant":409,"organization":421,"vendorReported":198},"cost-reduction","Cost reduction",{"slug":183,"title":427,"shortTitle":428,"definition":429,"status":8,"industries":430,"functions":431,"patterns":433,"audience":398,"autonomy":399,"adoptionStage":31,"segment":32,"evidenceCount":375,"publicEvidenceCount":375,"organizations":435,"bestGrade":217,"headline":219,"lastVerified":187,"indexable":243},"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.",[16,17,18],[20,432],"risk-management",[396,434],"anomaly-detection",[436],"bunq",{"slug":184,"title":438,"shortTitle":439,"definition":440,"status":8,"industries":441,"functions":443,"patterns":444,"audience":398,"autonomy":399,"adoptionStage":418,"segment":445,"evidenceCount":446,"publicEvidenceCount":446,"organizations":447,"bestGrade":249,"headline":451,"lastVerified":187,"indexable":243},"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.",[16,17,442],"capital-markets",[21,20],[417,397,25,24],"specialized-businesses",3,[448,449,450],"BNY","Incore Bank","M-DAQ Global",{"kpi":452,"label":453,"unit":237,"n":375,"nUpTo":371,"kind":408,"value":454,"qualifier":342,"claimant":409,"organization":448,"vendorReported":198},"automation-rate","Automation rate",25,{"slug":185,"title":456,"shortTitle":457,"definition":458,"status":8,"industries":459,"functions":460,"patterns":461,"audience":29,"autonomy":30,"adoptionStage":31,"segment":463,"evidenceCount":446,"publicEvidenceCount":446,"organizations":464,"bestGrade":217,"headline":219,"lastVerified":272,"indexable":243},"AI agent for source of wealth due diligence in private banking","Source of wealth diligence","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.",[18,16],[21,20],[417,397,462,24],"content-generation","front-office",[465,304],"Bank of Singapore",{"slug":186,"title":467,"shortTitle":468,"definition":469,"status":8,"industries":470,"functions":473,"patterns":476,"audience":29,"autonomy":30,"adoptionStage":31,"segment":477,"evidenceCount":478,"publicEvidenceCount":478,"organizations":479,"bestGrade":217,"headline":219,"lastVerified":187,"indexable":243},"AI for third party and vendor risk due diligence","Vendor due diligence","AI that reviews a vendor's security questionnaires, SOC and assurance reports, contracts and model documentation against the organization's control requirements, researches the vendor's ownership, sanctions, financial health and adverse media, drafts the risk assessment for a human to approve and keeps the register of material service providers current with ongoing monitoring.",[227,16,471,472,17],"insurance","government",[474,432,475],"procurement","regulatory-compliance",[417,23,397,24],"second-line",4,[480,481,482,483],"U.S. Department of Justice","Internal Revenue Service","U.S. Department of Agriculture","U.S. Trade and Development Agency",{"indexable":243,"reasons":485},[],[487,493,498,505,511,517,524,531,536,542,549,555,562,567,572,577,583,589,595,601,607,613,617,622,627,634,641,646,652,657,663,669,675,680],{"id":140,"label":488,"issuer":489,"region":226,"url":490,"description":491,"useCases":492,"indexable":243},"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":494,"issuer":489,"region":226,"url":495,"description":496,"useCases":497,"indexable":243},"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":499,"label":500,"issuer":501,"region":153,"url":502,"description":503,"useCases":504,"indexable":243},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":145,"label":506,"issuer":507,"region":333,"url":508,"description":509,"useCases":510,"indexable":243},"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":512,"label":513,"issuer":489,"region":226,"url":514,"description":515,"useCases":516,"indexable":243},"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.",66,{"id":518,"label":519,"issuer":520,"region":226,"url":521,"description":522,"useCases":523,"indexable":243},"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":525,"label":526,"issuer":527,"region":226,"url":528,"description":529,"useCases":530,"indexable":243},"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":143,"label":532,"issuer":158,"region":159,"url":533,"description":534,"useCases":535,"indexable":243},"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":537,"label":538,"issuer":539,"region":159,"url":540,"description":541,"useCases":454,"indexable":243},"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":543,"label":544,"issuer":545,"region":153,"url":546,"description":547,"useCases":548,"indexable":243},"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":550,"label":551,"issuer":552,"region":333,"url":553,"description":554,"useCases":548,"indexable":243},"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":556,"label":557,"issuer":558,"region":226,"url":559,"description":560,"useCases":561,"indexable":243},"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":563,"issuer":564,"region":153,"url":565,"description":566,"useCases":61,"indexable":243},"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":146,"label":568,"issuer":489,"region":226,"url":569,"description":570,"useCases":571,"indexable":243},"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":573,"label":574,"issuer":489,"region":226,"url":575,"description":576,"useCases":571,"indexable":243},"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":147,"label":578,"issuer":579,"region":333,"url":580,"description":581,"useCases":582,"indexable":243},"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":584,"label":585,"issuer":489,"region":226,"url":586,"description":587,"useCases":588,"indexable":243},"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":590,"label":591,"issuer":592,"region":333,"url":593,"description":594,"useCases":588,"indexable":243},"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":596,"label":597,"issuer":598,"region":153,"url":599,"description":600,"useCases":588,"indexable":243},"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":602,"label":603,"issuer":489,"region":226,"url":604,"description":605,"useCases":606,"indexable":243},"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":608,"label":609,"issuer":610,"region":333,"url":611,"description":612,"useCases":606,"indexable":243},"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":148,"label":614,"issuer":158,"region":159,"url":160,"description":615,"useCases":616,"indexable":243},"MAS Notice 626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":618,"label":619,"issuer":489,"region":226,"url":620,"description":621,"useCases":616,"indexable":243},"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":623,"label":624,"issuer":489,"region":226,"url":625,"description":626,"useCases":616,"indexable":243},"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":628,"label":629,"issuer":630,"region":226,"url":631,"description":632,"useCases":633,"indexable":243},"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":635,"label":636,"issuer":637,"region":333,"url":638,"description":639,"useCases":640,"indexable":243},"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.",8,{"id":642,"label":643,"issuer":489,"region":226,"url":644,"description":645,"useCases":640,"indexable":243},"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":647,"label":648,"issuer":489,"region":226,"url":649,"description":650,"useCases":651,"indexable":243},"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":144,"label":653,"issuer":654,"region":280,"url":655,"description":656,"useCases":419,"indexable":243},"CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":658,"label":659,"issuer":660,"region":226,"url":661,"description":662,"useCases":478,"indexable":243},"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":664,"label":665,"issuer":666,"region":226,"url":667,"description":668,"useCases":478,"indexable":243},"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":670,"label":671,"issuer":672,"region":159,"url":673,"description":674,"useCases":446,"indexable":243},"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":676,"label":677,"issuer":489,"region":226,"url":678,"description":679,"useCases":446,"indexable":243},"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":681,"label":682,"issuer":683,"region":333,"url":684,"description":685,"useCases":446,"indexable":243},"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.",1790598300777]