[{"data":1,"prerenderedAt":703},["ShallowReactive",2],{"uc-sanctions-screening-adjudication":3,"uc-regulations":503},{"useCase":4,"evidence":200,"blitsAiDeployments":390,"benchmarks":391,"indicative":398,"related":401,"indexability":501,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":18,"patterns":20,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":39,"indicativeValue":45,"macroEstimates":80,"feasibility":81,"implementation":95,"risk":135,"blitsAi":175,"faq":177,"related":187,"datePublished":194,"dateModified":194,"lastVerified":195,"changelog":196,"slug":199},"AI for sanctions screening alert adjudication","Sanctions screening adjudication","AI resolves sanctions and watchlist name matches, closing clear false positives and escalating real hits. UOB cut name screening false positives by 60% in a pilot.","published","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.",[11,12,13,14],"name screening alert adjudication","sanctions false positive reduction","payment screening alert review","watchlist match resolution",[16,17],"banking","payments",[19],"financial-crime-compliance",[21,22,23],"classification-and-routing","prediction-and-scoring","agentic-workflow",[25,26],"internal-tools","api","back-office","supervised-agent","early-adopters","middle-office","Missing a sanctioned party can bring enforcement action and large fines, so screening engines\nare tuned to match generously. A customer named Mohammed Ali, a ship with a common name or a\ncompany whose address contains a sanctioned city all create alerts, and most of them turn out to\nbe false positives. Each one needs an analyst to compare dates of birth, nationalities,\nidentifiers and context against the list entry.\n\nOn payments the pressure is time. An instant or cross border payment held for a name match\nbreaks the settlement promise to the customer, and a backlog on a busy day means delayed payroll\nor trade payments. On onboarding, screening alerts slow account opening. Meanwhile list updates\nafter a new sanctions package can raise alert volumes sharply overnight.",[],"1. **Parse the record.** Names, dates, addresses, identifiers and free text are extracted from\n   the customer record or the payment message, including structured ISO 20022 fields.\n2. **Resolve the match.** Entity resolution compares the record with the list entry across\n   transliterations, aliases, name order and cultural naming patterns, and weighs secondary\n   identifiers such as date of birth, nationality and registration numbers.\n3. **Score and explain.** A model estimates whether the alert is a true match and lists the\n   factors that support or contradict it, in words an analyst and an auditor can follow.\n4. **Decide under policy.** Alerts that meet approved criteria for a clear non match are closed\n   with that explanation stored. Possible and likely true matches go to an analyst, ranked by\n   risk, with the evidence side by side.\n5. **Assure.** A sample of automated closures is reviewed by a second analyst, and every list\n   update triggers regression tests on known true and false matches.",[35,36,37,38],"compliance","speed","employee-productivity","customer-experience",[40,41,42,43,44],"false-positive-reduction","automation-rate","processing-time-reduction","accuracy","productivity-gain",{"referenceOrg":46,"inputs":47,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"A bank screening payments and customers with 300,000 name screening alerts a year",[48,54,61,68],{"key":49,"label":50,"low":51,"high":51,"unit":52,"note":53},"alerts","Name and payment screening alerts per year",300000,"alerts per year","The reference bank.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"minutesPerAlert","Analyst minutes per alert today",3,8,"minutes per alert","Editorial assumption for level one review. Replace with your own time study.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"autoClosed","Share of alerts closed automatically as clear non matches",0.3,0.6,"fraction of alerts","Editorial assumption, kept at or below UOB's name screening pilot result on this page (60% fewer false positives on individual name alerts). Replace with results from your own parallel run.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"costPerHour","Fully loaded analyst cost per hour",35,60,"USD per hour","Editorial assumption. Replace with your own.","alerts * minutesPerAlert / 60 * autoClosed * costPerHour","USD","per year","Screening analyst capacity released","Counts analyst time only. It leaves out faster payment release and onboarding, lower penalty risk, and the cost of validation, list management and the platform.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"high","The matching problem is well understood, but a missed sanctions hit can lead to regulatory fines, as the Starling Bank case on this page shows. Validation, list management, change control and explainability carry more of the effort than the model.",[85,86,87,88],"Historical screening alerts with final decisions and reasons","Clean customer reference data with secondary identifiers (dates of birth, nationalities, registration numbers)","Current sanctions and watchlists with version history","Payment messages with structured party fields where available",[90,91,92,93,94],"Screening engine for customers and payments","Payment hub and message queues for held payments","Onboarding and customer data systems","Case management for escalated alerts","List management and watchlist data providers",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[97,100,103,106,109],{"title":98,"detail":99},"Tune the engine first","Before adding AI, fix data quality and fuzzy matching thresholds in the screening engine, and measure alert volume per list and per source. Some false positives are cheaper to prevent than to adjudicate.",{"title":101,"detail":102},"Build a labelled test set","Assemble historical alerts with final decisions plus known true matches and synthetic hard cases (aliases, transliterations, partial names) that every model version must pass.",{"title":104,"detail":105},"Run as a recommender","Show the model's recommendation and explanation to analysts for a full quarter and measure agreement, especially on the alerts analysts escalated.",{"title":107,"detail":108},"Automate only clear non matches","Approve auto closure for the band where secondary identifiers clearly contradict the list entry, and never for alerts where the model is uncertain.",{"title":110,"detail":111},"Govern list and model changes together","Re run the regression test set on every list update, model change and threshold change, and keep the results as evidence for auditors.",[113,114,115,116,117],"The model may close clear non matches but never a possible or confirmed true match","Every closure stores the explanation, the list version and the model version","Regression tests on known true matches run before every list, model or threshold change","Second analyst sampling of automated closures with a hard stop on errors","Screening coverage monitored so that no customer or payment skips screening when the AI service is down","Analysts decide every possible or likely true match and every payment rejection or asset freeze. A second line samples automated closures, and the sanctions compliance officer approves auto closure criteria and every change to them.",[120,121,122,123,124],"Alert volume and share closed automatically, per list and source","Error rate in second analyst sampling of automated closures","Time payments are held for screening","Agreement between model recommendation and analyst decision","Regression test pass rate on known true matches",[126,129,132],{"title":127,"detail":128},"A true hit closed automatically","The one failure that matters. Prevent it with conservative auto closure bands, regression tests on known matches and second analyst sampling.",{"title":130,"detail":131},"List update floods","A new sanctions package can raise alert volumes sharply overnight, and the model has not seen the new entries. Keep capacity plans and re test on every list update.",{"title":133,"detail":134},"Unexplainable decisions","A score without reasons cannot be defended to an examiner. Require the model to state the identifiers that support or contradict the match.",{"euAiAct":136,"regulations":139,"guidance":151,"controls":164,"incidents":170},{"tier":137,"basis":138},"minimal","Sanctions screening by banks and payment firms is not listed in Annex III: point 5 covers credit scoring and life and health insurance pricing, and point 6 covers AI used by or on behalf of law enforcement authorities. It is not a prohibited practice under Article 5, and as an internal tool it carries no Article 50 transparency duty. It still processes personal data at scale, so GDPR applies, and decisions that block a payment or freeze assets remain human decisions.",[140,141,142,143,144,145,146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","fatf-recommendations","dora","us-sr-11-7","mas-ai-risk-management","cbuae-ai-guidance","nist-ai-rmf","eu-amlr","mas-notice-626",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"A Framework for OFAC Compliance Commitments","US Office of Foreign Assets Control","north-america","https://ofac.treasury.gov/media/16331/download?inline","Lists sanctions screening software or filter faults among the root causes of apparent violations, which is why screening changes need testing and oversight.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Principles for Using Artificial Intelligence and Machine Learning in Financial Crime Compliance","Wolfsberg Group","global","https://wolfsberg-group.org/resources/202/93","The 2022 principles of a group of global banks for using AI and machine learning in financial crime compliance: legitimate purpose, proportionate use, design and technical expertise, accountability and oversight, and openness and transparency.",[165,166,167,168,169],"Written auto closure criteria approved by the sanctions compliance officer","Stored explanation, list version and model version for every decision","Regression test set of known true matches, run on every change","Second analyst sampling with a hard stop on errors","Model inventory entry with validation and change control",[171],{"title":172,"url":173,"note":174},"FCA fines Starling Bank for failings in its financial crime systems and controls","https://www.fca.org.uk/news/press-releases/fca-fines-starling-bank-failings-financial-crime-systems-and-controls","The FCA fined Starling Bank about GBP 29 million in 2024. From 2017 its automated screening system had screened customers against only a fraction of the full sanctions list, which shows why screening coverage and list completeness must be tested independently of any model.",{"howToBuild":176},"The screening engine stays in place. On Blits.ai the adjudication runs as an **agentic\nworkflow**, triggered through the API for each alert: the agent reads the alert and the list\nentry, calls **custom functions** to fetch secondary identifiers from customer and payment\nsystems, and applies the bank's adjudication procedures from a **knowledge base** with hybrid\nretrieval. It returns **structured output** with a recommendation and the identifiers that\nsupport or contradict the match.\n\nClosures and escalations follow **human in the loop approval** with a configurable threshold,\nand every run keeps a full audit trail. **Test suites** hold known true matches and hard cases\nand run before every change, **monitors** check the service on a schedule, and the platform is\nmodel agnostic with EU and UAE data residency.",[178,181,184],{"question":179,"answer":180},"Can AI clear sanctions alerts automatically?","Banks let AI close clear non matches under written criteria, with sampling and regression tests. It should never close a possible or likely true match, because a missed sanctioned party can bring enforcement action and large fines, as the FCA's GBP 29 million fine on Starling Bank for sanctions screening failings shows.",{"question":182,"answer":183},"What makes sanctions screening alerts so noisy?","Engines match generously to avoid misses, names are common and transliterated in many ways, and customer records often lack the secondary identifiers that would rule a match out. Better reference data reduces noise before any AI is added.",{"question":185,"answer":186},"Which banks use AI for screening adjudication?","Standard Chartered, HSBC and Mashreq have announced screening automation with Silent Eight, and UOB reported a 60 per cent reduction in false positives on individual name screening alerts in a six month pilot with Tookitaki. AJ Bell says it cut customer screening alert volume by 82 per cent with ComplyAdvantage. Most announcements disclose no production results, so insist on your own parallel run.",[188,189,190,191,192,193],"pep-and-adverse-media-screening","aml-alert-triage","trade-finance-crime-screening","payment-investigations-and-exceptions","perpetual-kyc","business-onboarding-and-ubo-discovery","2026-09-27","2026-09-26",[197],{"date":194,"note":198},"First published","sanctions-screening-adjudication",[201,242,265,295,324,345,365],{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":212,"stage":213,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":228,"sources":229,"verification":237,"grade":239,"id":240,"organizationSlug":241},"FNBO: agentic AI for enhanced due diligence and sanctions alerts with Nasdaq Verafin",[192,199],{"name":205,"anonymized":206,"country":207,"region":155,"industry":16},"First National Bank of Omaha (FNBO)",false,"US",[209],{"name":210,"role":211},"Nasdaq Verafin","platform","FNBO deployed Nasdaq Verafin's Agentic EDD Analyst and Agentic Sanctions Analyst, which remove manual information gathering across multiple systems for enhanced due diligence cases and sanctions alerts. The vendor reports that the bank spent 50% less time on these reviews and alerts and redirected investigator capacity to deeper analysis.","production",2026,[25],[217],"en",[219],{"kpi":220,"value":221,"unit":222,"qualifier":223,"period":224,"claimant":225,"quote":226,"sourceUrl":227},"handling-time-reduction",50,"percent","exact","per case, enhanced due diligence and sanctions alert reviews","independent","At First National Bank of Omaha, AI agents have begun taking on some of the work of human financial crime investigators, reducing the time that people spend on each case by 50%, according to bank executives.","https://www.americanbanker.com/news/how-fnbo-uses-agentic-ai-to-investigate-financial-crime",true,[230,233],{"url":231,"title":232,"publisher":210},"https://verafin.com/resource/fnbo-seizes-the-agentic-ai-advantage/","FNBO Seizes the Agentic AI Advantage",{"url":227,"title":234,"publisher":235,"date":236},"How FNBO uses agentic AI to investigate financial crime","American Banker","2026-06-24",{"level":238,"checkedAt":194},"source-verified","B","fnbo-verafin-agentic-edd-and-sanctions",null,{"title":243,"useCases":244,"organization":245,"vendors":248,"summary":251,"stage":252,"year":253,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":206,"sources":257,"verification":262,"grade":239,"id":263,"organizationSlug":264},"Standard Chartered: machine learning screening optimisation with Silent Eight",[199],{"name":246,"anonymized":206,"country":247,"region":161,"industry":16},"Standard Chartered","GB",[249],{"name":250,"role":211},"Silent Eight","Standard Chartered announced a partnership with Silent Eight to give its financial crime compliance teams machine learning and natural language processing for name screening. The system recommends whether a screening alert is a true or false match and explains the recommendation in a plain English narrative for the analyst. The announcement describes aims rather than results.","announced",2018,[25],[217],[],[258],{"url":259,"title":260,"publisher":246,"date":261},"https://www.sc.com/en/press-release/weve-partnered-with-regulatory-technology-firm-silent-eight/","We've partnered with Regulatory Technology firm Silent Eight","2018-07-09",{"level":238,"checkedAt":195},"standard-chartered-silent-eight-screening","standard-chartered",{"title":266,"useCases":267,"organization":268,"vendors":272,"summary":275,"stage":276,"year":253,"channels":277,"languages":278,"metrics":279,"outcomeDisclosed":228,"sources":287,"verification":292,"grade":239,"id":293,"organizationSlug":294},"UOB: machine learning pilot for name screening and transaction monitoring with Tookitaki",[199],{"name":269,"anonymized":206,"country":270,"region":271,"industry":16},"United Overseas Bank (UOB)","SG","asia-pacific",[273],{"name":274,"role":211},"Tookitaki","In a six month pilot reported in August 2018, UOB tested Tookitaki's Anti-Money Laundering Suite, with machine learning features co created by the bank, on top of its rule based name screening (against internal and external watch lists) and transaction monitoring, to separate genuine risk from false positives with explainable outputs. UOB reported large false positive reductions on name screening alerts and said it would progressively roll the solution out to customer risk assessment and sanctions screening, which it treats as processes separate from name screening.","pilot",[25],[217],[280,285],{"kpi":40,"value":72,"unit":222,"qualifier":223,"period":281,"claimant":282,"quote":283,"sourceUrl":284},"six month pilot, name screening alerts on individual names","organization","For name screening alerts, there was a 60 per cent and 50 per cent reduction in false positives","https://www.uobgroup.com/web-resources/uobgroup/pdf/newsroom/2018/UOB-and-Tookitaki-strengthen-combat-against-money-laundering.pdf",{"kpi":40,"value":221,"unit":222,"qualifier":223,"period":286,"claimant":282,"quote":283,"sourceUrl":284},"six month pilot, name screening alerts on corporate names",[288],{"url":284,"title":289,"publisher":290,"date":291},"UOB and Tookitaki strengthen combat against money laundering through co-created machine learning solution","UOB","2018-08-24",{"level":238,"checkedAt":195},"uob-tookitaki-name-screening-pilot","united-overseas-bank-uob",{"title":296,"useCases":297,"organization":298,"vendors":302,"summary":305,"stage":213,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":228,"sources":316,"verification":321,"grade":322,"id":323,"organizationSlug":241},"AJ Bell: machine learning customer screening with ComplyAdvantage",[199],{"name":299,"anonymized":206,"country":247,"region":300,"industry":301},"AJ Bell","europe","wealth-and-asset-management",[303],{"name":304,"role":211},"ComplyAdvantage","UK investment platform AJ Bell uses ComplyAdvantage's AI powered Customer Screening and Ongoing Monitoring, whose matching combines fuzzy logic with machine learning that learns aliases and global naming conventions. AJ Bell's Head of Financial Crime and MLRO says that optimising the system's settings cut alert volume by 82 percent, which lets analysts clear alerts faster and focus on the highest risk areas.",2025,[25],[217],[310],{"kpi":311,"value":312,"unit":222,"qualifier":223,"period":313,"claimant":282,"quote":314,"sourceUrl":315},"alert-volume-reduction",82,"customer screening alert volume","Through optimizing the levers within the system, we’ve been able to reduce our alert volume by 82 percent.","https://complyadvantage.com/customer-stories/aj-bell-reduces-alert-volumes-by-82-percent/",[317],{"url":315,"title":318,"publisher":304,"date":319,"archivedUrl":320},"AJ Bell Reduces Alert Volumes by 82% with AI-Powered Customer Screening","2025-08-05","https://web.archive.org/web/20251204194052/https://complyadvantage.com/customer-stories/aj-bell-reduces-alert-volumes-by-82-percent/",{"level":238,"checkedAt":195},"C","aj-bell-complyadvantage-customer-screening",{"title":325,"useCases":326,"organization":327,"vendors":330,"summary":333,"stage":252,"year":306,"channels":334,"languages":335,"metrics":337,"outcomeDisclosed":206,"sources":338,"verification":343,"grade":322,"id":344,"organizationSlug":241},"Ratepay: payment screening and transaction monitoring with Hawk, AI features planned",[199,189],{"name":328,"anonymized":206,"country":329,"region":300,"industry":17},"Ratepay","DE",[331],{"name":332,"role":211},"Hawk","Ratepay, a German provider of white label buy now pay later solutions and part of the Nexi Group, replaced its previous solution with Hawk's Payment Screening, which screens transactions in real time against global sanctions lists, and Hawk's AML Transaction Monitoring, with centralised case management for investigators and auditors. The vendor's story says Ratepay is now planning to add Hawk's AI technology for anomaly detection and false positive reduction, so the AI adjudication step this record is filed under is announced rather than live.",[25,26],[217,336],"de",[],[339],{"url":340,"title":341,"publisher":332,"date":342},"https://hawk.ai/news-press/how-ratepay-scaling-bnpl-solutions-aml-screening-technology-hawk","How Ratepay Is Scaling BNPL Solutions With AML & Screening Technology From Hawk","2025-08-07",{"level":238,"checkedAt":195},"ratepay-hawk-aml-screening",{"title":346,"useCases":347,"organization":348,"vendors":350,"summary":352,"stage":213,"year":353,"channels":354,"languages":355,"metrics":356,"outcomeDisclosed":206,"sources":357,"verification":363,"grade":322,"id":364,"organizationSlug":241},"HSBC: name screening, adverse media and transaction screening alert automation with Silent Eight",[199,188],{"name":349,"anonymized":206,"country":247,"region":161,"industry":16},"HSBC",[351],{"name":250,"role":211},"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,[25,26],[217],[],[358],{"url":359,"title":360,"publisher":361,"date":362},"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":238,"checkedAt":195},"hsbc-silent-eight-screening-automation",{"title":366,"useCases":367,"organization":368,"vendors":372,"summary":374,"stage":252,"year":353,"channels":375,"languages":376,"metrics":378,"outcomeDisclosed":206,"sources":379,"verification":388,"grade":322,"id":389,"organizationSlug":241},"Mashreq: name screening and adverse media alert adjudication with Silent Eight",[199,188],{"name":369,"anonymized":206,"country":370,"region":371,"industry":16},"Mashreq","AE","middle-east",[373],{"name":250,"role":211},"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.",[25],[217,377],"ar",[],[380,384],{"url":381,"title":382,"publisher":250,"date":383},"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":385,"title":382,"publisher":386,"date":387},"https://www.prnewswire.com/news-releases/mashreq-partners-with-silent-eight-for-compliance-alert-adjudication-302137038.html","PR Newswire","2024-05-08",{"level":238,"checkedAt":195},"mashreq-silent-eight-alert-adjudication",0,[392],{"kpi":40,"label":393,"unit":222,"aggregate":228,"higherIsBetter":228,"n":394,"nUpTo":390,"median":72,"min":72,"max":72,"byClaimant":395,"vendorOnly":206,"points":396},"False positive reduction",1,{"organization":394,"vendor":390,"regulator":390,"independent":390},[397],{"evidenceId":293,"organization":269,"value":72,"qualifier":223,"claimant":282,"grade":239,"pooled":228},{"low":399,"high":400},157500,1440000,[402,427,444,459,474,488],{"slug":188,"title":403,"shortTitle":404,"definition":405,"status":8,"industries":406,"functions":407,"patterns":409,"audience":413,"autonomy":414,"adoptionStage":29,"segment":30,"evidenceCount":415,"publicEvidenceCount":415,"organizations":416,"bestGrade":239,"headline":422,"lastVerified":194,"indexable":228},"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.",[16,17,301],[19,408],"onboarding-and-kyc",[410,411,21,412],"rag-knowledge-assistant","summarization","translation","employee-facing","copilot",7,[417,349,369,418,419,420,421],"Deutsche Bank","OCBC","Santander UK","Save the Children","Scotiabank",{"kpi":220,"label":423,"unit":222,"n":394,"nUpTo":394,"kind":424,"value":72,"qualifier":425,"claimant":426,"organization":420,"vendorReported":228},"Handling time reduction","reported","at-least","vendor",{"slug":189,"title":428,"shortTitle":429,"definition":430,"status":8,"industries":431,"functions":432,"patterns":433,"audience":413,"autonomy":28,"adoptionStage":29,"segment":30,"evidenceCount":58,"publicEvidenceCount":58,"organizations":435,"bestGrade":239,"headline":441,"lastVerified":194,"indexable":228},"AI for AML transaction monitoring alert triage","AML alert triage","Machine learning and AI agents that score anti money laundering alerts for genuine risk, close clear false positives with a written and stored rationale, and hand investigators the remaining alerts already enriched with the customer, counterparty and transaction context.",[16,17],[19],[22,434,23,411],"anomaly-detection",[436,437,349,438,328,439,269,440],"Australia Post","BMO and Amalgamated Bank","Nexo","Shift4","Uphold",{"kpi":40,"label":393,"unit":222,"n":442,"nUpTo":390,"kind":424,"value":443,"qualifier":223,"claimant":426,"organization":439,"vendorReported":228},2,86,{"slug":190,"title":445,"shortTitle":446,"definition":447,"status":8,"industries":448,"functions":449,"patterns":451,"audience":27,"autonomy":28,"adoptionStage":453,"segment":454,"evidenceCount":57,"publicEvidenceCount":57,"organizations":455,"bestGrade":322,"headline":241,"lastVerified":194,"indexable":228},"AI screening of trade finance transactions for trade based money laundering","Trade crime screening","AI that screens every trade finance transaction for financial crime risk: it checks parties, vessels and ports against sanctions and watchlists, tests goods descriptions against dual use and controlled goods lists, compares unit prices with benchmarks for over or under invoicing, and reads trade documents and messages for laundering red flags, then prepares a case narrative for a human investigator.",[16],[19,450],"operations",[452,434,21,411],"document-processing","emerging","specialized-businesses",[456,457,458],"ANZ, HSBC and Lloyds Banking Group","Stanbic Bank Uganda","United Bank Limited",{"slug":191,"title":460,"shortTitle":461,"definition":462,"status":8,"industries":463,"functions":464,"patterns":466,"audience":27,"autonomy":28,"adoptionStage":453,"segment":27,"evidenceCount":442,"publicEvidenceCount":442,"organizations":468,"bestGrade":239,"headline":471,"lastVerified":194,"indexable":228},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[16,17],[450,465],"customer-service",[23,452,21,467],"content-generation",[469,470],"BNY","JPMorgan Chase",{"kpi":41,"label":472,"unit":222,"n":394,"nUpTo":390,"kind":424,"value":473,"qualifier":425,"claimant":282,"organization":469,"vendorReported":206},"Automation rate",10,{"slug":192,"title":475,"shortTitle":476,"definition":477,"status":8,"industries":478,"functions":479,"patterns":480,"audience":27,"autonomy":28,"adoptionStage":453,"segment":30,"evidenceCount":481,"publicEvidenceCount":481,"organizations":482,"bestGrade":239,"headline":484,"lastVerified":195,"indexable":228},"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.",[16,17,301],[408,19],[23,452,410,411],5,[417,205,470,418,483],"Origin Bank",{"kpi":485,"label":486,"unit":222,"n":394,"nUpTo":390,"kind":424,"value":487,"qualifier":223,"claimant":282,"organization":470,"vendorReported":206},"cost-reduction","Cost reduction",40,{"slug":193,"title":489,"shortTitle":490,"definition":491,"status":8,"industries":492,"functions":494,"patterns":495,"audience":27,"autonomy":28,"adoptionStage":453,"segment":454,"evidenceCount":57,"publicEvidenceCount":57,"organizations":496,"bestGrade":322,"headline":499,"lastVerified":194,"indexable":228},"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.",[16,17,493],"capital-markets",[408,19],[452,23,21,411],[469,497,498],"Incore Bank","M-DAQ Global",{"kpi":41,"label":472,"unit":222,"n":394,"nUpTo":390,"kind":424,"value":500,"qualifier":223,"claimant":282,"organization":469,"vendorReported":206},25,{"indexable":228,"reasons":502},[],[504,510,515,522,528,533,539,546,552,558,565,570,577,583,588,593,600,606,612,618,624,630,634,639,644,651,657,662,668,673,680,686,692,697],{"id":140,"label":505,"issuer":506,"region":300,"url":507,"description":508,"useCases":509,"indexable":228},"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":511,"issuer":506,"region":300,"url":512,"description":513,"useCases":514,"indexable":228},"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":516,"label":517,"issuer":518,"region":161,"url":519,"description":520,"useCases":521,"indexable":228},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":148,"label":523,"issuer":524,"region":155,"url":525,"description":526,"useCases":527,"indexable":228},"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":144,"label":529,"issuer":506,"region":300,"url":530,"description":531,"useCases":532,"indexable":228},"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":142,"label":534,"issuer":535,"region":300,"url":536,"description":537,"useCases":538,"indexable":228},"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":540,"label":541,"issuer":542,"region":300,"url":543,"description":544,"useCases":545,"indexable":228},"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":146,"label":547,"issuer":548,"region":271,"url":549,"description":550,"useCases":551,"indexable":228},"MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":553,"label":554,"issuer":555,"region":271,"url":556,"description":557,"useCases":500,"indexable":228},"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":559,"label":560,"issuer":561,"region":161,"url":562,"description":563,"useCases":564,"indexable":228},"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":145,"label":566,"issuer":567,"region":155,"url":568,"description":569,"useCases":564,"indexable":228},"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":571,"label":572,"issuer":573,"region":300,"url":574,"description":575,"useCases":576,"indexable":228},"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":143,"label":578,"issuer":579,"region":161,"url":580,"description":581,"useCases":582,"indexable":228},"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":149,"label":584,"issuer":506,"region":300,"url":585,"description":586,"useCases":587,"indexable":228},"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":589,"label":590,"issuer":506,"region":300,"url":591,"description":592,"useCases":587,"indexable":228},"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":594,"label":595,"issuer":596,"region":155,"url":597,"description":598,"useCases":599,"indexable":228},"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":601,"label":602,"issuer":506,"region":300,"url":603,"description":604,"useCases":605,"indexable":228},"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":607,"label":608,"issuer":609,"region":155,"url":610,"description":611,"useCases":605,"indexable":228},"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":613,"label":614,"issuer":615,"region":161,"url":616,"description":617,"useCases":605,"indexable":228},"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":619,"label":620,"issuer":506,"region":300,"url":621,"description":622,"useCases":623,"indexable":228},"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":625,"label":626,"issuer":627,"region":155,"url":628,"description":629,"useCases":623,"indexable":228},"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":150,"label":631,"issuer":548,"region":271,"url":632,"description":633,"useCases":473,"indexable":228},"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":635,"label":636,"issuer":506,"region":300,"url":637,"description":638,"useCases":473,"indexable":228},"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":640,"label":641,"issuer":506,"region":300,"url":642,"description":643,"useCases":473,"indexable":228},"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":645,"label":646,"issuer":647,"region":300,"url":648,"description":649,"useCases":650,"indexable":228},"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":652,"label":653,"issuer":654,"region":155,"url":655,"description":656,"useCases":58,"indexable":228},"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":658,"label":659,"issuer":506,"region":300,"url":660,"description":661,"useCases":58,"indexable":228},"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":663,"label":664,"issuer":506,"region":300,"url":665,"description":666,"useCases":667,"indexable":228},"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":147,"label":669,"issuer":670,"region":371,"url":671,"description":672,"useCases":481,"indexable":228},"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":674,"label":675,"issuer":676,"region":300,"url":677,"description":678,"useCases":679,"indexable":228},"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":681,"label":682,"issuer":683,"region":300,"url":684,"description":685,"useCases":679,"indexable":228},"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":687,"label":688,"issuer":689,"region":271,"url":690,"description":691,"useCases":57,"indexable":228},"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":693,"label":694,"issuer":506,"region":300,"url":695,"description":696,"useCases":57,"indexable":228},"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":698,"label":699,"issuer":700,"region":155,"url":701,"description":702,"useCases":57,"indexable":228},"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.",1790598301523]