[{"data":1,"prerenderedAt":312},["ShallowReactive",2],{"uc-hub-function-financial-crime-compliance":3},{"type":4,"typeLabel":5,"term":6,"includeUnpublished":10,"indexable":11,"stats":12,"useCases":22,"benchmarks":238,"topIndustries":252,"topFunctions":264,"topPatterns":265,"stageMix":284,"regionMix":296,"organizations":311,"other":48},"function","Business function",{"id":7,"label":8,"description":9},"financial-crime-compliance","Financial crime compliance","Anti money laundering, sanctions screening, transaction monitoring and investigations.",false,true,{"useCases":13,"publicDeployments":14,"blitsAiDeployments":15,"organizations":16,"countries":17,"outcomeDisclosureRate":18,"gradeMix":19},12,42,0,37,15,50,{"A":15,"B":20,"C":21,"D":15},18,24,[23,50,69,97,120,141,154,173,189,202,214,227],{"slug":24,"title":25,"shortTitle":26,"definition":27,"status":28,"industries":29,"functions":32,"patterns":34,"audience":39,"autonomy":40,"adoptionStage":41,"segment":42,"evidenceCount":43,"publicEvidenceCount":43,"organizations":44,"bestGrade":47,"headline":48,"lastVerified":49,"indexable":11},"source-of-wealth-diligence","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.","published",[30,31],"wealth-and-asset-management","banking",[33,7],"onboarding-and-kyc",[35,36,37,38],"document-processing","agentic-workflow","content-generation","summarization","employee-facing","copilot","early-adopters","front-office",3,[45,46],"Bank of Singapore","Deutsche Bank","B",null,"2026-09-26",{"slug":51,"title":52,"shortTitle":53,"definition":54,"status":28,"industries":55,"functions":57,"patterns":59,"audience":39,"autonomy":40,"adoptionStage":61,"segment":62,"evidenceCount":63,"publicEvidenceCount":63,"organizations":64,"bestGrade":47,"headline":48,"lastVerified":49,"indexable":11},"suspicious-activity-report-drafting","AI copilot for SAR and STR narrative drafting","SAR and STR drafting","Generative AI that drafts the narrative of a single suspicious activity or suspicious transaction report from the investigation file (who, what, when, where, why and how), with every fact linked to its source record, so the investigator verifies, edits and files instead of starting from a blank page. It works case by case, unlike the periodic data returns of regulatory reporting.",[31,56],"payments",[7,58],"case-management",[37,38,60,36],"rag-knowledge-assistant","emerging","middle-office",4,[65,66,67,68],"Finshark","BMO and Amalgamated Bank","Nexo","Uphold",{"slug":70,"title":71,"shortTitle":72,"definition":73,"status":28,"industries":74,"functions":75,"patterns":76,"audience":39,"autonomy":79,"adoptionStage":41,"segment":62,"evidenceCount":80,"publicEvidenceCount":80,"organizations":81,"bestGrade":47,"headline":87,"lastVerified":96,"indexable":11},"aml-alert-triage","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.",[31,56],[7],[77,78,36,38],"prediction-and-scoring","anomaly-detection","supervised-agent",8,[82,66,83,67,84,85,86,68],"Australia Post","HSBC","Ratepay","Shift4","United Overseas Bank (UOB)",{"kpi":88,"label":89,"unit":90,"n":91,"nUpTo":15,"kind":92,"value":93,"qualifier":94,"claimant":95,"organization":85,"vendorReported":11},"false-positive-reduction","False positive reduction","percent",2,"reported",86,"exact","vendor","2026-09-27",{"slug":98,"title":99,"shortTitle":100,"definition":101,"status":28,"industries":102,"functions":104,"patterns":105,"audience":107,"autonomy":79,"adoptionStage":61,"segment":108,"evidenceCount":43,"publicEvidenceCount":43,"organizations":109,"bestGrade":113,"headline":114,"lastVerified":96,"indexable":11},"business-onboarding-and-ubo-discovery","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.",[31,56,103],"capital-markets",[33,7],[35,36,106,38],"classification-and-routing","back-office","specialized-businesses",[110,111,112],"BNY","Incore Bank","M-DAQ Global","C",{"kpi":115,"label":116,"unit":90,"n":117,"nUpTo":15,"kind":92,"value":118,"qualifier":94,"claimant":119,"organization":110,"vendorReported":10},"automation-rate","Automation rate",1,25,"organization",{"slug":121,"title":122,"shortTitle":123,"definition":124,"status":28,"industries":125,"functions":126,"patterns":128,"audience":39,"autonomy":40,"adoptionStage":41,"segment":129,"evidenceCount":130,"publicEvidenceCount":130,"organizations":131,"bestGrade":47,"headline":136,"lastVerified":49,"indexable":11},"market-abuse-surveillance-triage","AI for market abuse surveillance alert triage","Market abuse surveillance","AI that helps surveillance analysts triage market abuse and conduct alerts, such as spoofing, layering, wash trades, ramping and insider dealing, by gathering the trade, order, news and communications context, explaining in plain language what triggered each alert and drafting the investigation narrative for the analyst to disposition.",[103,31,30],[127,7],"regulatory-compliance",[78,36,38,106],"second-line",5,[132,46,133,134,135],"Commodity Futures Trading Commission","Japan Exchange Group","Nasdaq","U.S. Securities and Exchange Commission",{"kpi":137,"label":138,"unit":90,"n":117,"nUpTo":15,"kind":92,"value":139,"qualifier":140,"claimant":119,"organization":134,"vendorReported":10},"handling-time-reduction","Handling time reduction",33,"approximately",{"slug":142,"title":143,"shortTitle":144,"definition":145,"status":28,"industries":146,"functions":147,"patterns":149,"audience":107,"autonomy":40,"adoptionStage":41,"segment":62,"evidenceCount":43,"publicEvidenceCount":43,"organizations":150,"bestGrade":47,"headline":48,"lastVerified":96,"indexable":11},"mule-network-detection","AI for money mule account and network detection","Mule network detection","Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.",[31,56],[148,7],"fraud-prevention",[78,77,36,38],[151,152,153],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":155,"title":156,"shortTitle":157,"definition":158,"status":28,"industries":159,"functions":160,"patterns":161,"audience":39,"autonomy":40,"adoptionStage":41,"segment":62,"evidenceCount":163,"publicEvidenceCount":163,"organizations":164,"bestGrade":47,"headline":170,"lastVerified":96,"indexable":11},"pep-and-adverse-media-screening","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.",[31,56,30],[7,33],[60,38,106,162],"translation",7,[46,83,165,166,167,168,169],"Mashreq","OCBC","Santander UK","Save the Children","Scotiabank",{"kpi":137,"label":138,"unit":90,"n":117,"nUpTo":117,"kind":92,"value":171,"qualifier":172,"claimant":95,"organization":168,"vendorReported":11},60,"at-least",{"slug":174,"title":175,"shortTitle":176,"definition":177,"status":28,"industries":178,"functions":179,"patterns":180,"audience":107,"autonomy":79,"adoptionStage":61,"segment":62,"evidenceCount":130,"publicEvidenceCount":130,"organizations":181,"bestGrade":47,"headline":185,"lastVerified":49,"indexable":11},"perpetual-kyc","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.",[31,56,30],[33,7],[36,35,60,38],[46,182,183,166,184],"First National Bank of Omaha (FNBO)","JPMorgan Chase","Origin Bank",{"kpi":186,"label":187,"unit":90,"n":117,"nUpTo":15,"kind":92,"value":188,"qualifier":94,"claimant":119,"organization":183,"vendorReported":10},"cost-reduction","Cost reduction",40,{"slug":190,"title":191,"shortTitle":192,"definition":193,"status":28,"industries":194,"functions":196,"patterns":198,"audience":39,"autonomy":40,"adoptionStage":61,"segment":107,"evidenceCount":91,"publicEvidenceCount":91,"organizations":199,"bestGrade":47,"headline":48,"lastVerified":96,"indexable":11},"regulatory-report-assembly","AI for regulatory report assembly","Regulatory report assembly","AI that assembles periodic and data driven regulatory filings and returns, such as prudential and statistical returns, threshold and transaction reports and disclosure packs, by pulling data into the regulator's schema, validating it, reconciling figures to source, explaining movements against prior periods and drafting commentary, before a named officer reviews and submits. Narratives for individual suspicious activity cases are a separate use case.",[31,195,103,56],"insurance",[127,197,7],"finance-and-accounting",[36,78,37,38],[200,201],"Board of Governors of the Federal Reserve System","National Credit Union Administration",{"slug":203,"title":204,"shortTitle":205,"definition":206,"status":28,"industries":207,"functions":208,"patterns":209,"audience":107,"autonomy":79,"adoptionStage":41,"segment":62,"evidenceCount":163,"publicEvidenceCount":163,"organizations":210,"bestGrade":47,"headline":213,"lastVerified":49,"indexable":11},"sanctions-screening-adjudication","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.",[31,56],[7],[106,77,36],[211,182,83,165,84,212,86],"AJ Bell","Standard Chartered",{"kpi":88,"label":89,"unit":90,"n":117,"nUpTo":15,"kind":92,"value":171,"qualifier":94,"claimant":119,"organization":86,"vendorReported":10},{"slug":215,"title":216,"shortTitle":217,"definition":218,"status":28,"industries":219,"functions":220,"patterns":222,"audience":107,"autonomy":79,"adoptionStage":61,"segment":108,"evidenceCount":43,"publicEvidenceCount":43,"organizations":223,"bestGrade":113,"headline":48,"lastVerified":96,"indexable":11},"trade-finance-crime-screening","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.",[31],[7,221],"operations",[35,78,106,38],[224,225,226],"ANZ, HSBC and Lloyds Banking Group","Stanbic Bank Uganda","United Bank Limited",{"slug":228,"title":229,"shortTitle":230,"definition":231,"status":28,"industries":232,"functions":233,"patterns":235,"audience":107,"autonomy":79,"adoptionStage":41,"segment":62,"evidenceCount":117,"publicEvidenceCount":117,"organizations":236,"bestGrade":47,"headline":48,"lastVerified":96,"indexable":11},"dynamic-customer-risk-rating","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.",[31,56,30],[7,234],"risk-management",[77,78],[237],"bunq",[239],{"kpi":88,"label":89,"unit":90,"aggregate":11,"higherIsBetter":11,"n":63,"nUpTo":15,"median":240,"min":171,"max":241,"byClaimant":242,"vendorOnly":10,"points":243},73,95,{"organization":91,"vendor":91,"regulator":15,"independent":15},[244,246,248,250],{"evidenceId":245,"organization":169,"value":241,"qualifier":94,"claimant":95,"grade":113,"pooled":11},"scotiabank-workfusion-adverse-media-monitoring",{"evidenceId":247,"organization":85,"value":93,"qualifier":94,"claimant":95,"grade":113,"pooled":11},"shift4-thetaray-aml-transaction-monitoring",{"evidenceId":249,"organization":83,"value":171,"qualifier":94,"claimant":119,"grade":47,"pooled":11},"hsbc-dynamic-risk-assessment",{"evidenceId":251,"organization":86,"value":171,"qualifier":94,"claimant":119,"grade":47,"pooled":11},"uob-tookitaki-name-screening-pilot",[253,255,258,260,262],{"id":31,"label":254,"count":13},"Banking",{"id":56,"label":256,"count":257},"Payments and cards",9,{"id":30,"label":259,"count":130},"Wealth and asset management",{"id":103,"label":261,"count":43},"Capital markets",{"id":195,"label":263,"count":117},"Insurance",[],[266,269,271,274,276,278,280,282],{"id":38,"label":267,"count":268},"Summarization",10,{"id":36,"label":270,"count":257},"Agentic workflow",{"id":78,"label":272,"count":273},"Anomaly detection",6,{"id":106,"label":275,"count":130},"Classification and routing",{"id":35,"label":277,"count":63},"Document processing",{"id":77,"label":279,"count":63},"Prediction and scoring",{"id":37,"label":281,"count":43},"Content generation",{"id":60,"label":283,"count":43},"RAG knowledge assistant",[285,287,289,292,294],{"stage":286,"count":163},"announced",{"stage":288,"count":130},"pilot",{"stage":290,"count":291},"production",26,{"stage":293,"count":43},"scaled",{"stage":295,"count":117},"paused",[297,300,303,305,307,309],{"region":298,"count":299},"asia-pacific",13,{"region":301,"count":302},"north-america",11,{"region":304,"count":257},"europe",{"region":306,"count":163},"global",{"region":308,"count":117},"middle-east",{"region":310,"count":117},"africa",[211,152,224,82,66,110,45,151,200,132,46,65,182,83,111,183,133,112,165,134,201,67,166,184,84,153,167,168,169,85,225,212,135,226,86,68,237],1790598325085]