[{"data":1,"prerenderedAt":757},["ShallowReactive",2],{"uc-aml-alert-triage":3,"uc-regulations":555},{"useCase":4,"evidence":224,"blitsAiDeployments":449,"benchmarks":450,"indicative":469,"related":472,"indexability":553,"includeUnpublished":230},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":18,"patterns":20,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":49,"valueDrivers":50,"kpis":55,"indicativeValue":62,"macroEstimates":97,"feasibility":98,"implementation":112,"risk":155,"blitsAi":200,"faq":202,"related":212,"datePublished":219,"dateModified":219,"lastVerified":219,"changelog":220,"slug":223},"AI for AML transaction monitoring alert triage","AML alert triage","AI scores AML alerts, closes clear false positives and ranks the rest for investigators. HSBC reports 60% fewer false positive cases using machine learning.","published","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.",[11,12,13,14],"AML alert scoring","transaction monitoring alert prioritisation","AML false positive reduction","level one AML investigation automation",[16,17],"banking","payments",[19],"financial-crime-compliance",[21,22,23,24],"prediction-and-scoring","anomaly-detection","agentic-workflow","summarization",[26,27],"internal-tools","agent-desktop","employee-facing","supervised-agent","early-adopters","middle-office","Transaction monitoring takes a large share of the effort in anti money laundering. Rule based\nscenarios (thresholds, rapid movement of funds, high risk geographies) are tuned to miss nothing,\nso they generate large volumes of alerts, and the great majority close as false positives after\nan analyst has pulled statements, looked up counterparties and written a note.\n\nThe cost is not only money. Investigators spend their time clearing noise, real laundering\nhides in the backlog, and the quality of the written rationale varies from analyst to analyst,\nwhich is exactly what supervisors test. Adding people does not scale with payment volumes on\ninstant rails.",[34,39,44],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"The UN Office on Drugs and Crime reported in 2011 that criminals may have laundered around USD 1.6 trillion, or 2.7% of global GDP, in 2009, consistent with a 2 to 5% of global GDP range previously established by the International Monetary Fund.","UNODC estimates that criminals may have laundered US$ 1.6 trillion in 2009","https://www.unodc.org/unodc/en/press/releases/2011/October/unodc-estimates-that-criminals-may-have-laundered-usdollar-1.6-trillion-in-2009.html",2011,{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"Google Cloud, citing industry reporting, stated in 2023 that more than 95% of system generated alerts turn out to be false positives in the first phase of review, and that about 98% never lead to a suspicious activity report.","Google Cloud Launches AI-Powered Anti Money Laundering Product for Financial Institutions","https://www.googlecloudpresscorner.com/2023-06-21-Google-Cloud-Launches-AI-Powered-Anti-Money-Laundering-Product-for-Financial-Institutions",2023,{"statement":45,"sourceTitle":46,"sourceUrl":47,"year":48},"The Hong Kong Monetary Authority reported in November 2025 that 48 authorized institutions had assessed AI for transaction monitoring and that more than 30% of authorized institutions had already adopted it in their monitoring systems, with use cases concentrated in risk detection and alert prioritisation.","Supporting Artificial Intelligence Adoption in AML/CFT","https://brdr.hkma.gov.hk/eng/doc-ldg/docId/getPdf/20251118-3-EN/20251118-3-EN.pdf",2025,"1. **Score the alert.** A model trained on past alert outcomes, and increasingly on the full\n   customer and transaction picture rather than the rule hit alone, scores each alert for the\n   likelihood that it leads to a suspicious activity report.\n2. **Enrich it.** An agent gathers the evidence an analyst would: customer due diligence data,\n   expected activity, the transactions behind the alert, counterparties, screening results and\n   previous alerts or reports on the customer.\n3. **Draft the rationale.** For each alert the agent writes a structured narrative: what\n   triggered it, what the evidence shows and a proposed disposition, with every fact linked to\n   its source record.\n4. **Close or escalate under policy.** Alerts that meet approved low risk criteria are closed\n   with the stored rationale; the rest go to investigators, ranked by risk, with the evidence\n   pack attached.\n5. **Learn and assure.** Investigator decisions and quality assurance findings are fed back, and\n   a random sample of closed alerts is reviewed independently.",[51,52,53,54],"compliance","employee-productivity","cost-to-serve","risk-reduction",[56,57,58,59,60,61],"false-positive-reduction","alert-volume-reduction","detection-rate-improvement","processing-time-reduction","automation-rate","productivity-gain",{"referenceOrg":63,"inputs":64,"formula":92,"currency":93,"period":94,"resultLabel":95,"caveat":96},"A mid sized bank working 100,000 transaction monitoring alerts a year",[65,71,78,85],{"key":66,"label":67,"low":68,"high":68,"unit":69,"note":70},"alerts","Transaction monitoring alerts per year",100000,"alerts per year","The reference bank.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"hoursPerAlert","Analyst hours per alert today",0.5,1,"hours per alert","Editorial assumption for level one review including documentation. Replace with your own time study.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"alertReduction","Share of alert workload removed by scoring and auto closure",0.2,0.4,"fraction of alerts","Conservative against HSBC's reported 60% fewer false positive cases, because most banks keep rules in place alongside the model at first.",{"key":86,"label":87,"low":88,"high":89,"unit":90,"note":91},"costPerHour","Fully loaded analyst cost per hour",40,70,"USD per hour","Editorial assumption. Replace with your own.","alerts * hoursPerAlert * alertReduction * costPerHour","USD","per year","Investigation capacity released","Counts analyst time only. It leaves out the value of finding more genuine laundering, lower regulatory risk, the cost of model validation and data work, and the effort of running rules and models in parallel during the transition.",[],{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"high","Scoring and drafting are proven, but auto closing an AML alert is a regulated decision. Expect model validation, a parallel run against the existing process and a conversation with the supervisor before any alert is closed without a human.",[102,103,104,105],"Several years of alerts with final dispositions, and which ones led to a report","Customer due diligence data, expected activity and risk rating","Transaction and counterparty data at the level of detail the investigator uses","Written investigation procedures and quality assurance standards",[107,108,109,110,111],"Transaction monitoring system","AML case management","Customer due diligence and KYC records","Core banking and payment data","Screening results (sanctions, PEP, adverse media)",{"steps":113,"guardrails":129,"humanInTheLoop":135,"kpisToInstrument":136,"failureModes":142},[114,117,120,123,126],{"title":115,"detail":116},"Measure the baseline","Record alert volumes, conversion to reports, handling time and quality assurance findings per scenario. Without this baseline no one can show the model is better, including to the supervisor.",{"title":118,"detail":119},"Build the evidence pack before the score","Start with an agent that enriches alerts and drafts rationales for investigators. It delivers time savings early, carries little regulatory risk and generates the labelled data for scoring.",{"title":121,"detail":122},"Validate the scoring model","Train on historical dispositions, test against a hold out period, and have model risk validate it, including a check that alerts later reported as suspicious would not have been closed.",{"title":124,"detail":125},"Run in parallel","Score and draft on live alerts while investigators still work everything, and compare decisions for at least one full quarter before closing anything automatically.",{"title":127,"detail":128},"Introduce governed auto closure","Close only the lowest risk band, per scenario, with a stored rationale and independent sampling, and report the results to the money laundering reporting officer.",[130,131,132,133,134],"No alert on a high risk customer, a sanctions nexus or a prior report is closed without a human","Every closure stores the rationale, the evidence and the model version used","Independent sampling of auto closed alerts, with a threshold that stops auto closure","Facts in drafted narratives link to source records, and unsupported claims are rejected","Model inventory entry, validation and ongoing performance monitoring","Investigators decide every escalated alert and every filing. The money laundering reporting officer approves the auto closure policy and receives sampling results; model risk validates the scoring model and every material change.",[137,138,139,140,141],"Alert volume and false positive rate per scenario, before and after","Conversion from alert to suspicious activity report","Investigator handling time per alert and per case","Error rate found in independent sampling of auto closed alerts","Share of reports that came from alerts the model ranked low",[143,146,149,152],{"title":144,"detail":145},"Training on yesterday's decisions","A model trained on past dispositions learns past blind spots. Include alerts later reported through other routes and review the lowest scored band regularly.",{"title":147,"detail":148},"Defensive auto closure rates","Teams close too little automatically to show any benefit, or too much to satisfy a target. Set the band from validation results, not from a savings goal.",{"title":150,"detail":151},"Rationales that read well but prove nothing","Fluent narratives without evidence links do not survive an audit. Require citations to source records in every draft.",{"title":153,"detail":154},"Rules and models never reconciled","Running both forever doubles cost. Plan when rules are retired or retuned based on the parallel run.",{"euAiAct":156,"regulations":159,"guidance":171,"controls":193,"incidents":199},{"tier":157,"basis":158},"minimal","AML transaction monitoring is not listed in Annex III; point 5(b) covers creditworthiness and credit scoring and excludes systems used to detect financial fraud. The Article 5(1)(d) ban on predicting criminal offences from profiling alone does not apply to systems that support a human assessment already based on objective and verifiable facts linked to criminal activity, which is how alert triage should be designed. A decision to restrict an account taken solely by automated means would fall under GDPR Article 22 and national AML law, so consequential decisions need human review.",[160,161,162,163,164,165,166,167,168,169,170],"eu-ai-act","gdpr","fatf-recommendations","dora","us-sr-11-7","mas-ai-risk-management","nist-ai-rmf","iso-42001","eu-amlr","us-bsa","mas-notice-626",[172,176,182,188],{"title":46,"issuer":173,"region":174,"url":47,"note":175},"Hong Kong Monetary Authority","asia-pacific","Reports adoption of AI in transaction monitoring by Hong Kong authorized institutions and announces workshops on risk detection, alert prioritisation and generative AI for compiling suspicious transaction reports.",{"title":177,"issuer":178,"region":179,"url":180,"note":181},"Joint Statement Encouraging Innovative Industry Approaches to AML Compliance","FinCEN and the US federal banking agencies","north-america","https://www.fincen.gov/news/news-releases/treasurys-fincen-and-federal-banking-agencies-issue-joint-statement-encouraging","Innovative pilot programs should not in themselves subject banks to supervisory criticism, even if they prove unsuccessful.",{"title":183,"issuer":184,"region":185,"url":186,"note":187},"Principles for Using Artificial Intelligence and Machine Learning in Financial Crime Compliance","Wolfsberg Group","global","https://wolfsberg-group.org/resources/202/93","Industry principles from 2022 for AI in financial crime compliance: legitimate purpose, proportionate use, design and technical expertise, accountability and oversight, and openness and transparency.",{"title":189,"issuer":190,"region":174,"url":191,"note":192},"Notice 626 Prevention of Money Laundering and Countering the Financing of Terrorism, Banks","Monetary Authority of Singapore","https://www.mas.gov.sg/regulation/notices/notice-626","Example of national AML requirements for ongoing monitoring that an AI triage process must still meet.",[194,195,196,197,198],"Auto closure policy approved by the money laundering reporting officer, per scenario","Stored rationale, evidence and model version for every closed alert","Independent sampling with an error threshold that suspends auto closure","Model validation, inventory entry and ongoing performance monitoring","Documented parallel run results available for supervisory review",[],{"howToBuild":201},"On Blits.ai the enrichment and drafting run as an **agentic workflow**, triggered through the\nAPI for each alert. The agent calls **custom functions** that read the alert, the customer's\ndue diligence record and the transactions behind the hit, queries **SQL knowledge bases** for\nprior alerts, and follows the bank's investigation procedures from a **knowledge base** with\nhybrid retrieval. It returns **structured output**: a risk summary, the evidence with record\nreferences and a proposed disposition.\n\nClosures go through **human in the loop approval** until the bank's policy allows otherwise, and\nevery run has a full audit trail. **PII masking** limits the personal data that reaches the\nmodel, **test suites** replay historical alerts with known outcomes before every change, and\n**monitors** run scheduled checks against the agent and alert on failures. The scoring model itself can stay in\nthe bank's own analytics stack; the platform is model agnostic, with EU and UAE data residency.",[203,206,209],{"question":204,"answer":205},"How much can AI reduce AML false positives?","Published results vary widely with the starting point. HSBC says it now has 60% fewer false positive cases and finds two to four times more financial crime after moving to a machine learning approach built with Google Cloud, and Shift4 reports an 86% reduction after adding ThetaRay's AI transaction monitoring. Measure against your own baseline per scenario, because rule sets differ so much between institutions.",{"question":207,"answer":208},"Can an AML alert be closed without a human?","None of the regulators cited on this page prohibits it, but the bank remains accountable for every closure. The Wolfsberg principles ask institutions to validate AI regularly and hold them responsible for decisions that rely on it, whoever built the system. A cautious path starts with drafting and ranking, and closes only the lowest risk band automatically after a parallel run.",{"question":210,"answer":211},"Does this replace transaction monitoring rules?","Not necessarily. UOB's model complements its rules, which remain its first line of defence, while HSBC made a machine learning risk score its primary transaction monitoring system in key markets. The HKMA sees both paths in Hong Kong: some institutions replace rules with a holistic approach, others add AI use cases step by step. Retire rules only once a parallel run shows the model finds at least as much.",[213,214,215,216,217,218],"suspicious-activity-report-drafting","mule-network-detection","dynamic-customer-risk-rating","sanctions-screening-adjudication","fraud-alert-triage","trade-finance-crime-screening","2026-09-27",[221],{"date":219,"note":222},"First published","aml-alert-triage",[225,265,290,325,359,383,405,427],{"title":226,"useCases":227,"organization":228,"vendors":232,"summary":236,"stage":237,"year":238,"channels":239,"languages":240,"metrics":242,"outcomeDisclosed":251,"sources":252,"verification":260,"grade":262,"id":263,"organizationSlug":264},"Nexo: AI agents that write alert narratives and propose dispositions with Unit21",[213,223],{"name":229,"anonymized":230,"region":231,"industry":17},"Nexo",false,"europe",[233],{"name":234,"role":235},"Unit21","platform","Digital asset services company Nexo uses Unit21's transaction monitoring and case management with AI agents that automate alert narratives and dispositions. Analysts work in a supervisory role, verifying the AI generated output, investigating anomalies and applying judgment. The vendor reports that a majority of alert reviews are now automated, with further automation projected.","production",2026,[26],[241],"en",[243],{"kpi":60,"value":244,"unit":245,"qualifier":246,"period":247,"claimant":248,"quote":249,"sourceUrl":250},57,"percent","exact","share of alert reviews automated","vendor","By automating alert narratives and dispositions, Unit21’s AI Agents have enabled Nexo to achieve 57% automation in alert reviews, with projections to reach up to 80% as the models continue to evolve.","https://www.unit21.ai/customers/nexo",true,[253,255],{"url":250,"title":254,"publisher":234},"Nexo Case Study",{"url":256,"title":257,"publisher":258,"date":259},"https://baytobaynews.com/daily-state-news/stories/unit21-awarded-two-2026-datos-impact-awards-for-ai-innovation-cryptodigital-asset-aml-innovation,346520","Unit21 Awarded Two 2026 Datos Impact Awards for AI Innovation & Crypto/Digital Asset AML Innovation","Business Wire (via Bay to Bay News)","2026-09-14",{"level":261,"checkedAt":219},"source-verified","B","nexo-unit21-ai-alert-narratives",null,{"title":266,"useCases":267,"organization":268,"vendors":271,"summary":273,"stage":274,"year":238,"channels":275,"languages":276,"metrics":277,"outcomeDisclosed":251,"sources":284,"verification":288,"grade":262,"id":289,"organizationSlug":264},"Uphold: pilot of an AI agent for alert review and regulatory filing preparation with Unit21",[213,223],{"name":269,"anonymized":230,"country":270,"region":179,"industry":17},"Uphold","US",[272],{"name":234,"role":235},"Crypto platform Uphold unified alerts, cases and regulatory filings with FinCEN and FINTRAC in Unit21, and piloted Unit21's AI agent to help analysts review alerts faster and more consistently. The vendor reports a drop in median alert review time from the pilot and predicts much faster suspicious transaction report preparation, which has not yet been measured.","pilot",[26],[241],[278],{"kpi":279,"value":280,"unit":245,"qualifier":246,"period":281,"claimant":248,"quote":282,"sourceUrl":283},"handling-time-reduction",44,"median alert review time during the pilot","Median alert review time has dropped by 44% thanks to a pilot of Unit21’s AI Agent, which helps analysts process alerts faster and more consistently.","https://www.unit21.ai/customers/uphold",[285,287],{"url":283,"title":286,"publisher":234},"Uphold Case Study",{"url":256,"title":257,"publisher":258,"date":259},{"level":261,"checkedAt":219},"uphold-unit21-ai-agent-pilot",{"title":291,"useCases":292,"organization":293,"vendors":296,"summary":299,"stage":300,"year":301,"channels":302,"languages":303,"metrics":304,"outcomeDisclosed":251,"sources":315,"verification":322,"grade":262,"id":324,"organizationSlug":264},"HSBC: Dynamic Risk Assessment, machine learning AML monitoring built with Google Cloud",[223],{"name":294,"anonymized":230,"country":295,"region":185,"industry":16},"HSBC","GB",[297],{"name":298,"role":235},"Google Cloud (AML AI)","HSBC replaced rule based transaction monitoring with Dynamic Risk Assessment in its key markets, a machine learning system co developed with Google Cloud that scores customers for money laundering risk from their full transaction and customer data, and routes the highest risk to investigators. HSBC piloted it in 2021 and says it checks about 980 million transactions a month for signs of financial crime. HSBC reports finding two to four times more financial crime with much greater accuracy, and cutting the processing time to analyse billions of transactions from several weeks to a few days.","scaled",2024,[26],[],[305,311],{"kpi":56,"value":306,"unit":245,"qualifier":246,"period":307,"claimant":308,"quote":309,"sourceUrl":310},60,"compared with before, per HSBC","organization","Now, we have 60% fewer false positive cases.","https://www.hsbc.com/news-and-views/views/hsbc-views/harnessing-the-power-of-ai-to-fight-financial-crime",{"kpi":57,"value":306,"unit":245,"qualifier":312,"period":313,"claimant":248,"quote":314,"sourceUrl":42},"at-least","compared with rules based transaction monitoring","In fact, HSBC saw alert volumes decrease by more than 60%.",[316,319],{"url":310,"title":317,"publisher":294,"date":318},"Harnessing the power of AI to fight financial crime","2024-06-10",{"url":42,"title":41,"publisher":320,"date":321},"Google Cloud","2023-06-21",{"level":261,"checkedAt":323},"2026-09-26","hsbc-dynamic-risk-assessment",{"title":326,"useCases":327,"organization":328,"vendors":331,"summary":334,"stage":237,"year":335,"channels":336,"languages":337,"metrics":338,"outcomeDisclosed":251,"sources":351,"verification":356,"grade":262,"id":357,"organizationSlug":358},"UOB: machine learning prioritisation of transaction monitoring alerts with Tookitaki",[223],{"name":329,"anonymized":230,"country":330,"region":174,"industry":16},"United Overseas Bank (UOB)","SG",[332],{"name":333,"role":235},"Tookitaki","UOB co developed a machine learning anti money laundering solution with Tookitaki that sorts transaction monitoring alerts into three priority tiers and identifies connected parties, so investigators focus on the cases most likely to be suspicious. UOB said in December 2020 that it was the first Singapore bank to apply AI to transaction monitoring and name screening at the same time, working through more than 5,700 alerts a month, and that the model complements its rules rather than replacing them.",2020,[26],[241],[339,345],{"kpi":340,"value":341,"unit":245,"qualifier":246,"period":342,"claimant":308,"quote":343,"sourceUrl":344},"accuracy",96,"true positive prediction rate of the high priority tier","Since its implementation, UOB’s new AI solution has proven an overall true positive prediction rate of 96 per cent in the ‘high priority’ category","https://www.uobgroup.com/web-resources/uobgroup/pdf/newsroom/2020/UOB-new-AI-money-laundering-solution.pdf",{"kpi":346,"value":347,"unit":348,"qualifier":312,"period":349,"claimant":308,"quote":350,"sourceUrl":344},"interactions-handled",5700,"count","transaction alerts per month","UOB’s AI solution sieves through an average of more than 5,700 transaction alerts each month to flag cases that are more likely to be suspicious with an overall true positive prediction rate of 96 per cent",[352],{"url":344,"title":353,"publisher":354,"date":355},"UOB's new AI anti-money laundering solution helps the Bank cut through large volumes of transactions to pinpoint suspicious activities","UOB","2020-12-03",{"level":261,"checkedAt":323},"uob-tookitaki-aml-alert-prioritisation","united-overseas-bank-uob",{"title":360,"useCases":361,"organization":362,"vendors":364,"summary":370,"stage":371,"year":238,"channels":372,"languages":373,"metrics":374,"outcomeDisclosed":230,"sources":375,"verification":380,"grade":381,"id":382,"organizationSlug":264},"FIS with Anthropic: Financial Crimes AI Agent in development at BMO and Amalgamated Bank",[223,213],{"name":363,"anonymized":230,"region":179,"industry":16},"BMO and Amalgamated Bank",[365,367],{"name":366,"role":235},"FIS",{"name":368,"role":369},"Anthropic","model-provider","FIS announced in May 2026 that it is building a Financial Crimes AI Agent with Anthropic that assembles evidence across a bank's core systems for anti money laundering alert and case investigations and supports suspicious activity report narratives. BMO and Amalgamated Bank are developing with the agent, and FIS plans general availability in the second half of 2026. The release states aims for investigation time and narrative quality but no measured results.","announced",[26],[241],[],[376],{"url":377,"title":378,"publisher":366,"date":379},"https://www.fisglobal.com/about-us/media-room/press-release/2026/fis-brings-agentic-ai-to-banking-with-anthropic-starting-with-financial-crimes","FIS Brings Agentic AI to Banking with Anthropic, Starting with Financial Crimes","2026-05-04",{"level":261,"checkedAt":323},"C","fis-financial-crimes-ai-agent",{"title":384,"useCases":385,"organization":386,"vendors":390,"summary":395,"stage":237,"year":48,"channels":396,"languages":397,"metrics":398,"outcomeDisclosed":230,"sources":399,"verification":403,"grade":381,"id":404,"organizationSlug":264},"Australia Post: AI transaction monitoring and screening for its financial services with Napier AI",[223],{"name":387,"anonymized":230,"country":388,"region":174,"industry":389},"Australia Post","AU","logistics-and-transportation",[391,393],{"name":392,"role":235},"Napier AI",{"name":394,"role":235},"Microsoft Azure","As Australia Post grew its financial services alongside postal services, it deployed Napier AI's platform on Microsoft Azure for transaction monitoring, client screening and behavioural analytics. The vendor reports large gains in false positive reduction and unusual activity detection, and suspicious activity information that helped dismantle a money laundering syndicate, but its case study credits these gains to rule configuration, the no code sandbox and rule calibration rather than to AI or ML alert scoring.",[26],[241],[],[400],{"url":401,"title":402,"publisher":392},"https://www.napier.ai/case-study/australia-posts-digital-transformation-halves-false-positives","Australia Post's digital transformation halves false positives",{"level":261,"checkedAt":219},"australia-post-napier-aml-monitoring",{"title":406,"useCases":407,"organization":408,"vendors":411,"summary":414,"stage":371,"year":48,"channels":415,"languages":417,"metrics":419,"outcomeDisclosed":230,"sources":420,"verification":425,"grade":381,"id":426,"organizationSlug":264},"Ratepay: payment screening and transaction monitoring with Hawk, AI features planned",[216,223],{"name":409,"anonymized":230,"country":410,"region":231,"industry":17},"Ratepay","DE",[412],{"name":413,"role":235},"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.",[26,416],"api",[241,418],"de",[],[421],{"url":422,"title":423,"publisher":413,"date":424},"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":261,"checkedAt":323},"ratepay-hawk-aml-screening",{"title":428,"useCases":429,"organization":430,"vendors":432,"summary":435,"stage":237,"year":301,"channels":436,"languages":437,"metrics":438,"outcomeDisclosed":251,"sources":443,"verification":447,"grade":381,"id":448,"organizationSlug":264},"Shift4: AI transaction monitoring replacing static rules with ThetaRay",[223],{"name":431,"anonymized":230,"country":270,"region":185,"industry":17},"Shift4",[433],{"name":434,"role":235},"ThetaRay","Payments company Shift4 selected ThetaRay's AI transaction monitoring platform in late 2023 to replace static threshold rules, and completed its European deployment in the first quarter of 2024. The vendor reports a much lower false positive rate and more productive alerts that lead to investigations, with explainable risk scores for the compliance team.",[26],[241],[439],{"kpi":56,"value":440,"unit":245,"qualifier":246,"claimant":248,"quote":441,"sourceUrl":442},86,"How Shift4 slashed false positives by 86% and reclaimed analyst bandwidth across $200B+ in annual volume","https://thetaray.com/customer-stories/how-shift4-increased-productive-alerts-by-70-with-cognitive-ai-transaction-monitoring/",[444],{"url":442,"title":445,"publisher":434,"date":446},"How Shift4 Increased Productive Alerts by 70%","2026-08-04",{"level":261,"checkedAt":323},"shift4-thetaray-aml-transaction-monitoring",0,[451,459,464],{"kpi":56,"label":452,"unit":245,"aggregate":251,"higherIsBetter":251,"n":453,"nUpTo":449,"median":454,"min":306,"max":440,"byClaimant":455,"vendorOnly":230,"points":456},"False positive reduction",2,73,{"organization":75,"vendor":75,"regulator":449,"independent":449},[457,458],{"evidenceId":448,"organization":431,"value":440,"qualifier":246,"claimant":248,"grade":381,"pooled":251},{"evidenceId":324,"organization":294,"value":306,"qualifier":246,"claimant":308,"grade":262,"pooled":251},{"kpi":57,"label":460,"unit":245,"aggregate":251,"higherIsBetter":251,"n":75,"nUpTo":449,"median":306,"min":306,"max":306,"byClaimant":461,"vendorOnly":251,"points":462},"Alert volume reduction",{"organization":449,"vendor":75,"regulator":449,"independent":449},[463],{"evidenceId":324,"organization":294,"value":306,"qualifier":312,"claimant":248,"grade":262,"pooled":251},{"kpi":60,"label":465,"unit":245,"aggregate":251,"higherIsBetter":251,"n":75,"nUpTo":449,"median":244,"min":244,"max":244,"byClaimant":466,"vendorOnly":251,"points":467},"Automation rate",{"organization":449,"vendor":75,"regulator":449,"independent":449},[468],{"evidenceId":263,"organization":229,"value":244,"qualifier":246,"claimant":248,"grade":262,"pooled":251},{"low":470,"high":471},400000,2800000,[473,488,502,513,529,540],{"slug":213,"title":474,"shortTitle":475,"definition":476,"status":8,"industries":477,"functions":478,"patterns":480,"audience":28,"autonomy":483,"adoptionStage":484,"segment":31,"evidenceCount":485,"publicEvidenceCount":485,"organizations":486,"bestGrade":262,"headline":264,"lastVerified":323,"indexable":251},"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.",[16,17],[19,479],"case-management",[481,24,482,23],"content-generation","rag-knowledge-assistant","copilot","emerging",4,[487,363,229,269],"Finshark",{"slug":214,"title":489,"shortTitle":490,"definition":491,"status":8,"industries":492,"functions":493,"patterns":495,"audience":496,"autonomy":483,"adoptionStage":30,"segment":31,"evidenceCount":497,"publicEvidenceCount":497,"organizations":498,"bestGrade":262,"headline":264,"lastVerified":219,"indexable":251},"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.",[16,17],[494,19],"fraud-prevention",[22,21,23,24],"back-office",3,[499,500,501],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":215,"title":503,"shortTitle":504,"definition":505,"status":8,"industries":506,"functions":508,"patterns":510,"audience":496,"autonomy":29,"adoptionStage":30,"segment":31,"evidenceCount":75,"publicEvidenceCount":75,"organizations":511,"bestGrade":262,"headline":264,"lastVerified":219,"indexable":251},"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.",[16,17,507],"wealth-and-asset-management",[19,509],"risk-management",[21,22],[512],"bunq",{"slug":216,"title":514,"shortTitle":515,"definition":516,"status":8,"industries":517,"functions":518,"patterns":519,"audience":496,"autonomy":29,"adoptionStage":30,"segment":31,"evidenceCount":521,"publicEvidenceCount":521,"organizations":522,"bestGrade":262,"headline":527,"lastVerified":323,"indexable":251},"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],[19],[520,21,23],"classification-and-routing",7,[523,524,294,525,409,526,329],"AJ Bell","First National Bank of Omaha (FNBO)","Mashreq","Standard Chartered",{"kpi":56,"label":452,"unit":245,"n":75,"nUpTo":449,"kind":528,"value":306,"qualifier":246,"claimant":308,"organization":329,"vendorReported":230},"reported",{"slug":217,"title":530,"shortTitle":531,"definition":532,"status":8,"industries":533,"functions":534,"patterns":536,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"evidenceCount":497,"publicEvidenceCount":453,"organizations":537,"bestGrade":381,"headline":264,"lastVerified":219,"indexable":251},"AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[16,17],[494,535],"operations",[23,520,24,21],[538,539],"Coast","SEB",{"slug":218,"title":541,"shortTitle":542,"definition":543,"status":8,"industries":544,"functions":545,"patterns":546,"audience":496,"autonomy":29,"adoptionStage":484,"segment":548,"evidenceCount":497,"publicEvidenceCount":497,"organizations":549,"bestGrade":381,"headline":264,"lastVerified":219,"indexable":251},"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],[19,535],[547,22,520,24],"document-processing","specialized-businesses",[550,551,552],"ANZ, HSBC and Lloyds Banking Group","Stanbic Bank Uganda","United Bank Limited",{"indexable":251,"reasons":554},[],[556,562,567,573,579,584,591,598,603,610,617,622,629,635,640,645,651,657,663,669,675,681,685,690,695,702,709,714,720,728,734,740,746,751],{"id":160,"label":557,"issuer":558,"region":231,"url":559,"description":560,"useCases":561,"indexable":251},"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":161,"label":563,"issuer":558,"region":231,"url":564,"description":565,"useCases":566,"indexable":251},"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":167,"label":568,"issuer":569,"region":185,"url":570,"description":571,"useCases":572,"indexable":251},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":166,"label":574,"issuer":575,"region":179,"url":576,"description":577,"useCases":578,"indexable":251},"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":163,"label":580,"issuer":558,"region":231,"url":581,"description":582,"useCases":583,"indexable":251},"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":585,"label":586,"issuer":587,"region":231,"url":588,"description":589,"useCases":590,"indexable":251},"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":592,"label":593,"issuer":594,"region":231,"url":595,"description":596,"useCases":597,"indexable":251},"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":165,"label":599,"issuer":190,"region":174,"url":600,"description":601,"useCases":602,"indexable":251},"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":604,"label":605,"issuer":606,"region":174,"url":607,"description":608,"useCases":609,"indexable":251},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":611,"label":612,"issuer":613,"region":185,"url":614,"description":615,"useCases":616,"indexable":251},"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":164,"label":618,"issuer":619,"region":179,"url":620,"description":621,"useCases":616,"indexable":251},"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":623,"label":624,"issuer":625,"region":231,"url":626,"description":627,"useCases":628,"indexable":251},"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":162,"label":630,"issuer":631,"region":185,"url":632,"description":633,"useCases":634,"indexable":251},"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":168,"label":636,"issuer":558,"region":231,"url":637,"description":638,"useCases":639,"indexable":251},"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":641,"label":642,"issuer":558,"region":231,"url":643,"description":644,"useCases":639,"indexable":251},"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":169,"label":646,"issuer":647,"region":179,"url":648,"description":649,"useCases":650,"indexable":251},"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":652,"label":653,"issuer":558,"region":231,"url":654,"description":655,"useCases":656,"indexable":251},"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":658,"label":659,"issuer":660,"region":179,"url":661,"description":662,"useCases":656,"indexable":251},"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":664,"label":665,"issuer":666,"region":185,"url":667,"description":668,"useCases":656,"indexable":251},"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":670,"label":671,"issuer":558,"region":231,"url":672,"description":673,"useCases":674,"indexable":251},"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":676,"label":677,"issuer":678,"region":179,"url":679,"description":680,"useCases":674,"indexable":251},"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":170,"label":682,"issuer":190,"region":174,"url":191,"description":683,"useCases":684,"indexable":251},"MAS Notice 626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":686,"label":687,"issuer":558,"region":231,"url":688,"description":689,"useCases":684,"indexable":251},"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":691,"label":692,"issuer":558,"region":231,"url":693,"description":694,"useCases":684,"indexable":251},"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":696,"label":697,"issuer":698,"region":231,"url":699,"description":700,"useCases":701,"indexable":251},"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":703,"label":704,"issuer":705,"region":179,"url":706,"description":707,"useCases":708,"indexable":251},"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":710,"label":711,"issuer":558,"region":231,"url":712,"description":713,"useCases":708,"indexable":251},"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":715,"label":716,"issuer":558,"region":231,"url":717,"description":718,"useCases":719,"indexable":251},"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":721,"label":722,"issuer":723,"region":724,"url":725,"description":726,"useCases":727,"indexable":251},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":729,"label":730,"issuer":731,"region":231,"url":732,"description":733,"useCases":485,"indexable":251},"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":735,"label":736,"issuer":737,"region":231,"url":738,"description":739,"useCases":485,"indexable":251},"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":741,"label":742,"issuer":743,"region":174,"url":744,"description":745,"useCases":497,"indexable":251},"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":747,"label":748,"issuer":558,"region":231,"url":749,"description":750,"useCases":497,"indexable":251},"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":752,"label":753,"issuer":754,"region":179,"url":755,"description":756,"useCases":497,"indexable":251},"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.",1790598299049]