[{"data":1,"prerenderedAt":570},["ShallowReactive",2],{"uc-fraud-alert-triage":3,"uc-regulations":369},{"useCase":4,"evidence":204,"blitsAiDeployments":260,"benchmarks":261,"indicative":262,"related":265,"indexability":367,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":43,"valueDrivers":44,"kpis":49,"indicativeValue":55,"macroEstimates":91,"feasibility":92,"implementation":105,"risk":145,"blitsAi":178,"faq":180,"related":193,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI agent for fraud alert triage","Fraud alert triage","AI agents for fraud alert triage","How an AI agent works the fraud alert queue: it enriches each alert, closes clear false positives under written rules and briefs analysts with a drafted rationale.","published","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.",[12,13,14,15],"fraud case triage","fraud alert disposition","fraud analyst copilot","fraud queue automation",[17,18],"banking","payments",[20,21],"fraud-prevention","operations",[23,24,25,26],"agentic-workflow","classification-and-routing","summarization","prediction-and-scoring",[28,29],"agent-desktop","internal-tools","employee-facing","supervised-agent","early-adopters","middle-office","Every fraud engine produces a queue. Transactions held for review, customer fraud claims, alerts\nfrom device and behavioural tools and warnings from card schemes all land with analysts. Each\nalert has to be worked, whether it turns out to be fraud or a genuine customer, and each one means\nopening several systems, reading the customer's history and deciding whether to call, release or\nblock.\n\nWhen queues grow faster than teams, genuine customers wait for a held payment and real fraud gets\nworked too late. Much of the work on an alert is gathering context from several systems before an\nanalyst can judge it, and the reasoning behind a closed alert is not always recorded in a way that\ncan be audited later.",[36,41],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"In a Feedzai survey of 562 fraud and financial crime professionals at financial institutions (March and April 2025), 43% reported increased efficiency within fraud teams from AI.","AI Fraud Trends 2025: Banks Fight Back","https://www.feedzai.com/pressrelease/ai-fraud-trends-2025/",2025,{"statement":42,"sourceTitle":38,"sourceUrl":39,"year":40},"The same Feedzai survey found that 90% of financial institutions use AI to expedite fraud investigations and detect new tactics in real time.","1. **Collect the alert.** Alerts from the fraud engine, device intelligence, customer claims and\n   scheme notifications arrive in one queue with a common structure.\n2. **Enrich it.** The agent pulls customer profile, recent transactions, device and location\n   history, previous alerts and any contact the customer has had, through read only tools.\n3. **Group and rank.** Duplicate alerts on the same customer or event are merged, and a model\n   ranks the rest by risk and value at stake.\n4. **Propose a disposition.** For each alert the agent drafts a disposition (release, contact the\n   customer, block, escalate) with the evidence it used. Alerts that meet documented auto clear\n   criteria are closed with that rationale stored.\n5. **Hand over.** Everything else goes to an analyst with the summary, the evidence and the\n   suggested next step; the analyst decides and the decision is logged with the agent's draft.",[45,46,47,48],"employee-productivity","speed","risk-reduction","customer-experience",[50,51,52,53,54],"handling-time-reduction","automation-rate","false-positive-reduction","productivity-gain","time-saved-per-task",{"referenceOrg":56,"inputs":57,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A retail bank whose fraud team works 200,000 alerts a year",[58,64,71,79],{"key":59,"label":60,"low":61,"high":61,"unit":62,"note":63},"alerts","Fraud alerts worked per year",200000,"alerts per year","The reference bank.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"minutesPerAlert","Analyst minutes per alert today",8,15,"minutes per alert","Editorial assumption. Replace with your own time study.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77,"sourceUrl":78},"timeSaved","Share of analyst time saved per alert",0.2,0.4,"fraction of handling time","Editorial assumption. The low end matches the 20% cut in alert handling time Feedzai claims for the investigations skill of its Farol agent; the high end stays well below the 75% cut in daily time per person on manual reviews that Oscilar reports for Coast, which measures staff time rather than time per alert. Replace with your own pilot results.","https://www.prnewswire.com/news-releases/as-banks-pivot-to-agentic-ai-feedzai-unveils-farol-to-transform-fraud-analysis-and-cut-investigation-times-302888211.html",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerHour","Fully loaded analyst cost per hour",35,60,"USD per hour","Editorial assumption. Replace with your own.","alerts * minutesPerAlert / 60 * timeSaved * costPerHour","USD","per year","Analyst capacity released","Counts analyst time only. It leaves out faster release of genuine customers' payments, losses avoided by working real fraud sooner, and the cost of the platform and integrations.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":99},"medium","The agent only reads and drafts, which keeps the risk manageable, but it needs read access to many systems and a clear, approved definition of what may be closed without a human.",[96,97,98],"Historical alerts with their final dispositions and the reason recorded","Written procedures for each alert type, including what evidence an analyst checks","Access to customer, transaction, device and contact history through APIs",[100,101,102,103,104],"Fraud detection engine and alert queue","Case management system","Core banking and card platforms (read only)","Device intelligence and authentication logs","Contact centre and digital banking for customer outreach",{"steps":106,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[107,110,113,116,119],{"title":108,"detail":109},"Profile the queue","Break the last year of alerts down by source, type, final disposition and handling time. This shows where the false positives sit and which alert types are safe to automate first.",{"title":111,"detail":112},"Codify the procedures","Turn each alert type's procedure into explicit checks and evidence requirements that the agent follows, reviewed by the fraud operations lead.",{"title":114,"detail":115},"Start as a copilot","Let the agent enrich and draft only, with analysts deciding every alert. Measure agreement between the draft and the analyst's decision per alert type.",{"title":117,"detail":118},"Introduce auto clear per alert type","Where agreement is consistently high and the risk is low, approve documented auto clear criteria for that alert type, with a sample of closures reviewed independently every week.",{"title":120,"detail":121},"Feed decisions back to detection","Share the reasons alerts turn out false with the detection team, so rules and models are tuned and fewer bad alerts are produced in the first place.",[123,124,125,126,127],"Auto clear only for alert types and thresholds approved in writing, never for high value or vulnerable customer cases","Read only access for the agent; blocks and releases need an analyst or a separate approved action","Every disposition stores the evidence and rationale used, whether drafted by the agent or written by a human","Weekly independent sampling of auto cleared alerts, with automatic rollback if the error rate exceeds a limit","Customer data masked in prompts and logs where the model does not need it","Analysts decide every escalated alert and any action that affects a customer's money. Fraud operations approves which alert types may be auto cleared and reviews a sample of those closures every week; quality assurance compares the agent's drafts with final decisions.",[130,131,132,133,134],"Average handling time per alert, by alert type","Share of alerts auto cleared and the error rate found in sampling","Agreement rate between the agent's draft and the analyst's final decision","Time from alert to decision for confirmed fraud","Fraud that was later confirmed on alerts the agent had proposed to clear",[136,139,142],{"title":137,"detail":138},"Rubber stamping","Analysts accept drafts without reading them because they are usually right. Measure disagreement rates and include known fraud test cases in the queue.",{"title":140,"detail":141},"Auto clear drifting with the fraud mix","Criteria that were safe last quarter clear a new attack pattern. Review auto clear performance monthly and tie it to changes in the detection rules.",{"title":143,"detail":144},"Missing context","The agent drafts confidently from partial data when a system is unavailable. Make missing sources explicit in the draft and block auto clear when enrichment is incomplete.",{"euAiAct":146,"regulations":149,"guidance":159,"controls":171,"incidents":177},{"tier":147,"basis":148},"minimal","Internal triage of fraud alerts is not listed in Annex III, and point 5(b) explicitly excludes fraud detection from the high risk creditworthiness category. Article 50(1) covers any system that interacts directly with people, analysts included, but it does not apply where the use of AI is obvious to a reasonably well informed user, as it is in an internal analyst tool; the marking duties for generated content in Article 50(2) sit with the provider. Reassess if its output feeds credit decisions. Decisions that affect customers remain subject to GDPR and consumer protection rules.",[150,151,152,153,154,155,156,157,158],"eu-ai-act","gdpr","dora","uk-consumer-duty","uk-psr-app-reimbursement","us-sr-11-7","nist-ai-rmf","iso-42001","eu-psd2",[160,166],{"title":161,"issuer":162,"region":163,"url":164,"note":165},"Annex III: High-Risk AI Systems Referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) carves fraud detection out of the high risk creditworthiness category.",{"title":167,"issuer":168,"region":163,"url":169,"note":170},"APP scams","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement for authorised push payment scams raises the cost of slow or wrong alert decisions.",[172,173,174,175,176],"Written auto clear criteria per alert type, approved by fraud operations and risk","Stored rationale and evidence for every closed alert","Independent weekly sampling of auto cleared alerts with an error rate limit","Access control so the agent cannot move money or change blocks on its own","Inventory entry for the agent and its prompts, with change control",[],{"howToBuild":179},"On Blits.ai the triage runs as an **agentic workflow**, triggered through the API for each new\nalert or batch. The agent calls **custom functions** that read the case, the customer's recent\ntransactions and device history, and it can query **SQL knowledge bases** for past alerts. It\nfollows the fraud team's procedures, loaded into a **knowledge base** with hybrid retrieval, and\nreturns **structured output**: a proposed disposition, the evidence and the rationale.\n\nA tool execution policy limits the agent to read only tools, and any action that changes\nsomething requires **human in the loop approval**, where the analyst approves or rejects it.\nEvery run keeps a full audit trail. **PII masking** keeps account and card data out of prompts\nwhere it is not needed, **test suites** replay historical alerts with known outcomes before a\nchange goes live, and **monitors** run scheduled checks against the agent and alert on failure.\nThe platform is model agnostic and available with EU and UAE data residency.",[181,184,187,190],{"question":182,"answer":183},"Can an AI agent close fraud alerts on its own?","Only for alert types where it has proven it agrees with analysts and the risk is low, under written criteria, with a sample of closures reviewed every week. High value alerts, vulnerable customers and anything that blocks or releases money should stay with a human.",{"question":185,"answer":186},"How is this different from the fraud scoring model?","The scoring model decides in real time whether to hold a payment. Triage starts after that: it works the alerts and held payments the model created, gathers context and prepares or makes the disposition, on a timescale of minutes.",{"question":188,"answer":189},"What results have organizations reported?","Oscilar reports that Coast, a fleet card provider, cut the time its staff spend on manual reviews from 2 hours per person per day to under 30 minutes after adopting its case management platform with rule based routing, auto assignment and a feedback loop. Upstream of triage, Visa reports that active users of Decision Manager scoring shrank the manual review queue by 25% or more. Both are claims by the platform provider, not independent measurements.",{"question":191,"answer":192},"What should we be able to show an auditor or supervisor?","Why each alert was closed. Store the evidence and rationale for every disposition, keep the auto clear criteria under change control, and be able to show sampling results.",[194,195,196,197,198],"real-time-fraud-scoring","fraud-alert-confirmation","aml-alert-triage","mule-network-detection","scam-payment-interception","2026-09-27",[201],{"date":199,"note":202},"First published","fraud-alert-triage",[205,233],{"title":206,"useCases":207,"organization":208,"vendors":211,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":221,"outcomeDisclosed":210,"sources":222,"verification":227,"grade":230,"id":231,"organizationSlug":232},"SEB: quoted at the launch of Feedzai's Farol fraud agent",[203],{"name":209,"anonymized":210,"region":163,"industry":17},"SEB",false,[212],{"name":213,"role":214},"Feedzai","platform","Feedzai launched Farol in September 2026, an AI agent embedded in its fraud platform that retrieves and summarises alert data for investigators and supports fraud strategy work such as rule suggestions. SEB, described in the release as a northern European financial services group, is quoted at launch through its fraud prevention business owner on using a single interface for data retrieval, insight generation and rule suggestions. The release does not say how or how widely SEB uses Farol, and no SEB specific results are disclosed.","announced",2026,[29],[220],"en",[],[223],{"url":78,"title":224,"publisher":225,"date":226},"As Banks Pivot to Agentic AI, Feedzai Unveils Farol to Transform Fraud Analysis and Cut Investigation Times","Feedzai via PR Newswire","2026-09-24",{"level":228,"checkedAt":229},"source-verified","2026-09-26","C","seb-feedzai-farol-fraud-agent",null,{"title":234,"useCases":235,"organization":236,"vendors":240,"summary":243,"stage":244,"year":245,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":249,"sources":250,"verification":258,"grade":230,"id":259,"organizationSlug":232},"Coast: AI assisted fraud case management with Oscilar",[203],{"name":237,"anonymized":210,"country":238,"region":239,"industry":18},"Coast","US","north-america",[241],{"name":242,"role":214},"Oscilar","Coast, a US fleet and fuel card provider, uses Oscilar's risk platform for fraud decisioning and case management, including rule based routing and assignment of cases, a feedback loop, and generative AI features for case assignment and fraud analysis. The vendor reports that the time Coast's staff spend on manual reviews fell from 2 hours per person per day to under 30 minutes. This is time per person per day, not time per case: queues and auto assignment also let entry level case managers work independently from analysts, so part of the drop may be work moved between roles.","production",2024,[29],[220],[],true,[251,255],{"url":252,"title":253,"publisher":242,"archivedUrl":254},"https://oscilar.com/customers/coast","How Coast Cut Manual Review Time by 75%","https://web.archive.org/web/20241015125523/https://oscilar.com/customers/coast",{"url":256,"title":257,"publisher":237},"https://www.coastpay.com","Coast fleet and fuel cards",{"level":228,"checkedAt":229},"coast-oscilar-fraud-case-review",1,[],{"low":263,"high":264},186666.6666666667,1200000,[266,298,320,341,352],{"slug":194,"title":267,"shortTitle":268,"definition":269,"status":9,"industries":270,"functions":271,"patterns":272,"audience":274,"autonomy":275,"adoptionStage":276,"segment":33,"evidenceCount":277,"publicEvidenceCount":277,"organizations":278,"bestGrade":287,"headline":288,"lastVerified":199,"indexable":249},"Real time fraud scoring for card and instant payments","Real time fraud scoring","Machine learning that decides in milliseconds, without any conversation, how likely each card authorization and account to account payment is to be fraudulent, combining behavioural, device and network signals, so the bank can approve, challenge or block a payment before the money leaves. Working the resulting alerts and talking to the customer about them are separate use cases.",[17,18],[20],[26,273],"anomaly-detection","back-office","autonomous","mainstream",9,[279,280,281,282,283,284,285,286],"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Commonwealth Bank of Australia","Mastercard","NatWest Group","Pay.UK","Revolut","Stripe","Visa","B",{"kpi":289,"label":290,"unit":291,"n":292,"nUpTo":293,"kind":294,"value":295,"qualifier":296,"claimant":297,"organization":232,"vendorReported":210},"fraud-loss-reduction","Fraud loss reduction","percent",3,0,"median",30,"exact","organization",{"slug":195,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":303,"patterns":305,"audience":308,"autonomy":31,"adoptionStage":309,"segment":310,"evidenceCount":311,"publicEvidenceCount":311,"organizations":312,"bestGrade":287,"headline":316,"lastVerified":199,"indexable":249},"AI agent for fraud alert confirmation with cardholders","Fraud alert confirmation","A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.",[17,18],[20,304],"customer-service",[306,307,23],"conversational-agent","voice-agent","customer-facing","emerging","front-office",5,[313,280,314,284,315],"Capital One","Macquarie Bank","Westpac",{"kpi":289,"label":290,"unit":291,"n":317,"nUpTo":293,"kind":318,"value":319,"qualifier":296,"claimant":297,"organization":280,"vendorReported":210},2,"reported",76,{"slug":196,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":325,"patterns":327,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"evidenceCount":67,"publicEvidenceCount":67,"organizations":328,"bestGrade":287,"headline":337,"lastVerified":199,"indexable":249},"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.",[17,18],[326],"financial-crime-compliance",[26,273,23,25],[329,330,331,332,333,334,335,336],"Australia Post","BMO and Amalgamated Bank","HSBC","Nexo","Ratepay","Shift4","United Overseas Bank (UOB)","Uphold",{"kpi":52,"label":338,"unit":291,"n":317,"nUpTo":293,"kind":318,"value":339,"qualifier":296,"claimant":340,"organization":334,"vendorReported":249},"False positive reduction",86,"vendor",{"slug":197,"title":342,"shortTitle":343,"definition":344,"status":9,"industries":345,"functions":346,"patterns":347,"audience":274,"autonomy":348,"adoptionStage":32,"segment":33,"evidenceCount":292,"publicEvidenceCount":292,"organizations":349,"bestGrade":287,"headline":232,"lastVerified":199,"indexable":249},"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.",[17,18],[20,326],[273,26,23,25],"copilot",[350,279,351],"BigPay","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":198,"title":353,"shortTitle":354,"definition":355,"status":9,"industries":356,"functions":357,"patterns":358,"audience":308,"autonomy":31,"adoptionStage":32,"segment":310,"evidenceCount":359,"publicEvidenceCount":359,"organizations":360,"bestGrade":287,"headline":363,"lastVerified":229,"indexable":249},"AI scam intervention for instant payments","Scam payment interception","AI that talks to the customer when they are about to authorise an instant payment that looks like a scam: it combines the payee check and the risk score, asks targeted questions about the payment in plain language, explains the specific scam pattern, and holds, delays or escalates the payment to a human specialist when the risk stays high. Unlike fraud scoring, which stops payments the customer did not make, it protects customers from payments they are being manipulated into making.",[17,18],[20,304],[306,26,23,307],6,[280,281,284,361,362,315],"Starling Bank","Vodafone",{"kpi":364,"label":365,"unit":291,"n":317,"nUpTo":293,"kind":318,"value":366,"qualifier":296,"claimant":340,"organization":361,"vendorReported":249},"detection-rate-improvement","Detection improvement",300,{"indexable":249,"reasons":368},[],[370,375,380,387,393,398,405,411,419,426,433,438,445,451,457,462,469,475,481,487,493,499,505,510,514,520,526,531,536,543,550,553,559,564],{"id":150,"label":371,"issuer":162,"region":163,"url":372,"description":373,"useCases":374,"indexable":249},"EU AI Act","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":151,"label":376,"issuer":162,"region":163,"url":377,"description":378,"useCases":379,"indexable":249},"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":157,"label":381,"issuer":382,"region":383,"url":384,"description":385,"useCases":386,"indexable":249},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":156,"label":388,"issuer":389,"region":239,"url":390,"description":391,"useCases":392,"indexable":249},"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":152,"label":394,"issuer":162,"region":163,"url":395,"description":396,"useCases":397,"indexable":249},"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":399,"label":400,"issuer":401,"region":163,"url":402,"description":403,"useCases":404,"indexable":249},"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":153,"label":406,"issuer":407,"region":163,"url":408,"description":409,"useCases":410,"indexable":249},"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":412,"label":413,"issuer":414,"region":415,"url":416,"description":417,"useCases":418,"indexable":249},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":420,"label":421,"issuer":422,"region":415,"url":423,"description":424,"useCases":425,"indexable":249},"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":427,"label":428,"issuer":429,"region":383,"url":430,"description":431,"useCases":432,"indexable":249},"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":155,"label":434,"issuer":435,"region":239,"url":436,"description":437,"useCases":432,"indexable":249},"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":439,"label":440,"issuer":441,"region":163,"url":442,"description":443,"useCases":444,"indexable":249},"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":446,"label":447,"issuer":448,"region":383,"url":449,"description":450,"useCases":68,"indexable":249},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":452,"label":453,"issuer":162,"region":163,"url":454,"description":455,"useCases":456,"indexable":249},"eu-amlr","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":458,"label":459,"issuer":162,"region":163,"url":460,"description":461,"useCases":456,"indexable":249},"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":463,"label":464,"issuer":465,"region":239,"url":466,"description":467,"useCases":468,"indexable":249},"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":470,"label":471,"issuer":162,"region":163,"url":472,"description":473,"useCases":474,"indexable":249},"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":476,"label":477,"issuer":478,"region":239,"url":479,"description":480,"useCases":474,"indexable":249},"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":482,"label":483,"issuer":484,"region":383,"url":485,"description":486,"useCases":474,"indexable":249},"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":488,"label":489,"issuer":162,"region":163,"url":490,"description":491,"useCases":492,"indexable":249},"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":494,"label":495,"issuer":496,"region":239,"url":497,"description":498,"useCases":492,"indexable":249},"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":500,"label":501,"issuer":414,"region":415,"url":502,"description":503,"useCases":504,"indexable":249},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":506,"label":507,"issuer":162,"region":163,"url":508,"description":509,"useCases":504,"indexable":249},"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":158,"label":511,"issuer":162,"region":163,"url":512,"description":513,"useCases":504,"indexable":249},"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":515,"label":516,"issuer":517,"region":163,"url":518,"description":519,"useCases":277,"indexable":249},"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.",{"id":521,"label":522,"issuer":523,"region":239,"url":524,"description":525,"useCases":67,"indexable":249},"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":527,"label":528,"issuer":162,"region":163,"url":529,"description":530,"useCases":67,"indexable":249},"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":532,"label":533,"issuer":162,"region":163,"url":534,"description":535,"useCases":359,"indexable":249},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":537,"label":538,"issuer":539,"region":540,"url":541,"description":542,"useCases":311,"indexable":249},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":544,"label":545,"issuer":546,"region":163,"url":547,"description":548,"useCases":549,"indexable":249},"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":154,"label":551,"issuer":168,"region":163,"url":169,"description":552,"useCases":549,"indexable":249},"UK APP scam reimbursement rules","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":554,"label":555,"issuer":556,"region":415,"url":557,"description":558,"useCases":292,"indexable":249},"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":560,"label":561,"issuer":162,"region":163,"url":562,"description":563,"useCases":292,"indexable":249},"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":565,"label":566,"issuer":567,"region":239,"url":568,"description":569,"useCases":292,"indexable":249},"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.",1790598295073]