[{"data":1,"prerenderedAt":640},["ShallowReactive",2],{"uc-mule-network-detection":3,"uc-regulations":440},{"useCase":4,"evidence":204,"blitsAiDeployments":309,"benchmarks":310,"indicative":317,"related":320,"indexability":438,"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":41,"valueDrivers":42,"kpis":46,"indicativeValue":52,"macroEstimates":80,"feasibility":81,"implementation":95,"risk":135,"blitsAi":180,"faq":182,"related":192,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI for money mule account and network detection","Mule network detection","AI money mule detection for banks","AI finds money mule accounts through the links between them. RBI's MuleHunter.AI reached 21 Indian banks in 2025; five Australian banks share receiving account data.","published","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.",[12,13,14,15],"money mule detection","mule account detection","scam proceeds tracing","network analytics for financial crime",[17,18],"banking","payments",[20,21],"fraud-prevention","financial-crime-compliance",[23,24,25,26],"anomaly-detection","prediction-and-scoring","agentic-workflow","summarization",[28,29],"internal-tools","api","back-office","copilot","early-adopters","middle-office","Scams and most fraud need a place to land the money. Mule accounts, opened by fraudsters or run by\nrecruited account holders, receive the proceeds and move them on quickly, for example to other\nbanks, crypto exchanges, cash machines or remittance services. By the time the victim reports, the\nmoney has often left.\n\nA single bank looking at one account at a time sees little: a new account with some incoming\ntransfers. The pattern only shows in the network, such as many senders who are scam victims, a\nfan in and fan out shape, shared devices and addresses, or accounts that were opened in a burst.\nRegulators are also shifting scam losses onto firms. Under the UK reimbursement rules for\nauthorised push payment scams, the sending and receiving firms split the cost of reimbursing\nvictims equally, so for a receiving bank detecting mules is now a loss and compliance issue, not\nonly a crime prevention one.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"The US Federal Trade Commission reports that in 2024 consumers reported losing more money to scams paid by bank transfer or cryptocurrency than through all other payment methods combined.","New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024","https://www.ftc.gov/news-events/news/press-releases/2025/03/new-ftc-data-show-big-jump-reported-losses-fraud-125-billion-2024",2025,"1. **Build the graph.** Accounts, customers, devices, IP addresses, addresses, phone numbers and\n   payment flows become nodes and edges, updated continuously from onboarding and payment data.\n2. **Score accounts and communities.** Behavioural models score each account for mule like\n   activity (rapid in and out, pass through balances, sudden change after dormancy), and graph\n   algorithms find clusters and chains that share signals.\n3. **Bring in external signals.** Scam reports from other banks, confirmation of payee\n   mismatches, industry or central bank mule lists and law enforcement requests enrich the scores.\n4. **Assemble the case.** An agent drafts a fund flow timeline and case narrative for each\n   cluster: who received what from whom, where it went next, and which signals link the accounts.\n5. **Decide and act.** An investigator decides on restrictions, exits, recall requests to peer\n   banks and reporting, and the decision and reason are recorded against every account touched.",[43,44,45],"risk-reduction","compliance","speed",[47,48,49,50,51],"detection-rate-improvement","fraud-loss-reduction","false-positive-reduction","processing-time-reduction","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"A retail bank receiving scam proceeds in 2,000 reported cases a year",[55,61,68],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"cases","Scam cases per year where the bank received the funds",2000,"cases per year","The reference bank. Replace with your own count of inbound scam reports.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"averageLoss","Average amount received per case",1500,4000,"USD per case","Editorial assumption. Replace with your own data.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"extraRecovery","Additional share of funds frozen or recovered through earlier detection",0.05,0.15,"fraction of funds","Editorial assumption. Public deployments rarely disclose recovery rates, so keep this low until you have your own results.","cases * averageLoss * extraRecovery","USD","per year","Scam proceeds frozen or recovered","Covers recovered funds only. It leaves out reimbursement liabilities avoided under schemes that share losses with the receiving bank, the investigation time saved, regulatory benefits, and the cost of false restrictions on genuine customers.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"high","Graph analytics needs clean entity resolution across customers, devices and counterparties, and the best signals come from outside the bank. Restricting accounts is high impact, so the decision process and customer remediation need as much work as the model.",[85,86,87,88],"Payment flows with counterparty identifiers, including inbound instant payments","Onboarding data, device and session data linked to accounts","Confirmed mule and scam case outcomes for training and evaluation","Access to industry or central bank mule intelligence where it exists",[90,91,92,93,94],"Payments hub and core banking system","Onboarding and identity verification systems","Fraud and AML case management","Industry data sharing schemes and peer bank recall processes","Account restriction and exit workflows",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[97,100,103,106,109],{"title":98,"detail":99},"Start from confirmed cases","Collect every confirmed mule account and inbound scam case from the last two years and map the signals they shared. This becomes the training set and the benchmark.",{"title":101,"detail":102},"Resolve entities before modelling","Link customers, accounts, devices and contact details reliably; poor entity resolution produces false networks that lead to wrong restrictions.",{"title":104,"detail":105},"Combine rules, behaviour and graph","Start with known typologies as rules, add behavioural scoring, then graph features and community detection, and measure the lift each layer adds on the benchmark set.",{"title":107,"detail":108},"Give investigators the network view","Provide a visual network and a drafted fund flow narrative per cluster, so investigators can act on a whole network at once instead of account by account.",{"title":110,"detail":111},"Connect to the outside","Join industry intelligence sharing and agree recall and freeze procedures with peer banks, so detection turns into recovered funds.",[113,114,115,116,117],"Account restrictions and exits only by a trained investigator, with the reason recorded","Fast review and remediation route for customers restricted in error","Graph links shown with the evidence behind them, never as an unexplained score","Regular testing for disparate impact across customer groups, for example by age, nationality and student status","Data sharing with peer banks only under the legal gateway that allows it","The models and agent find and assemble; investigators decide. Every restriction, exit, recall request and report is a documented human decision, and a second line reviews samples of both actioned and dismissed clusters.",[120,121,122,123,124],"Mule accounts identified per month and the share confirmed on investigation","Time from first inbound scam payment to restriction","Value of funds frozen or recovered","Share of restricted customers released after review","Inbound scam reports from peer banks per million accounts",[126,129,132],{"title":127,"detail":128},"Networks built on bad links","Shared addresses in student housing or shared devices in families create false clusters. Weight links by strength and require investigator review of the evidence.",{"title":130,"detail":131},"Detection without recovery","Mules are found after the money has moved on. Measure time to restriction, not only detection counts, and connect to recall processes.",{"title":133,"detail":134},"Targeting the recruited, missing the organisers","Restricting individual mules without mapping the network leaves the organisers active. Work at cluster level and share intelligence.",{"euAiAct":136,"regulations":139,"guidance":151,"controls":173,"incidents":179},{"tier":137,"basis":138},"minimal","Detecting mule accounts is fraud and AML detection by a private firm, which Annex III does not list; point 5(b) explicitly excludes systems used to detect financial fraud from the credit scoring category. Restricting an account based solely on an automated score can be a decision with similarly significant effects under GDPR Article 22, so keep a human decision and a route to challenge.",[140,141,142,143,144,145,146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","fatf-recommendations","uk-consumer-duty","dora","mas-ai-risk-management","us-sr-11-7","eu-amlr","us-bsa","uk-psr-app-reimbursement",[152,158,164,168],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"APP scams","Payment Systems Regulator","europe","https://www.psr.org.uk/our-work/app-scams/","UK reimbursement for authorised push payment scams over Faster Payments and CHAPS is split 50:50 between sending and receiving firms, which puts mule detection on the receiving bank's balance sheet.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Guidelines on Shared Responsibility Framework","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/guidelines/guidelines-on-shared-responsibility-framework","Singapore's framework, in force since 16 December 2024, that assigns anti phishing duties to financial institutions and telcos and requires payouts to scam victims where those duties are breached.",{"title":165,"issuer":160,"region":161,"url":166,"note":167},"COSMIC, Collaborative Sharing of ML/TF Information and Cases","https://www.mas.gov.sg/regulation/anti-money-laundering/cosmic","Platform launched by MAS with six major banks in April 2024 for sharing red flag information on customers across institutions. It currently covers misuse of legal persons, trade finance and proliferation financing, not retail mule accounts.",{"title":169,"issuer":170,"region":161,"url":171,"note":172},"Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee report","Reserve Bank of India","https://www.rbi.org.in/Scripts/PublicationReportDetails.aspx?UrlPage=&ID=1306","India's framework for responsible AI in the financial sector (August 2025), which names fraud detection as a high stakes use and recommends that AI models are validated and tested periodically, including for drift and bias.",[174,175,176,177,178],"Documented decision process for restrictions and exits, with recorded reasons","Customer remediation route with a service level for review","Model inventory entry, validation and fairness testing","Legal basis documented for every external data sharing arrangement","Audit trail linking each restriction to the network evidence and the investigator",[],{"howToBuild":181},"The graph and scoring models run in the bank's analytics platform. Blits.ai adds the\ninvestigation layer: an **agentic workflow**, triggered through the API for each flagged\ncluster, calls **custom functions** and **SQL knowledge bases** to pull the flows, accounts and\nprior cases, and drafts a fund flow timeline and case narrative as **structured output**, with\neach statement tied to the underlying records.\n\nRestrictions, recall requests and exits go through **human in the loop approval**, with a full\naudit trail per run. Where the bank wants to check a flagged customer, a **conversational\nagent** in the bank's app (through the **API channel**), on **WhatsApp** or on **voice** can ask about the purpose of recent payments and\nhand over to a specialist. **PII masking**, **guardrails** and **test suites** apply throughout,\nand the platform is model agnostic with EU and UAE data residency.",[183,186,189],{"question":184,"answer":185},"Why does mule detection need graph analytics?","A mule account often looks ordinary on its own. The signal is in the links: many victims paying in, money leaving quickly to the same onward accounts, and devices or contact details shared across accounts opened around the same time.",{"question":187,"answer":188},"Can banks detect mules together?","Increasingly yes. In Australia, five large banks joined BioCatch Trust Australia in November 2024 to share intelligence on receiving accounts before a payment leaves. In India, the central bank's innovation hub offers MuleHunter.AI to banks; the Governor said in October 2025 that it had scaled to 21 banks.",{"question":190,"answer":191},"Should an AI model freeze accounts automatically?","No. Freezing or exiting an account is high impact for the customer and often irreversible in practice. Let the model find and prioritise, and let a trained investigator decide with the evidence in front of them.",[193,194,195,196,197,198],"real-time-fraud-scoring","scam-payment-interception","aml-alert-triage","fraud-alert-triage","suspicious-activity-report-drafting","application-and-identity-fraud-detection","2026-09-27",[201],{"date":199,"note":202},"First published","mule-network-detection",[205,251,289],{"title":206,"useCases":207,"organization":208,"vendors":213,"summary":217,"stage":218,"year":40,"channels":219,"languages":220,"metrics":221,"outcomeDisclosed":231,"sources":232,"verification":245,"grade":248,"id":249,"organizationSlug":250},"Reserve Bank of India: MuleHunter.AI mule account detection model for banks",[203],{"name":209,"anonymized":210,"country":211,"region":161,"industry":212},"Reserve Bank Innovation Hub (Reserve Bank of India)",false,"IN","government",[214],{"name":215,"role":216},"Reserve Bank Innovation Hub","in-house","The Reserve Bank Innovation Hub, a subsidiary of the Reserve Bank of India, built MuleHunter.AI, a machine learning model that helps banks detect mule accounts used to move fraud proceeds. The RBI announced the pilot with two large public sector banks in December 2024, and the Governor said in October 2025 that it had been scaled from about 5 banks to 21 banks, using system wide learning. The RBI has declined to disclose how many mule accounts it has identified.","scaled",[29],[],[222],{"kpi":223,"value":224,"unit":225,"qualifier":226,"period":227,"claimant":228,"quote":229,"sourceUrl":230},"users-served",21,"count","exact","banks using the model, October 2025","organization","MuleHunter.ai, developed by the Reserve Bank Innovation Hub has been scaled up from about 5 banks at the beginning of this year to 21 banks.","https://www.rbi.org.in/Scripts/BS_SpeechesView.aspx?Id=1525",true,[233,236,240],{"url":230,"title":234,"publisher":170,"date":235},"Driving Inclusive and Sustainable Growth Through Digital Public Infrastructure and FinTech","2025-10-10",{"url":237,"title":238,"publisher":170,"date":239},"https://www.rbi.org.in/scripts/BS_PressReleaseDisplay.aspx?prid=59245","Statement on Developmental and Regulatory Policies","2024-12-06",{"url":241,"title":242,"publisher":243,"date":244},"https://www.medianama.com/2025/12/223-rti-23-banks-mulehunter-mule-accounts/","23 Banks Use Mulehunter.AI, RBI Won't Disclose Mule Data","MediaNama","2025-12-30",{"level":246,"checkedAt":247},"source-verified","2026-09-26","B","reserve-bank-innovation-hub-mulehunter-ai",null,{"title":252,"useCases":253,"organization":254,"vendors":257,"summary":261,"stage":262,"year":40,"channels":263,"languages":264,"metrics":266,"outcomeDisclosed":231,"sources":274,"verification":286,"grade":287,"id":288,"organizationSlug":250},"BioCatch Trust Australia: behavioural intelligence sharing on receiving accounts across five banks",[203,193],{"name":255,"anonymized":210,"country":256,"region":161,"industry":17},"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","AU",[258],{"name":259,"role":260},"BioCatch","platform","In November 2024 ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac joined BioCatch Trust Australia, launched as a pilot of an interbank network that shares behavioural and device intelligence about receiving accounts, so the sending bank can review a payment to a likely mule account before money leaves. BioCatch reports that in the third quarter of 2025 the network analysed more than 180 million payments and revealed more than $60 million in attempted fraud (currency not stated in the release), and that two more institutions, including Macquarie Bank, have since joined.","production",[29],[265],"en",[267],{"kpi":51,"value":268,"unit":225,"qualifier":269,"period":270,"claimant":271,"quote":272,"sourceUrl":273},180000000,"at-least","payments analysed in the third quarter of 2025","vendor","in the third quarter of 2025 alone, analyzed more than 180 million payments totaling more than $330 billion, revealing more than $60 million in attempted fraud.","https://www.biocatch.com/press-release/aussie-intelligence-sharing-exposes-more-than-60-million-in-fraud-attempts-in-three-months",[275,278,282],{"url":273,"title":276,"publisher":259,"date":277},"Aussie intelligence-sharing exposes more than $60 million in fraud attempts in three months","2025-11-26",{"url":279,"title":280,"publisher":259,"date":281},"https://www.biocatch.com/press-release/biocatch-partners-australian-banks-fraud-scams-intelligence-sharing-network","BioCatch partners with Australian banks on launch of fraud and scams intelligence-sharing network","2024-11-20",{"url":283,"title":284,"publisher":285,"date":281},"https://www.nab.com.au/news/technology-ai/nab-biocatch-trust","NAB joins BioCatch Trust Australia to protect customers from scams and fraud","NAB",{"level":246,"checkedAt":247},"C","biocatch-trust-australia-mule-intelligence",{"title":290,"useCases":291,"organization":292,"vendors":295,"summary":298,"stage":262,"year":40,"channels":299,"languages":300,"metrics":301,"outcomeDisclosed":231,"sources":302,"verification":307,"grade":287,"id":308,"organizationSlug":250},"BigPay: stopping money mule networks with Feedzai in Malaysia",[203],{"name":293,"anonymized":210,"country":294,"region":161,"industry":18},"BigPay","MY",[296],{"name":297,"role":260},"Feedzai","BigPay, a Malaysian electronic money platform that was receiving over 1,000 reported mule cases a month, worked with Feedzai to turn mule patterns into detection rules on its existing Feedzai platform: alert logic for suspicious inbound payments, decline rules based on funding velocity and beneficiary risk, and rules that block cash out channels such as crypto, ATMs and remittances. The source attributes BigPay's result to this analyst built rule set and presents Feedzai's AI assisted alert prioritisation as the platform's next layer of defense, not as the cause of the outcome. The vendor reports that BigPay neutralised a new mule network surge within 72 hours and reduced overall mule activity by more than 90 percent within 60 days.",[29,28],[265],[],[303],{"url":304,"title":305,"publisher":297,"date":306},"https://www.feedzai.com/customer-stories/bigpay-money-mules/","BigPay Stops Money Mules with Feedzai","2025-02-25",{"level":246,"checkedAt":247},"bigpay-feedzai-mule-detection",0,[311],{"kpi":51,"label":312,"unit":225,"aggregate":210,"higherIsBetter":231,"n":313,"nUpTo":309,"median":268,"min":268,"max":268,"byClaimant":314,"vendorOnly":231,"points":315},"Interactions handled",1,{"organization":309,"vendor":313,"regulator":309,"independent":309},[316],{"evidenceId":288,"organization":255,"value":268,"qualifier":269,"claimant":271,"grade":287,"pooled":231},{"low":318,"high":319},150000,1200000,[321,345,368,389,401,415],{"slug":193,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":326,"patterns":327,"audience":30,"autonomy":328,"adoptionStage":329,"segment":33,"evidenceCount":330,"publicEvidenceCount":330,"organizations":331,"bestGrade":248,"headline":339,"lastVerified":199,"indexable":231},"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],[24,23],"autonomous","mainstream",9,[255,332,333,334,335,336,337,338],"Commonwealth Bank of Australia","Mastercard","NatWest Group","Pay.UK","Revolut","Stripe","Visa",{"kpi":48,"label":340,"unit":341,"n":342,"nUpTo":309,"kind":343,"value":344,"qualifier":226,"claimant":228,"organization":250,"vendorReported":210},"Fraud loss reduction","percent",3,"median",30,{"slug":194,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":350,"patterns":352,"audience":355,"autonomy":356,"adoptionStage":32,"segment":357,"evidenceCount":358,"publicEvidenceCount":358,"organizations":359,"bestGrade":248,"headline":363,"lastVerified":247,"indexable":231},"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],[20,351],"customer-service",[353,24,25,354],"conversational-agent","voice-agent","customer-facing","supervised-agent","front-office",6,[332,333,336,360,361,362],"Starling Bank","Vodafone","Westpac",{"kpi":47,"label":364,"unit":341,"n":365,"nUpTo":309,"kind":366,"value":367,"qualifier":226,"claimant":271,"organization":360,"vendorReported":231},"Detection improvement",2,"reported",300,{"slug":195,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":373,"patterns":374,"audience":375,"autonomy":356,"adoptionStage":32,"segment":33,"evidenceCount":376,"publicEvidenceCount":376,"organizations":377,"bestGrade":248,"headline":386,"lastVerified":199,"indexable":231},"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],[21],[24,23,25,26],"employee-facing",8,[378,379,380,381,382,383,384,385],"Australia Post","BMO and Amalgamated Bank","HSBC","Nexo","Ratepay","Shift4","United Overseas Bank (UOB)","Uphold",{"kpi":49,"label":387,"unit":341,"n":365,"nUpTo":309,"kind":366,"value":388,"qualifier":226,"claimant":271,"organization":383,"vendorReported":231},"False positive reduction",86,{"slug":196,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":394,"patterns":396,"audience":375,"autonomy":356,"adoptionStage":32,"segment":33,"evidenceCount":342,"publicEvidenceCount":365,"organizations":398,"bestGrade":287,"headline":250,"lastVerified":199,"indexable":231},"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.",[17,18],[20,395],"operations",[25,397,26,24],"classification-and-routing",[399,400],"Coast","SEB",{"slug":197,"title":402,"shortTitle":403,"definition":404,"status":9,"industries":405,"functions":406,"patterns":408,"audience":375,"autonomy":31,"adoptionStage":411,"segment":33,"evidenceCount":412,"publicEvidenceCount":412,"organizations":413,"bestGrade":248,"headline":250,"lastVerified":247,"indexable":231},"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.",[17,18],[21,407],"case-management",[409,26,410,25],"content-generation","rag-knowledge-assistant","emerging",4,[414,379,381,385],"Finshark",{"slug":198,"title":416,"shortTitle":417,"definition":418,"status":9,"industries":419,"functions":422,"patterns":425,"audience":30,"autonomy":356,"adoptionStage":32,"segment":357,"evidenceCount":358,"publicEvidenceCount":358,"organizations":428,"bestGrade":248,"headline":435,"lastVerified":247,"indexable":231},"AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[17,18,420,212,421],"cross-industry","telecommunications",[20,423,424],"onboarding-and-kyc","lending-and-credit",[426,23,427,24],"document-processing","computer-vision",[429,430,431,432,433,434],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":47,"label":364,"unit":436,"n":313,"nUpTo":309,"kind":366,"value":437,"qualifier":226,"claimant":228,"organization":432,"vendorReported":210},"multiplier",2.5,{"indexable":231,"reasons":439},[],[441,447,452,460,468,473,479,485,490,497,504,509,516,522,527,532,538,544,550,556,562,568,574,579,584,590,596,601,606,614,620,623,629,634],{"id":140,"label":442,"issuer":443,"region":155,"url":444,"description":445,"useCases":446,"indexable":231},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":141,"label":448,"issuer":443,"region":155,"url":449,"description":450,"useCases":451,"indexable":231},"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":453,"label":454,"issuer":455,"region":456,"url":457,"description":458,"useCases":459,"indexable":231},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":461,"label":462,"issuer":463,"region":464,"url":465,"description":466,"useCases":467,"indexable":231},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":145,"label":469,"issuer":443,"region":155,"url":470,"description":471,"useCases":472,"indexable":231},"DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":142,"label":474,"issuer":475,"region":155,"url":476,"description":477,"useCases":478,"indexable":231},"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":144,"label":480,"issuer":481,"region":155,"url":482,"description":483,"useCases":484,"indexable":231},"FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":146,"label":486,"issuer":160,"region":161,"url":487,"description":488,"useCases":489,"indexable":231},"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":491,"label":492,"issuer":493,"region":161,"url":494,"description":495,"useCases":496,"indexable":231},"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":498,"label":499,"issuer":500,"region":456,"url":501,"description":502,"useCases":503,"indexable":231},"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":147,"label":505,"issuer":506,"region":464,"url":507,"description":508,"useCases":503,"indexable":231},"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":510,"label":511,"issuer":512,"region":155,"url":513,"description":514,"useCases":515,"indexable":231},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":143,"label":517,"issuer":518,"region":456,"url":519,"description":520,"useCases":521,"indexable":231},"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":148,"label":523,"issuer":443,"region":155,"url":524,"description":525,"useCases":526,"indexable":231},"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":528,"label":529,"issuer":443,"region":155,"url":530,"description":531,"useCases":526,"indexable":231},"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":149,"label":533,"issuer":534,"region":464,"url":535,"description":536,"useCases":537,"indexable":231},"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":539,"label":540,"issuer":443,"region":155,"url":541,"description":542,"useCases":543,"indexable":231},"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":545,"label":546,"issuer":547,"region":464,"url":548,"description":549,"useCases":543,"indexable":231},"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":551,"label":552,"issuer":553,"region":456,"url":554,"description":555,"useCases":543,"indexable":231},"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":557,"label":558,"issuer":443,"region":155,"url":559,"description":560,"useCases":561,"indexable":231},"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":563,"label":564,"issuer":565,"region":464,"url":566,"description":567,"useCases":561,"indexable":231},"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":569,"label":570,"issuer":160,"region":161,"url":571,"description":572,"useCases":573,"indexable":231},"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":575,"label":576,"issuer":443,"region":155,"url":577,"description":578,"useCases":573,"indexable":231},"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":580,"label":581,"issuer":443,"region":155,"url":582,"description":583,"useCases":573,"indexable":231},"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":585,"label":586,"issuer":587,"region":155,"url":588,"description":589,"useCases":330,"indexable":231},"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":591,"label":592,"issuer":593,"region":464,"url":594,"description":595,"useCases":376,"indexable":231},"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":597,"label":598,"issuer":443,"region":155,"url":599,"description":600,"useCases":376,"indexable":231},"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":602,"label":603,"issuer":443,"region":155,"url":604,"description":605,"useCases":358,"indexable":231},"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":607,"label":608,"issuer":609,"region":610,"url":611,"description":612,"useCases":613,"indexable":231},"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":615,"label":616,"issuer":617,"region":155,"url":618,"description":619,"useCases":412,"indexable":231},"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":150,"label":621,"issuer":154,"region":155,"url":156,"description":622,"useCases":412,"indexable":231},"UK APP scam reimbursement rules","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":624,"label":625,"issuer":626,"region":161,"url":627,"description":628,"useCases":342,"indexable":231},"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":630,"label":631,"issuer":443,"region":155,"url":632,"description":633,"useCases":342,"indexable":231},"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":635,"label":636,"issuer":637,"region":464,"url":638,"description":639,"useCases":342,"indexable":231},"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.",1790598300690]