[{"data":1,"prerenderedAt":635},["ShallowReactive",2],{"uc-spam-and-scam-call-blocking":3,"uc-regulations":426},{"useCase":4,"evidence":194,"blitsAiDeployments":331,"benchmarks":332,"indicative":347,"related":350,"indexability":424,"includeUnpublished":200},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":26,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":48,"valueDrivers":49,"kpis":53,"indicativeValue":58,"macroEstimates":92,"feasibility":93,"implementation":106,"risk":148,"blitsAi":172,"faq":174,"related":184,"datePublished":188,"dateModified":188,"lastVerified":189,"changelog":190,"slug":193},"AI spam and scam call blocking for mobile and landline subscribers","Spam and scam call blocking","AI scam and spam call blocking for telcos","Operators use AI to block and label scam calls before customers answer. O2 labels about 70 million calls a month; Bell has blocked or labelled over 540 million.","published","AI in the operator's network that protects subscribers from unwanted calls: it analyses incoming calls in real time, blocks known fraudulent calls, and labels suspected scam, spam and spoofed calls on the customer's screen before they answer, so subscribers can decide whether to pick up. Fraud against the operator itself, such as SIM swap or revenue share fraud, is a separate use case.",[12,13,14,15,16],"scam call protection","spam call labelling","robocall blocking","spoofed call detection","caller risk warning",[18],"telecommunications",[20,21],"fraud-prevention","customer-service",[23,24,25],"anomaly-detection","classification-and-routing","prediction-and-scoring",[27,28,29],"voice","mobile-app","api","customer-facing","autonomous","mainstream","customer-protection","Phone scams are one of the main ways criminals reach victims. Callers pose as a bank, a tax\nauthority, an online retailer or the operator itself, often with a spoofed number that looks local\nor familiar, and push people to hand over details or move money. In the UK, Virgin Media O2 and\nHiya found fake Amazon, HMRC and banking calls at the top of the list of nuisance calls in early\n2026, and in Australia Telstra cites the ACCC's\nfinding that phone scams accounted for the highest overall financial losses among all contact\nmethods in Australia in 2024. The side effect is that people stop answering unknown numbers: in\nTelstra's research, 42% of Australians with a mobile device say they are less likely to answer\ncalls because of scam fears, which also hurts the legitimate organizations that need to reach them.\n\nScammers constantly adapt their numbers and tactics, so static block lists fall behind. Operators\nalso have to avoid blocking genuine calls, which is why some malicious calls still slip through.\nThe network sees signals no single phone can see: how many calls a number makes and whether a call\nclaiming a local number actually arrives from abroad. Using those signals at scale, in real time,\nis where machine learning helps; BT says its vendor Hiya uses machine learning to improve scam\ndetection the more malicious calls it encounters.",[36,41,43],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"Telstra cites the ACCC's Targeting Scams report, under which phone scams accounted for the highest overall financial losses among all contact methods in Australia in 2024, with AUD 107.2 million reported lost across 2,179 reports.","Suspicious phone calls: what Telstra is doing to raise the alarm","https://www.telstra.com.au/exchange/suspicious-phone-calls--what-telstra-is-doing-to-raise-the-alarm",2025,{"statement":42,"sourceTitle":38,"sourceUrl":39,"year":40},"Telstra reports research with YouGov under which 42% of Australians who own a mobile device are less likely to answer calls out of concern about being scammed.",{"statement":44,"sourceTitle":45,"sourceUrl":46,"year":47},"O2 cites Hiya's State of the Call report, under which 16% of UK consumers fell victim to phone scams in the previous year, losing an average of GBP 798 each.","O2 launches free AI-powered scam call detection service to help combat fraud and nuisance calls","https://news.virginmediao2.co.uk/o2-launches-free-ai-powered-scam-call-detection-service-to-help-combat-fraud-and-nuisance-calls/",2024,"1. **Analyse every unknown call.** When a call arrives from an unknown number, the model scores it\n   in real time on the behaviour of the calling number (for example a high volume of calls from a\n   single number), where the call really comes from, customer reports and other data points.\n2. **Detect spoofing.** Models and network checks flag calls whose presented number does not match\n   where the call really comes from, such as an overseas call showing a local mobile number.\n3. **Block or label.** Known fraudulent calls are blocked or diverted to voicemail; suspected scam or\n   spam calls are delivered with a warning label on the handset or landline display; verified\n   businesses can show their name.\n4. **Learn from reports.** Customer reports (for example to the 7726 short code in the UK) and\n   investigations are used to block the numbers behind them and to refine the blocking services,\n   so new scam trends are identified and blocked faster.\n5. **Trace and shut down.** Operators work with other carriers and regulators to trace scam calls\n   back to their origin and stop the parties bringing them into the network.",[50,51,52],"risk-reduction","customer-experience","inclusion-and-access",[54,55,56,57],"interactions-handled","users-served","fraud-loss-reduction","customer-satisfaction",{"referenceOrg":59,"inputs":60,"formula":87,"currency":88,"period":89,"resultLabel":90,"caveat":91},"A mobile operator with 5 million subscribers",[61,66,73,80],{"key":62,"label":63,"low":64,"high":64,"unit":62,"note":65},"subscribers","Subscribers protected",5000000,"The reference operator.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"victimRate","Share of subscribers who lose money to a phone scam in a year",0.002,0.005,"fraction of subscribers","Editorial assumption, deliberately far below the survey figure cited on this page (Hiya reports 16% of UK consumers fell victim to phone scams), because survey victimisation includes small and unreported losses.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"averageLoss","Average loss per victim",500,1000,"USD per victim","Editorial assumption; Hiya's survey cited on this page reports an average loss of GBP 798 per UK victim.",{"key":81,"label":82,"low":83,"high":84,"unit":85,"note":86},"preventedShare","Share of those losses prevented by blocking and warnings",0.1,0.3,"fraction of losses","Editorial assumption. O2 reports that calls labelled suspected scam are answered 42% less often; not every unanswered scam call is a prevented loss.","subscribers * victimRate * averageLoss * preventedShare","USD","per year","Customer scam losses prevented","Customer losses only, and a rough order of magnitude. It leaves out the operator's savings on scam related complaints and contacts, the value of customers trusting calls again, and the cost of genuine calls that are wrongly labelled or blocked.",[],{"complexity":94,"complexityNote":95,"dataPrerequisites":96,"integrations":101},"medium","Specialist vendors can provide the scoring and labelling (BT and O2 use Hiya), while Telstra and Bell describe their own network capabilities. Either way, the work is integration with the voice network, handset support, handling of wrongly labelled businesses, and regulatory alignment on blocking.",[97,98,99,100],"Real time call signalling and call detail records","Caller authentication data where available (for example STIR/SHAKEN in North America)","Customer scam reports and complaints","Registry of verified business numbers",[102,103,104,105],"Voice core and signalling platforms for blocking and diversion","Handset or network based caller display for labels","Customer reporting channels such as the 7726 short code in the UK","Industry traceback and intelligence sharing with other carriers and regulators",{"steps":107,"guardrails":123,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[108,111,114,117,120],{"title":109,"detail":110},"Start with the network signals you control","Block numbers that should never originate calls and calls that present a domestic number but arrive from abroad, before adding model based scoring.",{"title":112,"detail":113},"Label before you block","For uncertain calls, a warning label lets the customer decide and gives you feedback. Block only above a high confidence threshold.",{"title":115,"detail":116},"Handle false positives fast","Give businesses a way to register numbers and dispute labels, and give customers a way to report missed scams and wrongly flagged calls.",{"title":118,"detail":119},"Cover every customer group","Extend protection to landlines and older handsets, where vulnerable customers are concentrated, not only to the newest smartphones.",{"title":121,"detail":122},"Measure harm, not only volume","Track answer rates on labelled calls and scam reports per thousand customers, not just the number of calls blocked.",[124,125,126,127],"Blocking only above a validated confidence threshold; everything else is labelled, not blocked","Emergency and priority numbers never blocked","A dispute process for businesses whose calls are wrongly labelled","Clear customer information about what is analysed and how to switch labelling off where permitted","Fraud analysts set blocking thresholds and review new campaign patterns, a team handles disputes from businesses, and customer reports are treated as training signals. Blocking rules follow the national regulator's requirements.",[130,131,132,133,134],"Calls blocked and labelled per month, per category","Answer rate and call duration for labelled versus unlabelled calls","Scam reports per thousand customers","Disputes from businesses and share upheld","Share of customers covered, including landlines and older devices",[136,139,142,145],{"title":137,"detail":138},"Genuine calls marked as scam","Hospitals, schools or delivery firms get labelled and people stop answering them. Run a fast dispute process and verified caller programmes.",{"title":140,"detail":141},"Volume as the only measure","Billions of calls blocked says little about harm prevented. Track answer rates and scam reports.",{"title":143,"detail":144},"Protection only for new phones","Handset based labels miss older devices and landlines. Combine network blocking with display features for all lines.",{"title":146,"detail":147},"Scammers move to other channels","Scammers shift between calls, texts and messaging apps as each channel gets harder to use. Coordinate call and message protection.",{"euAiAct":149,"regulations":152,"guidance":159,"controls":166,"incidents":171},{"tier":150,"basis":151},"minimal","Scoring, blocking and labelling calls is not listed in Annex III, is not a prohibited practice under Article 5, and the system does not interact with people or generate content, so Article 50 does not apply. A conversational scambaiting agent such as O2's Daisy talks to callers with a synthetic voice, which raises separate Article 50 transparency questions and should be assessed on its own.",[153,154,155,156,157,158],"telecom-consumer-rules","gdpr","uk-gdpr","eu-ai-act","eecc","au-scams-prevention-framework",[160],{"title":161,"issuer":162,"region":163,"url":164,"note":165},"Scam calls and messages","Ofcom","europe","https://www.ofcom.org.uk/phones-and-broadband/scam-calls-and-messages","UK regulator hub on scam calls and messages, including its statement on tackling scam calls from abroad, which covers its calling line identification guidance on how providers should process calls from abroad that present a UK mobile number.",[167,168,169,170],"Documented blocking and labelling policy aligned with national rules","Monitoring of false positives and business disputes","Privacy notice and legal basis for analysing call metadata","Regular review of thresholds against new scam campaigns",[],{"howToBuild":173},"Call scoring and labelling run in the voice network with specialist vendors. Blits.ai covers the\nconversations around it: a **voice or chat agent** on **web chat, WhatsApp, SMS and the phone**\nhelps customers report a scam, check whether a call was genuine, and get advice on what to do\nnext, from a **knowledge base** of approved scam guidance with hybrid retrieval, and hands\ndistressed or vulnerable callers to a human with **human handover**.\n\nA second **AI agent** can help business customers dispute a wrong label, collecting evidence in\na **flow** and routing it to the fraud team through **custom functions**. **Guardrails** stop the\nagent from giving advice outside approved content, **PII masking** protects numbers and personal\ndata, and **analytics** show report volumes and themes.",[175,178,181],{"question":176,"answer":177},"How many calls do operators block or label?","Bell reports analysing more than 4.4 billion calls and blocking or labelling over 540 million since launching Suspicious Call Detection in 2025. Virgin Media O2 labels around 70 million suspected scam and spam calls a month. Telstra reports blocking more than 11 million scam calls a month on average and showing Scam Protect warnings on about 12 million calls a month.",{"question":179,"answer":180},"Do warning labels actually change behaviour?","Virgin Media O2 reports that calls labelled suspected scam are answered 42% less often and last 89% less time than unflagged calls.",{"question":182,"answer":183},"Can AI also fight back against scammers?","O2 built Daisy, an AI voice persona that answers scam calls and keeps fraudsters talking, in some cases for 40 minutes, to waste their time and expose their tactics. It is an awareness and disruption tool rather than a replacement for network blocking.",[185,186,187],"telecom-fraud-detection","scam-payment-interception","mule-network-detection","2026-09-27","2026-09-26",[191],{"date":188,"note":192},"First published","spam-and-scam-call-blocking",[195,230,261,291,308],{"title":196,"useCases":197,"organization":198,"vendors":203,"summary":204,"stage":205,"year":40,"channels":206,"languages":207,"metrics":210,"outcomeDisclosed":219,"sources":220,"verification":225,"grade":227,"id":228,"organizationSlug":229},"Bell: AI Suspicious Call Detection and spoofed call flagging",[193],{"name":199,"anonymized":200,"country":201,"region":202,"industry":18},"Bell Canada",false,"CA","north-america",[],"Bell launched AI powered Suspicious Call Detection in 2025 to block or label spam and fraudulent calls for its wireless customers. In July 2026 it added a new AI model that identifies and flags spoofed calls, which manipulate caller ID to impersonate trusted organizations or contacts, in real time. The enhancement rolls out automatically, with no opt in required, to customers on iOS and Android phones across Bell, Virgin Plus, Lucky Mobile, PC Mobile, No Name and Maxi. Bell says it was the first carrier in Canada to flag spoofed calls this way.","scaled",[27,28],[208,209],"en","fr",[211],{"kpi":54,"value":212,"unit":213,"qualifier":214,"period":215,"claimant":216,"quote":217,"sourceUrl":218},540000000,"count","at-least","2025 launch to July 2026, calls blocked or labelled","organization","Since launching Suspicious Call Detection in 2025, Bell has analyzed more than 4.4 billion calls and blocked or labelled over 540 million suspicious or fraudulent calls.","https://www.bce.ca/news-and-media/newsroom?article=bell-first-carrier-in-canada-to-detect-and-protect-against-spoofed-calls-with-ai",true,[221],{"url":218,"title":222,"publisher":223,"date":224},"Bell first carrier in Canada to detect and protect against spoofed calls with AI","BCE","2026-07-28",{"level":226,"checkedAt":189},"source-verified","B","bell-suspicious-call-detection",null,{"title":231,"useCases":232,"organization":233,"vendors":236,"summary":240,"stage":205,"year":47,"channels":241,"languages":242,"metrics":243,"outcomeDisclosed":219,"sources":254,"verification":258,"grade":227,"id":259,"organizationSlug":260},"BT: Enhanced Call Protect scam and spam screening on Digital Voice landlines",[193],{"name":234,"anonymized":200,"country":235,"region":163,"industry":18},"BT Group","GB",[237],{"name":238,"role":239},"Hiya","platform","BT's Digital Voice home phone service includes Enhanced Call Protect, an AI powered tool from Hiya that monitors incoming calls, diverts scam calls to a junk voicemail and shows a \"Nuisance?\" warning on the landline display for suspected spam, while showing the name of registered businesses. In its first four months it blocked more than 2.4 million scam calls and identified about 17.7 million spam calls. BT also runs an AI network level firewall against calls from abroad that use a UK number for scam purposes.",[27],[208],[244,249],{"kpi":54,"value":245,"unit":213,"qualifier":214,"period":246,"claimant":216,"quote":247,"sourceUrl":248},2430000,"May to early October 2024, scam calls blocked","BT’s new Enhanced Call Protect on Digital Voice has successfully blocked more than 2,430,000 scam and identified 17,700,000 spam calls to landlines since the new scam protection service from Hiya was introduced in May.","https://newsroom.bt.com/bts-new-home-phone-scam-protection-service-stops-201-million-scam-and-spam-attempts-in-first-4-months/",{"kpi":55,"value":250,"unit":213,"qualifier":251,"period":252,"claimant":216,"quote":253,"sourceUrl":248},2500000,"exact","October 2024","2.5 million BT customers already receive the new call vetting service, a benefit of migrating to Digital Voice.",[255],{"url":248,"title":256,"publisher":234,"date":257},"BT’s new home phone scam protection service stops 20.1 million scam and spam attempts in first 4 months","2024-10-03",{"level":226,"checkedAt":189},"bt-enhanced-call-protect","bt-group",{"title":262,"useCases":263,"organization":264,"vendors":266,"summary":268,"stage":205,"year":47,"channels":269,"languages":270,"metrics":271,"outcomeDisclosed":219,"sources":282,"verification":288,"grade":227,"id":289,"organizationSlug":290},"Virgin Media O2: Call Defence AI scam and spam call labelling",[193],{"name":265,"anonymized":200,"country":235,"region":163,"industry":18},"Virgin Media O2",[267],{"name":238,"role":239},"O2 launched Call Defence in November 2024 at no extra cost. Built with Hiya, it uses adaptive AI to analyse the behaviour of unknown numbers in real time and shows a warning label on the customer's screen for suspected scam or spam calls, while also blocking known fraudulent calls. It rolled out automatically on Android and on iOS 18 and later. By March 2026 it had labelled more than 1 billion calls, and O2 reports that calls labelled suspected scam are answered 42% less often and last 89% less time than unflagged calls.",[27,28],[208],[272,278],{"kpi":54,"value":273,"unit":213,"qualifier":274,"period":275,"claimant":216,"quote":276,"sourceUrl":277},70000000,"approximately","per month, calls labelled as suspected scam or spam (2026)","O2 first launched the service for its customers in November 2024, and today around 70 million calls every month are being labelled as suspected scam or spam.","https://news.virginmediao2.co.uk/ai-helps-virgin-media-o2-detect-and-flag-1-billion-suspected-scam-and-spam-calls-to-customers/",{"kpi":54,"value":279,"unit":213,"qualifier":214,"period":280,"claimant":216,"quote":281,"sourceUrl":277},1000000000,"cumulative, November 2024 to March 2026","Virgin Media O2 has today reached a major milestone in its fight against fraudsters, after using AI to successfully label more than 1 billion suspected scam and spam calls to O2 customers.",[283,286],{"url":277,"title":284,"publisher":265,"date":285},"AI helps Virgin Media O2 detect and flag 1 billion suspected scam and spam calls to customers","2026-03-06",{"url":46,"title":45,"publisher":265,"date":287},"2024-11-28",{"level":226,"checkedAt":189},"virgin-media-o2-call-defence","virgin-media-o2",{"title":292,"useCases":293,"organization":294,"vendors":295,"summary":296,"stage":297,"year":47,"channels":298,"languages":299,"metrics":300,"outcomeDisclosed":219,"sources":301,"verification":306,"grade":227,"id":307,"organizationSlug":290},"Virgin Media O2: Daisy, an AI voice persona that wastes scammers' time",[193],{"name":265,"anonymized":200,"country":235,"region":163,"industry":18},[],"O2 created Daisy, a lifelike AI voice persona of an elderly woman, trained with help from the scambaiter Jim Browning. Daisy combines several AI models to listen and respond to scam callers in real time without human input, telling long stories and giving false details so fraudsters spend their time on her instead of real victims. O2 says Daisy has kept fraudsters on calls for 40 minutes at a time. It was launched as part of O2's Swerve the Scammers awareness campaign and is a disruption and awareness tool rather than a protection service for individual customers.","pilot",[27],[208],[],[302],{"url":303,"title":304,"publisher":265,"date":305},"https://news.virginmediao2.co.uk/o2-unveils-daisy-the-ai-granny-wasting-scammers-time/","O2 unveils Daisy, the AI granny wasting scammers’ time","2024-11-14",{"level":226,"checkedAt":189},"virgin-media-o2-daisy-ai-scambaiter",{"title":309,"useCases":310,"organization":311,"vendors":315,"summary":316,"stage":205,"year":317,"channels":318,"languages":319,"metrics":320,"outcomeDisclosed":219,"sources":321,"verification":328,"grade":227,"id":329,"organizationSlug":330},"Telstra: network level blocking of scam, spoofed and Wangiri calls",[193,185],{"name":312,"anonymized":200,"country":313,"region":314,"industry":18},"Telstra","AU","asia-pacific",[],"As part of its Cleaner Pipes initiative, Telstra blocks suspected scam calls in its network before they reach customers. Upgrades in 2021 made blocking more aggressive, improved detection of Wangiri one ring calls from international premium numbers and of spoofed calls that pretend to come from local numbers or trusted brands, and doubled the monthly volume blocked within four months. Telstra says it keeps evolving its algorithms and detection methods and takes care not to block genuine calls. From December 2024 it added Telstra Scam Protect, an in house network feature that warns customers on screen about calls that look spoofed, arrive from overseas while showing a local number, or come from a number with a suspicious calling pattern. Its Scam Protect article (published March 2025, updated May 2026) reports blocking more than 11 million scam calls a month on average and Scam Protect warnings on an average of 12 million calls a month.",2021,[27],[208],[],[322,326],{"url":323,"title":324,"publisher":312,"date":325},"https://www.telstra.com.au/exchange/were-now-blocking-over-13-million-scam-calls-a-month","We're now blocking over 13 million scam calls a month","2021-06-15",{"url":39,"title":38,"publisher":312,"date":327},"2025-03-13",{"level":226,"checkedAt":189},"telstra-scam-call-blocking","telstra",0,[333,341],{"kpi":54,"label":334,"unit":213,"aggregate":200,"higherIsBetter":219,"n":335,"nUpTo":331,"median":273,"min":245,"max":212,"byClaimant":336,"vendorOnly":200,"points":337},"Interactions handled",3,{"organization":335,"vendor":331,"regulator":331,"independent":331},[338,339,340],{"evidenceId":228,"organization":199,"value":212,"qualifier":214,"claimant":216,"grade":227,"pooled":219},{"evidenceId":289,"organization":265,"value":273,"qualifier":274,"claimant":216,"grade":227,"pooled":219},{"evidenceId":259,"organization":234,"value":245,"qualifier":214,"claimant":216,"grade":227,"pooled":219},{"kpi":55,"label":342,"unit":213,"aggregate":200,"higherIsBetter":219,"n":343,"nUpTo":331,"median":250,"min":250,"max":250,"byClaimant":344,"vendorOnly":200,"points":345},"Users served",1,{"organization":343,"vendor":331,"regulator":331,"independent":331},[346],{"evidenceId":259,"organization":234,"value":250,"qualifier":251,"claimant":216,"grade":227,"pooled":219},{"low":348,"high":349},500000,7500000,[351,372,396,411],{"slug":185,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":356,"patterns":359,"audience":360,"autonomy":361,"adoptionStage":362,"segment":33,"evidenceCount":363,"publicEvidenceCount":363,"organizations":364,"bestGrade":227,"headline":366,"lastVerified":188,"indexable":219},"AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","AI that protects the operator's own network, revenue and numbers from fraud: it watches call, messaging, roaming and account activity to detect SIM swap and port out takeovers, international revenue share fraud (IRSF) and Wangiri one ring scams, blocks or flags them in real time, and shares risk signals with banks and other businesses that rely on the phone number for security. Scam calls aimed at subscribers are handled by call blocking.",[18],[20,357,358],"network-operations","security-operations",[23,25,24],"back-office","supervised-agent","early-adopters",4,[312,365],"Vodafone",{"kpi":367,"label":368,"unit":369,"n":343,"nUpTo":331,"kind":370,"value":371,"qualifier":251,"claimant":216,"organization":365,"vendorReported":200},"detection-rate-improvement","Detection improvement","percent","reported",30,{"slug":186,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":379,"patterns":380,"audience":30,"autonomy":361,"adoptionStage":362,"segment":384,"evidenceCount":385,"publicEvidenceCount":385,"organizations":386,"bestGrade":227,"headline":392,"lastVerified":189,"indexable":219},"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.",[377,378],"banking","payments",[20,21],[381,25,382,383],"conversational-agent","agentic-workflow","voice-agent","front-office",6,[387,388,389,390,365,391],"Commonwealth Bank of Australia","Mastercard","Revolut","Starling Bank","Westpac",{"kpi":367,"label":368,"unit":369,"n":393,"nUpTo":331,"kind":370,"value":394,"qualifier":251,"claimant":395,"organization":390,"vendorReported":219},2,300,"vendor",{"slug":187,"title":397,"shortTitle":398,"definition":399,"status":9,"industries":400,"functions":401,"patterns":403,"audience":360,"autonomy":405,"adoptionStage":362,"segment":406,"evidenceCount":335,"publicEvidenceCount":335,"organizations":407,"bestGrade":227,"headline":229,"lastVerified":188,"indexable":219},"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.",[377,378],[20,402],"financial-crime-compliance",[23,25,382,404],"summarization","copilot","middle-office",[408,409,410],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":412,"title":413,"shortTitle":414,"definition":415,"status":9,"industries":416,"functions":417,"patterns":419,"audience":30,"autonomy":361,"adoptionStage":362,"segment":384,"evidenceCount":363,"publicEvidenceCount":335,"organizations":421,"bestGrade":227,"headline":229,"lastVerified":188,"indexable":219},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[377,378],[21,20,418],"operations",[381,383,24,420,382],"document-processing",[387,422,423],"Klarna","Visa",{"indexable":219,"reasons":425},[],[427,433,438,446,453,459,465,472,479,486,493,499,506,513,519,524,531,537,543,548,553,559,565,570,575,582,589,594,599,607,613,619,624,629],{"id":156,"label":428,"issuer":429,"region":163,"url":430,"description":431,"useCases":432,"indexable":219},"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":154,"label":434,"issuer":429,"region":163,"url":435,"description":436,"useCases":437,"indexable":219},"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":439,"label":440,"issuer":441,"region":442,"url":443,"description":444,"useCases":445,"indexable":219},"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":447,"label":448,"issuer":449,"region":202,"url":450,"description":451,"useCases":452,"indexable":219},"nist-ai-rmf","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":454,"label":455,"issuer":429,"region":163,"url":456,"description":457,"useCases":458,"indexable":219},"dora","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":155,"label":460,"issuer":461,"region":163,"url":462,"description":463,"useCases":464,"indexable":219},"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":466,"label":467,"issuer":468,"region":163,"url":469,"description":470,"useCases":471,"indexable":219},"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":473,"label":474,"issuer":475,"region":314,"url":476,"description":477,"useCases":478,"indexable":219},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":480,"label":481,"issuer":482,"region":314,"url":483,"description":484,"useCases":485,"indexable":219},"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":487,"label":488,"issuer":489,"region":442,"url":490,"description":491,"useCases":492,"indexable":219},"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":494,"label":495,"issuer":496,"region":202,"url":497,"description":498,"useCases":492,"indexable":219},"us-sr-11-7","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":500,"label":501,"issuer":502,"region":163,"url":503,"description":504,"useCases":505,"indexable":219},"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":507,"label":508,"issuer":509,"region":442,"url":510,"description":511,"useCases":512,"indexable":219},"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.",15,{"id":514,"label":515,"issuer":429,"region":163,"url":516,"description":517,"useCases":518,"indexable":219},"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":520,"label":521,"issuer":429,"region":163,"url":522,"description":523,"useCases":518,"indexable":219},"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":525,"label":526,"issuer":527,"region":202,"url":528,"description":529,"useCases":530,"indexable":219},"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":532,"label":533,"issuer":429,"region":163,"url":534,"description":535,"useCases":536,"indexable":219},"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":538,"label":539,"issuer":540,"region":202,"url":541,"description":542,"useCases":536,"indexable":219},"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":153,"label":544,"issuer":545,"region":442,"url":546,"description":547,"useCases":536,"indexable":219},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":157,"label":549,"issuer":429,"region":163,"url":550,"description":551,"useCases":552,"indexable":219},"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":554,"label":555,"issuer":556,"region":202,"url":557,"description":558,"useCases":552,"indexable":219},"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":560,"label":561,"issuer":475,"region":314,"url":562,"description":563,"useCases":564,"indexable":219},"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":566,"label":567,"issuer":429,"region":163,"url":568,"description":569,"useCases":564,"indexable":219},"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":571,"label":572,"issuer":429,"region":163,"url":573,"description":574,"useCases":564,"indexable":219},"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":576,"label":577,"issuer":578,"region":163,"url":579,"description":580,"useCases":581,"indexable":219},"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":583,"label":584,"issuer":585,"region":202,"url":586,"description":587,"useCases":588,"indexable":219},"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":590,"label":591,"issuer":429,"region":163,"url":592,"description":593,"useCases":588,"indexable":219},"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":595,"label":596,"issuer":429,"region":163,"url":597,"description":598,"useCases":385,"indexable":219},"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":600,"label":601,"issuer":602,"region":603,"url":604,"description":605,"useCases":606,"indexable":219},"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":608,"label":609,"issuer":610,"region":163,"url":611,"description":612,"useCases":363,"indexable":219},"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":614,"label":615,"issuer":616,"region":163,"url":617,"description":618,"useCases":363,"indexable":219},"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":158,"label":620,"issuer":621,"region":314,"url":622,"description":623,"useCases":335,"indexable":219},"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":625,"label":626,"issuer":429,"region":163,"url":627,"description":628,"useCases":335,"indexable":219},"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":630,"label":631,"issuer":632,"region":202,"url":633,"description":634,"useCases":335,"indexable":219},"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.",1790598306323]