[{"data":1,"prerenderedAt":634},["ShallowReactive",2],{"uc-sms-phishing-and-scam-message-blocking":3,"uc-regulations":402},{"useCase":4,"evidence":171,"blitsAiDeployments":303,"benchmarks":304,"indicative":315,"related":318,"indexability":400,"includeUnpublished":177},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":38,"valueDrivers":39,"kpis":42,"indicativeValue":45,"macroEstimates":78,"feasibility":79,"implementation":92,"risk":127,"blitsAi":151,"faq":153,"related":163,"datePublished":166,"dateModified":166,"lastVerified":166,"changelog":167,"slug":170},"AI blocking of phishing and scam text messages in the operator network","Scam text message blocking","AI smishing and scam text blocking for telcos","Operators use AI to stop smishing before it reaches customers. Proximus blocked 3.2 million suspicious messages in two weeks and Telenet 2.4 million after launch.","published","Scam text message blocking is AI in the operator's messaging network that scores text messages as they pass through and stops phishing and scam messages before they reach the subscriber. It looks at sender behavior, message volume and links, and it is separate from labeling scam calls and from fraud against the operator itself.",[12,13,14,15,16],"smishing detection","anti smishing platform","SMS firewall","scam SMS filtering","text message phishing protection",[18],"telecommunications",[20,21],"fraud-prevention","security-operations",[23,24],"classification-and-routing","anomaly-detection",[26],"sms","back-office","autonomous","mainstream","customer-protection","Smishing is phishing sent by text message. The messages remind people of an overdue bill, ask them to\ncomplete missing personal details or promise a refund, and they usually carry a link to a fake website\nthat steals data or money. A text message arrives on the phone's own messaging app and often from a\nnumber the recipient has never seen, so the victim has little to judge it by.\n\nOperators see what a single handset cannot: one number sending a remarkably high number of messages,\nor the same suspicious link appearing in many messages at once. Scammers adapt: Telstra says that if\ncriminals guess a message with a suspicious link has been blocked, they change the characteristics of the\nmessage to avoid being blocked again. Proximus and Telenet group started blocking in autumn 2023 under\nStopPhishing, a federal project that in 2022 subsidized new anti fraud platforms at the two operators for\ntext messages, email and voice calls.",[33],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"Proximus cites Febelfin figures under which fraudsters took nearly EUR 40 million from people in Belgium in 2022 through phishing of all kinds, including text messages, emails and social media posts.","Proximus already stopped over 3 million suspicious text messages thanks to new platform","https:\u002F\u002Fproximus.com\u002Fnews\u002F2023\u002F20231024-proximus-stops-three-million-suspicious-text-messages.html",2023,"1. **Score every message in transit.** The platform sits in the operator's messaging path and looks at\n   patterns such as a number sending an unusually high number of messages, the same suspicious link\n   spreading across messages, and the content of messages that look suspicious where the law allows it.\n2. **Block above a threshold.** Messages the model rates as phishing are stopped automatically and never\n   reach the subscriber. A Telenet group fraud expert gives the example of one number sending up to 10,000 messages in a short time with a suspicious link.\n3. **Retrain on new campaigns.** Proximus says the algorithm can be trained further to recognize phishing\n   messages better over time, and Telenet group describes its platform as self learning, so detection\n   keeps up with new wording and sending patterns.\n4. **Report large campaigns.** Blocked volumes and sender patterns go to the operator's fraud team, and\n   Telenet group files a complaint with the police when it spots a large scam campaign.",[40,41],"risk-reduction","customer-experience",[43,44],"interactions-handled","fraud-loss-reduction",{"referenceOrg":46,"inputs":47,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A mobile operator with 3 million subscribers",[48,53,60,66],{"key":49,"label":50,"low":51,"high":51,"unit":49,"note":52},"subscribers","Subscribers protected",3000000,"The reference operator.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"victimRate","Share of subscribers who lose money to a scam text message in a year",0.001,0.003,"fraction of subscribers","Editorial assumption, replace with your own fraud report data.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":59},"averageLoss","Average loss per victim",300,800,"EUR per victim",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"preventedShare","Share of those losses prevented by blocking",0.2,0.5,"fraction of losses","Editorial assumption, deliberately conservative. Proximus quotes a UK initiative that led to a 90% drop in smishing complaints, but a complaint count is not a loss count.","subscribers * victimRate * averageLoss * preventedShare","EUR","per year","Customer losses prevented","Customer losses only, and a rough order of magnitude. It leaves out the operator's savings on scam related contacts, the effect on customer trust and the cost of genuine messages that are blocked by mistake.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"medium","The platform needs access to the messaging core (SMS center and messaging firewall) and a legal basis to inspect message content, which Belgian operators received through a change in the law. The Belgian platforms were built with public funding that covered half of the cost over three years, so the business case and the regulatory permission come before the model work.",[83,84,85,86],"Message metadata from the SMS center, such as sender, volume and timing","Message content and links, where the law permits inspection","Confirmed scam reports from customers and from the national cybersecurity authority","Lists of legitimate bulk senders such as banks and delivery firms",[88,89,90,91],"Messaging core and SMS firewall for blocking in the path of the message","Fraud team case tools for reviewing sender patterns","Customer reporting channel for missed scams and wrongly blocked messages","Exchange of sender intelligence with other operators and regulators",{"steps":93,"guardrails":106,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[94,97,100,103],{"title":95,"detail":96},"Start with sender behavior","Block numbers that send an unusually high number of messages and links already known as malicious before you read message content.",{"title":98,"detail":99},"Settle the legal basis","Agree with the data protection officer and the regulator what may be inspected, for how long data is kept and who may see it, then add content analysis for suspicious messages.",{"title":101,"detail":102},"Register legitimate senders","Give banks, delivery firms and authorities a way to register sender names and links so their bulk messages are not blocked.",{"title":104,"detail":105},"Measure blocked volume and harm","Track blocked messages per day next to scam reports per thousand customers, so a falling report rate confirms that blocking works.",[107,108,109,110],"Block automatically only above a validated confidence threshold","Messages to emergency services and from registered public authorities are never blocked","Content inspection limited to messages that are already suspicious on behavior","A dispute path for senders whose messages are wrongly blocked","Fraud analysts set thresholds, review new campaign patterns and handle disputes from senders. Customers can report missed scams, and those reports feed the next round of training.",[113,114,115,116],"Suspicious messages blocked per day, per category","Scam reports per thousand customers","Legitimate messages blocked by mistake and disputes upheld","Time from first appearance of a campaign to its block",[118,121,124],{"title":119,"detail":120},"Legitimate bulk senders blocked","Banks and delivery firms send many messages with links. Without a sender register, genuine one time codes and parcel notices get stopped.",{"title":122,"detail":123},"Blocked volume reported as the result","Millions of blocked messages say little about harm prevented. Pair the count with scam reports and reported losses.",{"title":125,"detail":126},"Scammers move channel","Scammers shift to email, voice calls and messaging apps once text messages get harder. Coordinate protection across channels.",{"euAiAct":128,"regulations":131,"guidance":138,"controls":145,"incidents":150},{"tier":129,"basis":130},"minimal","Scoring and blocking messages is not listed in Annex III and is not a prohibited practice under Article 5. The system does not interact with people or generate content, so Article 50 does not apply. A message filter protects subscribers from fraud and is not a safety component in the management and operation of critical digital infrastructure under Annex III point 2 (Recital 55 excludes components used solely for cybersecurity). It is governed mainly by telecom and data protection law.",[132,133,134,135,136,137],"telecom-consumer-rules","gdpr","eecc","eu-ai-act","mas-shared-responsibility-framework","au-scams-prevention-framework",[139],{"title":140,"issuer":141,"region":142,"url":143,"note":144},"Scam calls and messages","Ofcom","europe","https:\u002F\u002Fwww.ofcom.org.uk\u002Fphones-and-broadband\u002Fscam-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.",[146,147,148,149],"Documented blocking policy aligned with national telecom law","Privacy notice and legal basis for inspecting message content","Monitoring of false positives and sender disputes","Regular review of thresholds against new scam campaigns",[],{"howToBuild":152},"Blits.ai does not filter messages in the network: scoring and blocking run in the operator's messaging\ncore. Blits.ai fits the reporting and support around that filter. Customers forward a suspicious text to\nan **agent** on **SMS, WhatsApp or web chat**. The agent extracts the sender and any link, passes them to\nthe fraud team's tools through **custom functions** so a missed campaign can be blocked, and answers from\na **knowledge base** of approved scam guidance whether the operator ever sends this kind of message. A\ncustomer who already clicked a link or shared bank details goes to a person through **human handover**.\n\nBusiness senders whose genuine messages were blocked use a **flow** that collects the sender name,\nsample messages and timing, and routes the dispute to the fraud team. **Guardrails** keep answers inside\napproved advice, **PII masking** hides numbers and personal data in logs, and **analytics** show report\nvolumes and themes. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia,\nin your private cloud, or fully on premises.",[154,157,160],{"question":155,"answer":156},"How many scam text messages do operators block?","Proximus reported 3.2 million suspicious messages blocked in the first two weeks after its platform went live on 4 October 2023, and Telenet and BASE reported 2.4 million after launch in autumn 2023. VRT NWS reported in September 2025 that Proximus and Telenet group together blocked 37.3 million text messages in 2024, and 10.75 million text messages, 165 million emails and 16 million incoming voice calls between 1 January and 6 July 2025. Telstra reported more than 225 million scam text messages blocked between April and December 2022.",{"question":158,"answer":159},"Is this the same as scam call blocking?","No. Call blocking labels and stops scam calls on the voice network, while this use case works on text messages in the messaging network. Operators often run both, and VRT NWS reports that the Belgian systems also filter voice calls and email.",{"question":161,"answer":162},"Why do operators need a change in the law for this?","Reading message content is restricted by the confidentiality of communications under the ePrivacy Directive. A Telenet group fraud expert told VRT NWS that a change in the law lets its systems look at the content of messages that already look suspicious.",[164,165],"spam-and-scam-call-blocking","telecom-fraud-detection","2026-11-11",[168],{"date":166,"note":169},"First published","sms-phishing-and-scam-message-blocking",[172,214,239,269],{"title":173,"useCases":174,"organization":175,"vendors":180,"summary":183,"stage":184,"year":37,"channels":185,"languages":186,"metrics":187,"outcomeDisclosed":195,"sources":196,"verification":209,"grade":211,"id":212,"organizationSlug":213},"Proximus: platform that detects and blocks smishing text messages",[170],{"name":176,"anonymized":177,"country":178,"region":142,"industry":18,"wikidata":179},"Proximus",false,"BE","Q2005282",[181],{"name":176,"role":182},"in-house","Proximus, the Belgian telecom operator, put a platform into use on 4 October 2023 that detects and automatically blocks smishing, phishing sent by text message, before the messages reach customers. The platform looks for patterns such as numbers that send a remarkably high number of messages and suspicious links, and Proximus says artificial intelligence lets the algorithm be trained further to recognize phishing messages better. The operator reported 3.2 million blocked messages in the first two weeks. The platform was developed with public funding under the federal StopPhishing project, and Belgian press reported in 2025 that Proximus and Telenet group filter text messages, emails and voice calls with detection systems trained with artificial intelligence.","scaled",[26],[],[188],{"kpi":43,"value":189,"unit":190,"qualifier":191,"period":192,"claimant":193,"quote":194,"sourceUrl":36},3200000,"count","exact","first two weeks after launch on 4 October 2023, suspicious text messages blocked","organization","Initial results are promising: in the first two weeks after implementation, as many as 3.2 million suspicious messages were blocked, which comes down to 229,000 per day on average.",true,[197,200,205],{"url":36,"title":35,"publisher":198,"date":199},"Proximus Group","2023-10-24",{"url":201,"title":202,"publisher":203,"date":204},"https:\u002F\u002Fwww.vrt.be\u002Fvrtnws\u002Fnl\u002F2024\u002F02\u002F27\u002Fproximus-houdt-16-2-miljoen-valse-smsen-tegen\u002F","Proximus en Telenet houden 28 miljoen valse sms'en tegen in 5 maanden tijd","VRT NWS","2024-02-27",{"url":206,"title":207,"publisher":203,"date":208},"https:\u002F\u002Fwww.vrt.be\u002Fvrtnws\u002Fnl\u002F2025\u002F09\u002F18\u002Ftelenet-group-proximus-detectie-valse-sms-mail-telefoon\u002F","Proximus en Telenet blokkeren 165 miljoen valse emails in strijd tegen fraude","2025-09-18",{"level":210,"checkedAt":166},"source-verified","B","proximus-ai-smishing-blocking-platform",null,{"title":215,"useCases":216,"organization":217,"vendors":220,"summary":222,"stage":184,"year":37,"channels":223,"languages":224,"metrics":225,"outcomeDisclosed":195,"sources":231,"verification":237,"grade":211,"id":238,"organizationSlug":213},"Telenet group: self learning platform that blocks smishing text messages",[170],{"name":218,"anonymized":177,"country":178,"region":142,"industry":18,"wikidata":219},"Telenet group","Q616340",[221],{"name":218,"role":182},"Telenet and BASE, the mobile brands of Telenet group, started using a self learning platform in autumn 2023 that detects and automatically blocks smishing, phishing by text message. The platform recognizes patterns such as numbers that send a very high number of messages and suspicious links being sent around. Telenet group paid half of the development and running cost, and the rest came from public funding. In VRT NWS reported in February 2024 that Telenet and Proximus had used new software with AI since October, and in 2025 a Telenet group fraud expert told VRT NWS that the operator's detection systems for text messages, emails and voice calls are trained with artificial intelligence.",[26],[],[226],{"kpi":43,"value":227,"unit":190,"qualifier":191,"period":228,"claimant":193,"quote":229,"sourceUrl":230},2400000,"since the launch of the platform in autumn 2023, suspicious text messages blocked","Since the implementation, 2.4 million suspicious messages were blocked, which comes down to 170,000 per day on average.","https:\u002F\u002Fpress.telenet.be\u002Ftelenet-and-base-already-blocked-over-2-million-suspicious-text-messages-thanks-to-new-platform",[232,235,236],{"url":230,"title":233,"publisher":218,"date":234},"Telenet and BASE already blocked over 2 million suspicious text messages thanks to new platform","2023-11-08",{"url":201,"title":202,"publisher":203,"date":204},{"url":206,"title":207,"publisher":203,"date":208},{"level":210,"checkedAt":166},"telenet-smishing-blocking-platform",{"title":240,"useCases":241,"organization":242,"vendors":247,"summary":248,"stage":184,"year":249,"channels":250,"languages":251,"metrics":253,"outcomeDisclosed":195,"sources":259,"verification":266,"grade":211,"id":267,"organizationSlug":268},"Telstra: network level SMS scam filter that blocks malicious text messages",[170],{"name":243,"anonymized":177,"country":244,"region":245,"industry":18,"wikidata":246},"Telstra","AU","asia-pacific","Q721162",[],"As part of its Cleaner Pipes initiative, Telstra launched an SMS scam filter in April 2022 that scans text messages as they travel across its network and blocks malicious messages with suspicious links or telephone numbers before they reach the customer. Telstra says the content is scanned by a machine that looks for suspicious patterns and characteristics, and that a small team in Australia reviews a sample of suspected messages, with the recipient's identity masked, to confirm a scam and adjust the technology to block future messages with the same criteria or near matches. Messages from banks and other businesses, government departments and Emergency Alerts are not scanned, and customers can opt out. The filter product comes from an unnamed specialist telecommunications supplier and is operated by Telstra staff in Australia. Telstra reported more than 225 million scam messages blocked between April and December 2022.",2022,[26],[252],"en",[254],{"kpi":43,"value":255,"unit":190,"qualifier":191,"period":256,"claimant":193,"quote":257,"sourceUrl":258},225000000,"April to December 2022, scam text messages blocked","Since then, we have continued to block millions of scam messages, with a staggering 225 million blocked since April this year.","https:\u002F\u002Fwww.telstra.com.au\u002Fexchange\u002Fwe-ve-blocked-over-225-million-scam-text-messages-since-april.html",[260,263],{"url":258,"title":261,"publisher":243,"date":262},"We've blocked over 225 million scam text messages since April","2022-12-29",{"url":264,"title":265,"publisher":243},"https:\u002F\u002Fwww.telstra.com.au\u002Fcyber-security-and-safety\u002Factive-scams\u002Fblock-malicious-sms-with-sms-scam-filter","SMS scam filter blocking malicious SMS before they reach you",{"level":210,"checkedAt":166},"telstra-sms-scam-filter","telstra",{"title":270,"useCases":271,"organization":272,"vendors":276,"summary":278,"stage":184,"year":279,"channels":280,"languages":282,"metrics":283,"outcomeDisclosed":195,"sources":290,"verification":299,"grade":301,"id":302,"organizationSlug":213},"Bharti Airtel: AI powered Safe Network spam and fraud detection",[164,170],{"name":273,"anonymized":177,"country":274,"region":245,"industry":18,"wikidata":275},"Bharti Airtel","IN","Q854867",[277],{"name":273,"role":182},"Airtel rolled out India's first network based, AI powered spam detection system for a telecom operator in September 2024, analyzing call and message patterns at the network level to flag spam and fraud in real time for mobile and Wi-Fi broadband subscribers, without an app to install. The service has since grown into a wider Safe Network program that also screens malicious links and warns customers being talked into sharing an OTP. Airtel reported a two year cumulative total of spam calls and messages identified by the system in September 2026.",2026,[281],"api",[],[284],{"kpi":43,"value":285,"unit":190,"qualifier":286,"period":287,"claimant":193,"quote":288,"sourceUrl":289},98400000000,"at-least","cumulative since the system's September 2024 launch, reported September 2026","According to Airtel, its AI-powered network security tools have identified more than 98.4 billion spam calls and 4.4 billion spam messages since the spam detection system was launched.","https:\u002F\u002Fwww.digit.in\u002Fnews\u002Ftelecom\u002Fairtel-blocks-1-62-million-malicious-links-and-flags-98-4-billion-spam-calls-here-is-how-it-works.html",[291,295],{"url":289,"title":292,"publisher":293,"date":294},"Airtel blocks 1.62 million malicious links and flags 98.4 billion spam calls: Here is how it works","Digit","2026-09-30",{"url":296,"title":297,"publisher":273,"date":298},"https:\u002F\u002Fwww.airtel.in\u002Fpress-release\u002F09-2024\u002Fairtel-cracks-down-on-spam-launches-indias-first-ai-powered-network-solution-for-spam-detection","Airtel cracks down on SPAM, launches India's first AI-powered network solution for SPAM detection","2024-09-25",{"level":210,"checkedAt":300},"2026-10-01","C","bharti-airtel-ai-safe-network-spam-detection",0,[305],{"kpi":43,"label":306,"unit":190,"aggregate":177,"higherIsBetter":195,"n":307,"nUpTo":303,"median":308,"min":227,"max":285,"byClaimant":309,"vendorOnly":177,"points":310},"Interactions handled",4,114100000,{"organization":307,"vendor":303,"regulator":303,"independent":303},[311,312,313,314],{"evidenceId":302,"organization":273,"value":285,"qualifier":286,"claimant":193,"grade":301,"pooled":195},{"evidenceId":267,"organization":243,"value":255,"qualifier":191,"claimant":193,"grade":211,"pooled":195},{"evidenceId":212,"organization":176,"value":189,"qualifier":191,"claimant":193,"grade":211,"pooled":195},{"evidenceId":238,"organization":218,"value":227,"qualifier":191,"claimant":193,"grade":211,"pooled":195},{"low":316,"high":317},180000,3600000,[319,335,355,383],{"slug":164,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":324,"patterns":326,"audience":328,"autonomy":28,"adoptionStage":29,"segment":30,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":211,"headline":213,"lastVerified":334,"indexable":195},"AI spam and scam call blocking for mobile and landline subscribers","Spam and scam call blocking","Spam and scam call blocking is AI in the operator's network that protects subscribers from unwanted calls by analyzing incoming calls in real time. It 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.",[18],[20,325],"customer-service",[24,23,327],"prediction-and-scoring","customer-facing",6,[331,273,332,243,333],"Bell Canada","BT Group","Virgin Media O2","2026-09-26",{"slug":165,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":340,"patterns":342,"audience":27,"autonomy":343,"adoptionStage":344,"segment":30,"evidenceCount":307,"publicEvidenceCount":307,"organizations":345,"bestGrade":211,"headline":347,"lastVerified":354,"indexable":195},"AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","Telecom fraud detection is AI that protects an operator's network, revenue and numbers from fraud by watching call, messaging, roaming and account activity. It detects 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],[20,341,21],"network-operations",[24,327,23],"supervised-agent","early-adopters",[243,346],"Vodafone",{"kpi":348,"label":349,"unit":350,"n":351,"nUpTo":303,"kind":352,"value":353,"qualifier":191,"claimant":193,"organization":346,"vendorReported":177},"detection-rate-improvement","Detection improvement","percent",1,"reported",30,"2026-09-27",{"slug":356,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":364,"patterns":366,"audience":368,"autonomy":343,"adoptionStage":369,"evidenceCount":370,"publicEvidenceCount":370,"organizations":371,"bestGrade":301,"headline":377,"lastVerified":382,"indexable":195},"identity-and-access-review-agent","AI agent for identity and access review and certification","Identity and access review agent","An AI agent that runs user access reviews, flags entitlements that differ from peers in similar roles, and leaves only the exceptions for a human reviewer. It pulls entitlement data from every connected application, scores each grant against the peer profile, groups or pre approves low risk, expected access, and keeps an audit ready record of every decision.",[361,362,363],"cross-industry","technology","energy-and-utilities",[21,365],"it-and-engineering",[327,23,24,367],"agentic-workflow","employee-facing","emerging",5,[372,373,374,375,376],"AmeriGas","Hippo Insurance Services","Netskope","SailPoint","Sprout Social",{"kpi":378,"label":379,"unit":350,"n":351,"nUpTo":351,"kind":352,"value":380,"qualifier":191,"claimant":381,"organization":376,"vendorReported":195},"processing-time-reduction","Cycle time reduction",75,"vendor","2026-10-13",{"slug":384,"title":385,"shortTitle":386,"definition":387,"status":9,"industries":388,"functions":390,"patterns":392,"audience":27,"autonomy":343,"adoptionStage":29,"evidenceCount":394,"publicEvidenceCount":394,"organizations":395,"bestGrade":211,"headline":213,"lastVerified":399,"indexable":195},"exam-and-assessment-integrity","AI assisted proctoring and integrity monitoring for remote exams","Exam and assessment integrity","Exam and assessment integrity AI supports the integrity of a remote, high stakes exam by verifying a test taker's identity and watching for signs of cheating. It analyzes behavior such as typing patterns, facial matching and session activity for signs of impersonation or unauthorized help, and flags sessions for a trained human reviewer to decide, rather than letting a model issue an automated finding of misconduct on its own.",[389],"education",[391,21],"operations",[24,393,23],"computer-vision",3,[396,397,398],"Duolingo","Educational Testing Service (ETS)","Pearson VUE","2026-09-29",{"indexable":195,"reasons":401},[],[403,409,414,422,430,437,443,450,457,464,470,476,482,489,496,503,509,515,521,526,533,538,543,550,554,561,566,572,579,584,591,596,602,607,611,617,622,628],{"id":135,"label":404,"issuer":405,"region":142,"url":406,"description":407,"useCases":408,"indexable":195},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",306,{"id":133,"label":410,"issuer":405,"region":142,"url":411,"description":412,"useCases":413,"indexable":195},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",276,{"id":415,"label":416,"issuer":417,"region":418,"url":419,"description":420,"useCases":421,"indexable":195},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",150,{"id":423,"label":424,"issuer":425,"region":426,"url":427,"description":428,"useCases":429,"indexable":195},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":431,"label":432,"issuer":433,"region":142,"url":434,"description":435,"useCases":436,"indexable":195},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",86,{"id":438,"label":439,"issuer":405,"region":142,"url":440,"description":441,"useCases":442,"indexable":195},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":444,"label":445,"issuer":446,"region":142,"url":447,"description":448,"useCases":449,"indexable":195},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":451,"label":452,"issuer":453,"region":245,"url":454,"description":455,"useCases":456,"indexable":195},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":458,"label":459,"issuer":460,"region":245,"url":461,"description":462,"useCases":463,"indexable":195},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":465,"label":466,"issuer":405,"region":142,"url":467,"description":468,"useCases":469,"indexable":195},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":471,"label":472,"issuer":473,"region":418,"url":474,"description":475,"useCases":469,"indexable":195},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":477,"label":478,"issuer":479,"region":426,"url":480,"description":481,"useCases":469,"indexable":195},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":483,"label":484,"issuer":485,"region":426,"url":486,"description":487,"useCases":488,"indexable":195},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",21,{"id":490,"label":491,"issuer":492,"region":142,"url":493,"description":494,"useCases":495,"indexable":195},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":497,"label":498,"issuer":499,"region":426,"url":500,"description":501,"useCases":502,"indexable":195},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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.",17,{"id":504,"label":505,"issuer":405,"region":142,"url":506,"description":507,"useCases":508,"indexable":195},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",15,{"id":510,"label":511,"issuer":512,"region":418,"url":513,"description":514,"useCases":508,"indexable":195},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":516,"label":517,"issuer":405,"region":142,"url":518,"description":519,"useCases":520,"indexable":195},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":132,"label":522,"issuer":523,"region":418,"url":524,"description":525,"useCases":520,"indexable":195},"Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":527,"label":528,"issuer":529,"region":426,"url":530,"description":531,"useCases":532,"indexable":195},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":534,"label":535,"issuer":405,"region":142,"url":536,"description":537,"useCases":532,"indexable":195},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":539,"label":540,"issuer":405,"region":142,"url":541,"description":542,"useCases":532,"indexable":195},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":544,"label":545,"issuer":546,"region":426,"url":547,"description":548,"useCases":549,"indexable":195},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":134,"label":551,"issuer":405,"region":142,"url":552,"description":553,"useCases":549,"indexable":195},"European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":555,"label":556,"issuer":557,"region":142,"url":558,"description":559,"useCases":560,"indexable":195},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":562,"label":563,"issuer":405,"region":142,"url":564,"description":565,"useCases":560,"indexable":195},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":567,"label":568,"issuer":453,"region":245,"url":569,"description":570,"useCases":571,"indexable":195},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":573,"label":574,"issuer":575,"region":426,"url":576,"description":577,"useCases":578,"indexable":195},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":580,"label":581,"issuer":405,"region":142,"url":582,"description":583,"useCases":578,"indexable":195},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":585,"label":586,"issuer":587,"region":588,"url":589,"description":590,"useCases":329,"indexable":195},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":592,"label":593,"issuer":405,"region":142,"url":594,"description":595,"useCases":370,"indexable":195},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":597,"label":598,"issuer":599,"region":142,"url":600,"description":601,"useCases":370,"indexable":195},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":137,"label":603,"issuer":604,"region":245,"url":605,"description":606,"useCases":307,"indexable":195},"Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":136,"label":608,"issuer":453,"region":245,"url":609,"description":610,"useCases":307,"indexable":195},"MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":612,"label":613,"issuer":614,"region":142,"url":615,"description":616,"useCases":307,"indexable":195},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":618,"label":619,"issuer":405,"region":142,"url":620,"description":621,"useCases":394,"indexable":195},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":623,"label":624,"issuer":625,"region":426,"url":626,"description":627,"useCases":394,"indexable":195},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",{"id":629,"label":630,"issuer":631,"region":588,"url":632,"description":633,"useCases":394,"indexable":195},"sdaia-ai-ethics","SDAIA AI ethics principles","Saudi Data and AI Authority","https:\u002F\u002Fsdaia.gov.sa\u002Fen\u002FSDAIA\u002Fabout\u002FDocuments\u002Fai-principles.pdf","Saudi Arabia's national AI ethics principles, applied across sectors including finance and government.",1794496886379]