[{"data":1,"prerenderedAt":613},["ShallowReactive",2],{"uc-telecom-fraud-detection":3,"uc-regulations":408},{"useCase":4,"evidence":200,"blitsAiDeployments":297,"benchmarks":298,"indicative":305,"related":308,"indexability":406,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":27,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":47,"valueDrivers":48,"kpis":53,"indicativeValue":57,"macroEstimates":84,"feasibility":85,"implementation":99,"risk":140,"blitsAi":177,"faq":179,"related":189,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","AI telecom fraud detection: SIM swap and IRSF","Operators use AI on network data to stop SIM swap, IRSF and Wangiri fraud and share risk signals with banks, as Telstra and CommBank do with Fraud Indicator.","published","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.",[12,13,14,15,16],"telco fraud management","SIM swap fraud detection","IRSF detection","Wangiri fraud prevention","network fraud signals for banks",[18],"telecommunications",[20,21,22],"fraud-prevention","network-operations","security-operations",[24,25,26],"anomaly-detection","prediction-and-scoring","classification-and-routing",[28,29,30],"api","voice","sms","back-office","supervised-agent","early-adopters","customer-protection","Telecom fraud hits operators and their customers at once. In a SIM swap or port out attack, a\ncriminal takes over a victim's phone number and with it the one time passcodes that protect bank\naccounts and crypto wallets. In international revenue share fraud, criminals push calls to premium\nnumbers they control, often held in another country, through compromised SIM accounts or hacked\ncompany phone systems (PBXs), and take a share of what the victim is billed. In Wangiri scams, a\ncall rings once so the victim calls back to an expensive premium number.\n\nThese patterns move fast and change constantly. Rules written for last month's attack miss this\nmonth's, and blocking too aggressively cuts off genuine calls and customers. Telstra points out\nthat fraudsters who get enough personal information can persuade customers to give up their one\ntime codes and then access bank and superannuation accounts and investment or crypto wallets. The operator holds the\nsignals that reveal these attacks, such as a recent SIM change, an unusual calling pattern or a\nnumber that behaves like a fraud line, but they are only useful if they are scored in real time and\nshared safely.",[37,42],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"Telstra cites the Australian Institute of Criminology's estimate that the annual impact of identity crime in Australia exceeds AUD 3.1 billion.","What are SIM swaps and porting fraud, and how are we working to stop it?","https://www.telstra.com.au/exchange/what-are-sim-swaps-and-porting-fraud--and-how-are-we-working-to-",2022,{"statement":43,"sourceTitle":44,"sourceUrl":45,"year":46},"Vodafone reports that more than GBP 485 million was lost to authorised push payment fraud in the UK in 2022.","Vodafone Business launches scam signal to defend against impersonation fraud","https://www.vodafone.com/news/newsroom/technology/vodafone-business-launches-scam-signal-to-defend-against-impersonation-fraud",2024,"1. **Stream the signals.** Call detail records, signalling, SIM and port events, roaming records,\n   account changes and customer reports flow into a real time scoring layer.\n2. **Score behaviour, not just lists.** Models learn normal calling and account behaviour and flag\n   deviations: a burst of short international calls to a high cost range, a new SIM followed by\n   logins elsewhere, a number whose call pattern matches known Wangiri campaigns.\n3. **Act by risk.** High confidence fraud traffic is blocked at the network; account takeovers\n   trigger step up checks; uncertain cases go to fraud analysts with the evidence.\n4. **Share risk signals.** Through standard network APIs, such as SIM Swap and Number Verify,\n   banks and online services can ask whether a number was recently swapped or behaves unusually,\n   and use the answer in their own risk decisions.\n5. **Learn from outcomes.** Analyst decisions, customer reports and chargebacks feed back into the\n   models, so detection keeps up as fraudsters change tactics.",[49,50,51,52],"risk-reduction","customer-experience","cost-to-serve","revenue-growth",[54,55,56],"detection-rate-improvement","fraud-loss-reduction","false-positive-reduction",{"referenceOrg":58,"inputs":59,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A mobile operator with 10 million subscribers",[60,65,72],{"key":61,"label":62,"low":63,"high":63,"unit":61,"note":64},"subscribers","Mobile subscribers",10000000,"The reference operator.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"fraudCostPerSubscriber","Fraud losses and write offs per subscriber per year (IRSF, subscription and SIM related fraud)",0.5,2,"USD per subscriber per year","Editorial assumption. Replace with your own fraud loss reporting.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"reduction","Share of fraud losses prevented by AI detection",0.15,0.3,"fraction of fraud losses","Editorial assumption, replace with your own. No source on this page measures prevented operator fraud losses; the only measured result is Vodafone's 30% improvement in bank scam detection in a UK pilot, which concerns authorised push payment scams rather than IRSF, Wangiri or subscription fraud.","subscribers * fraudCostPerSubscriber * reduction","USD","per year","Operator fraud losses avoided","Operator losses only. It leaves out losses avoided by customers and banks, revenue from selling fraud signals through network APIs, analyst time saved, and the cost of false blocks.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"high","Real time scoring on network events at operator scale is demanding, fraud patterns shift quickly, and sharing signals with banks brings privacy, consent and contractual work.",[89,90,91,92],"Call detail records and signalling data in near real time","SIM change, port out and account change events with timestamps","Labelled fraud cases from the fraud team and customer reports","Number ranges and destinations known for high cost or fraud use",[94,95,96,97,98],"Network switching and signalling platforms for blocking","Fraud management system and case tools","Customer account and SIM management systems","Network API gateway for SIM Swap, Number Verify and similar services","Customer reporting channels such as short codes for spam and scam reports",{"steps":100,"guardrails":116,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":127},[101,104,107,110,113],{"title":102,"detail":103},"Map the fraud types and their cost","Quantify losses per fraud type (IRSF, Wangiri, subscription fraud, SIM swap) and decide which ones justify real time detection first.",{"title":105,"detail":106},"Get the events in real time","Attacks on hacked phone systems are often run outside office hours so they last longer, and a daily batch finds them after the bill has grown. Stream call records and SIM events instead.",{"title":108,"detail":109},"Combine rules and models","Keep proven rules for known patterns and add models that catch unusual behaviour, with every model alert reviewed by analysts until precision is proven.",{"title":111,"detail":112},"Tune the blocking threshold on genuine traffic","Measure how many genuine calls and customers each threshold would block before switching it on, and give customers a quick way to report wrong blocks.",{"title":114,"detail":115},"Offer signals to partners carefully","Expose SIM swap and verification signals through standard APIs with contracts, consent and purpose limits, starting with banks.",[117,118,119,120],"Blocking only above a validated confidence threshold, with a fast route to unblock genuine customers","Signals shared with partners limited to risk indicators, never raw call or location records","Consent and purpose limitation for every partner use of network data","Analyst review of account level actions such as suspending a SIM","Fraud analysts review model alerts that lead to account actions, set blocking thresholds and approve new rules. Customer service can reverse a block after identity checks, and every reversal is fed back to the models.",[123,124,125,126],"Fraud losses per fraud type, normalised for traffic","Detection rate and time to detect for confirmed fraud cases","False positive rate, including genuine calls blocked and customers wrongly flagged","Partner outcomes from shared signals, such as scams stopped by banks",[128,131,134,137],{"title":129,"detail":130},"Blocking genuine customers","An aggressive threshold cuts off legitimate international callers or new SIM users. Measure impact on genuine traffic before and after.",{"title":132,"detail":133},"Fraudsters adapt faster than rules","Static rules catch last month's pattern only. Retrain often and watch for sudden drops in alerts.",{"title":135,"detail":136},"Signals without context","A bank treats a recent SIM swap as proof of fraud and locks out a customer who just replaced a phone. Share signals as risk inputs, not verdicts.",{"title":138,"detail":139},"Privacy overreach","Partners ask for more network data than they need. Limit sharing to purpose bound risk indicators.",{"euAiAct":141,"regulations":144,"guidance":153,"controls":171,"incidents":176},{"tier":142,"basis":143},"context-dependent","Fraud detection is not listed as high risk in Annex III, and point 5(b) explicitly excludes systems used to detect financial fraud from the creditworthiness category. Blocking fraud traffic is not normally a safety component of critical digital infrastructure (point 2). The tier can change if the same scores are reused for an Annex III purpose: eligibility for essential public assistance benefits and services (point 5(a)), creditworthiness or credit scoring of natural persons (point 5(b)), or risk assessment and pricing for life and health insurance (point 5(c)). A voice or chat agent that takes fraud reports from customers also carries the Article 50(1) duty to tell people they are dealing with an AI system.",[145,146,147,148,149,150,151,152],"gdpr","uk-gdpr","eu-ai-act","telecom-consumer-rules","nist-ai-rmf","eecc","nis2","au-scams-prevention-framework",[154,160,166],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"Cyber Telecom Crime Report 2019","Europol European Cybercrime Centre and Trend Micro Research","europe","https://www.europol.europa.eu/cms/sites/default/files/documents/cyber-telecom_crime_report_2019_public.pdf","Threat models for telecom fraud, including international revenue share fraud through hacked PBXs and SIM accounts, and Wangiri callback fraud to premium numbers.",{"title":161,"issuer":162,"region":163,"url":164,"note":165},"CAMARA SIM Swap API","CAMARA project (Linux Foundation)","global","https://camaraproject.org/sim-swap/","Open API standard that lets banks and online services check whether a SIM was recently changed, used by operators including Vodafone.",{"title":167,"issuer":168,"region":157,"url":169,"note":170},"APP scams","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","UK reimbursement rules for authorised push payment scams, split between sending and receiving firms; Vodafone cites this reimbursement duty as a reason banks are turning to network based APIs.",[172,173,174,175],"Data protection impact assessment for fraud scoring and signal sharing","Documented thresholds and rules with change control","Monitoring of false positives and customer complaints about blocking","Contracts and technical limits on partner use of network risk signals",[],{"howToBuild":178},"Real time scoring and blocking run in the network and the fraud management system. Blits.ai adds\nthe investigation and customer layers: an **AI agent** with a **SQL knowledge base** over alerts\nand cases helps fraud analysts summarise a case and find related numbers, and a **knowledge base**\nholds fraud playbooks. **Agentic workflows** call **custom functions** to suspend a SIM or reverse\na block, always with **human in the loop approval**.\n\nFor customers, a **voice or chat agent** can handle fraud reports and wrongly blocked numbers,\nverify identity through an authentication step in a **flow**, and hand over to the fraud team with\nthe case summary. **PII masking** keeps phone numbers and identity data out of model prompts, and\nthe per tenant user audit log and per task agentic audit trails record who did what.",[180,183,186],{"question":181,"answer":182},"How do operators help banks stop SIM swap fraud?","By sharing a risk signal rather than data. In 2022 Telstra said it would give banks, on request, a rating on a risk scale that shows whether a mobile service used for identity has had a recent SIM swap or port out, so the bank can ask for more information before a transfer goes ahead.",{"question":184,"answer":185},"Does network data really improve fraud detection?","Vodafone reports that scam detection improved by 30% after three months of piloting its Scam Signal service with a UK bank; that service targets authorised push payment scams, and Vodafone does not say whether it uses AI. Telstra says the Scam and Fraud Indicator, built by Quantium Telstra with CommBank, uses AI; Fraud Indicator shares intelligence about unusual mobile usage to help detect fraudulently opened accounts, and the gain of more than 25 per cent announced at launch in 2025 was an expectation, not a measured result.",{"question":187,"answer":188},"What is Wangiri fraud and can it be blocked?","Wangiri calls ring once from an international number so the victim calls back to a costly premium number. Operators block known patterns in the network; Telstra described improving its Wangiri blocking in 2021, when its platform blocked around 13 million suspected scam calls a month.",[190,191,192,193,194],"spam-and-scam-call-blocking","scam-payment-interception","application-and-identity-fraud-detection","order-to-activation-and-esim-onboarding-assistant","mule-network-detection","2026-09-27",[197],{"date":195,"note":198},"First published","telecom-fraud-detection",[201,235,259,274],{"title":202,"useCases":203,"organization":204,"vendors":209,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":220,"sources":221,"verification":230,"grade":232,"id":233,"organizationSlug":234},"Telstra and CommBank: Scam Indicator and Fraud Indicator built on mobile network intelligence",[199,192],{"name":205,"anonymized":206,"country":207,"region":208,"industry":18},"Telstra",false,"AU","asia-pacific",[210],{"name":211,"role":212},"Quantium Telstra","in-house","Quantium Telstra built two services in collaboration with Commonwealth Bank. Scam Indicator detects and intercepts suspected scam calls to bank customers in real time and was later extended to landlines. Fraud Indicator, live from early 2025, securely shares intelligence about unusual mobile service usage so the bank can spot fraudsters opening accounts with a phone number they control. Telstra describes the Scam and Fraud Indicator as using AI and says it has safeguarded thousands of customers and prevented millions of dollars in fraud since 2023; the expected gain in detection of fraudulent accounts was published as a forecast.","production",2025,[28],[218],"en",[],true,[222,226],{"url":223,"title":224,"publisher":205,"date":225},"https://www.telstra.com.au/exchange/telstra-and-commbank-expand-collaboration-to-increase-fraud-dete","Telstra and CommBank expand collaboration to increase fraud detection rates","2025-02-10",{"url":227,"title":228,"publisher":205,"date":229},"https://www.telstra.com.au/exchange/telstra-s-ai-transformation--strategy--partnerships-and-real-wor","Telstra's AI transformation: strategy, partnerships and real-world results","2026-04-20",{"level":231,"checkedAt":195},"source-verified","B","telstra-quantium-fraud-indicator","telstra",{"title":236,"useCases":237,"organization":238,"vendors":241,"summary":242,"stage":214,"year":46,"channels":243,"languages":244,"metrics":245,"outcomeDisclosed":220,"sources":253,"verification":256,"grade":232,"id":257,"organizationSlug":258},"Vodafone: Scam Signal network data service against impersonation fraud",[199,191],{"name":239,"anonymized":206,"country":240,"region":157,"industry":18},"Vodafone","GB",[],"Vodafone Carrier Services launched Scam Signal, an API that analyses real time network data during a live bank transaction to detect social engineering behind authorised push payment fraud, so banks can stop fraudulent transfers as they happen. It sits in Vodafone's Identity Hub next to the SIM Swap and Number Verify APIs, which use CAMARA open standards. JT Group, working with FICO, was the first channel partner to offer it. In a three month pilot with a UK bank that Vodafone does not name, scam detection improved by 30%.",[28],[],[246],{"kpi":54,"value":247,"unit":248,"qualifier":249,"period":250,"claimant":251,"quote":252,"sourceUrl":45},30,"percent","exact","three month pilot with a UK bank","organization","Scam detection using this service improved by 30% after only three months of a successful pilot with a leading UK bank.",[254],{"url":45,"title":44,"publisher":239,"date":255},"2024-04-23",{"level":231,"checkedAt":195},"vodafone-scam-signal","vodafone",{"title":260,"useCases":261,"organization":262,"vendors":263,"summary":264,"stage":265,"year":41,"channels":266,"languages":267,"metrics":268,"outcomeDisclosed":206,"sources":269,"verification":272,"grade":232,"id":273,"organizationSlug":234},"Telstra: SIM swap and port out risk ratings for banks",[199],{"name":205,"anonymized":206,"country":207,"region":208,"industry":18},[],"In January 2022 Telstra said it had started working with organisations in the banking industry and would provide a risk rating, a number on a risk scale, when a banking organisation asks whether a mobile service used as a form of identity has had a recent SIM swap or port out. Banks and credit unions would request it when a customer makes a transfer, especially to a new recipient, and use it to ask for more information rather than to block the customer automatically. Telstra said it was considering applying the technology in retail, insurance, transport and logistics, social networking and online gaming. No results are published, and no later Telstra source cited here confirms how widely the rating is used today.","announced",[28],[218],[],[270],{"url":40,"title":39,"publisher":205,"date":271},"2022-01-13",{"level":231,"checkedAt":195},"telstra-sim-swap-and-porting-risk-signal",{"title":275,"useCases":276,"organization":277,"vendors":278,"summary":279,"stage":280,"year":281,"channels":282,"languages":283,"metrics":284,"outcomeDisclosed":220,"sources":285,"verification":294,"grade":232,"id":296,"organizationSlug":234},"Telstra: network level blocking of scam, spoofed and Wangiri calls",[190,199],{"name":205,"anonymized":206,"country":207,"region":208,"industry":18},[],"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.","scaled",2021,[29],[218],[],[286,290],{"url":287,"title":288,"publisher":205,"date":289},"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":291,"title":292,"publisher":205,"date":293},"https://www.telstra.com.au/exchange/suspicious-phone-calls--what-telstra-is-doing-to-raise-the-alarm","Suspicious phone calls: what Telstra is doing to raise the alarm","2025-03-13",{"level":231,"checkedAt":295},"2026-09-26","telstra-scam-call-blocking",0,[299],{"kpi":54,"label":300,"unit":248,"aggregate":220,"higherIsBetter":220,"n":301,"nUpTo":297,"median":247,"min":247,"max":247,"byClaimant":302,"vendorOnly":206,"points":303},"Detection improvement",1,{"organization":301,"vendor":297,"regulator":297,"independent":297},[304],{"evidenceId":257,"organization":239,"value":247,"qualifier":249,"claimant":251,"grade":232,"pooled":220},{"low":306,"high":307},750000,6000000,[309,326,350,372,391],{"slug":190,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":314,"patterns":316,"audience":317,"autonomy":318,"adoptionStage":319,"segment":34,"evidenceCount":320,"publicEvidenceCount":320,"organizations":321,"bestGrade":232,"headline":325,"lastVerified":295,"indexable":220},"AI spam and scam call blocking for mobile and landline subscribers","Spam and scam call blocking","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.",[18],[20,315],"customer-service",[24,26,25],"customer-facing","autonomous","mainstream",5,[322,323,205,324],"Bell Canada","BT Group","Virgin Media O2",null,{"slug":191,"title":327,"shortTitle":328,"definition":329,"status":9,"industries":330,"functions":333,"patterns":334,"audience":317,"autonomy":32,"adoptionStage":33,"segment":338,"evidenceCount":339,"publicEvidenceCount":339,"organizations":340,"bestGrade":232,"headline":346,"lastVerified":295,"indexable":220},"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.",[331,332],"banking","payments",[20,315],[335,25,336,337],"conversational-agent","agentic-workflow","voice-agent","front-office",6,[341,342,343,344,239,345],"Commonwealth Bank of Australia","Mastercard","Revolut","Starling Bank","Westpac",{"kpi":54,"label":300,"unit":248,"n":69,"nUpTo":297,"kind":347,"value":348,"qualifier":249,"claimant":349,"organization":344,"vendorReported":220},"reported",300,"vendor",{"slug":192,"title":351,"shortTitle":352,"definition":353,"status":9,"industries":354,"functions":357,"patterns":360,"audience":31,"autonomy":32,"adoptionStage":33,"segment":338,"evidenceCount":339,"publicEvidenceCount":339,"organizations":363,"bestGrade":232,"headline":369,"lastVerified":295,"indexable":220},"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.",[331,332,355,356,18],"cross-industry","government",[20,358,359],"onboarding-and-kyc","lending-and-credit",[361,24,362,25],"document-processing","computer-vision",[364,365,366,367,368,205],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer",{"kpi":54,"label":300,"unit":370,"n":301,"nUpTo":297,"kind":347,"value":371,"qualifier":249,"claimant":251,"organization":367,"vendorReported":206},"multiplier",2.5,{"slug":193,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":377,"patterns":380,"audience":317,"autonomy":32,"adoptionStage":381,"segment":338,"evidenceCount":382,"publicEvidenceCount":382,"organizations":383,"bestGrade":232,"headline":387,"lastVerified":295,"indexable":220},"AI assistant for telecom order to activation and eSIM onboarding","Order to activation and eSIM onboarding","An AI assistant that takes a new or existing customer from order to a working service: it collects and checks the order details, guides number porting, eSIM download or SIM activation and installation appointments, tracks the order and fixes or escalates the step that is stuck, on messaging, app, web or phone.",[18],[378,358,315,379],"sales","operations",[335,336,26,361],"emerging",3,[384,385,386],"Reliance Jio","Singtel","Verizon",{"kpi":388,"label":389,"unit":248,"n":301,"nUpTo":297,"kind":347,"value":390,"qualifier":249,"claimant":251,"organization":385,"vendorReported":206},"automation-rate","Automation rate",76,{"slug":194,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":396,"patterns":398,"audience":31,"autonomy":400,"adoptionStage":33,"segment":401,"evidenceCount":382,"publicEvidenceCount":382,"organizations":402,"bestGrade":232,"headline":325,"lastVerified":195,"indexable":220},"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.",[331,332],[20,397],"financial-crime-compliance",[24,25,336,399],"summarization","copilot","middle-office",[403,404,405],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)",{"indexable":220,"reasons":407},[],[409,415,420,427,434,440,446,453,460,467,474,480,487,494,500,504,511,517,523,528,533,539,545,550,555,562,569,574,579,586,593,597,602,607],{"id":147,"label":410,"issuer":411,"region":157,"url":412,"description":413,"useCases":414,"indexable":220},"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":145,"label":416,"issuer":411,"region":157,"url":417,"description":418,"useCases":419,"indexable":220},"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":421,"label":422,"issuer":423,"region":163,"url":424,"description":425,"useCases":426,"indexable":220},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":149,"label":428,"issuer":429,"region":430,"url":431,"description":432,"useCases":433,"indexable":220},"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":435,"label":436,"issuer":411,"region":157,"url":437,"description":438,"useCases":439,"indexable":220},"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":146,"label":441,"issuer":442,"region":157,"url":443,"description":444,"useCases":445,"indexable":220},"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":447,"label":448,"issuer":449,"region":157,"url":450,"description":451,"useCases":452,"indexable":220},"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":454,"label":455,"issuer":456,"region":208,"url":457,"description":458,"useCases":459,"indexable":220},"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":461,"label":462,"issuer":463,"region":208,"url":464,"description":465,"useCases":466,"indexable":220},"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":468,"label":469,"issuer":470,"region":163,"url":471,"description":472,"useCases":473,"indexable":220},"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":475,"label":476,"issuer":477,"region":430,"url":478,"description":479,"useCases":473,"indexable":220},"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":481,"label":482,"issuer":483,"region":157,"url":484,"description":485,"useCases":486,"indexable":220},"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":488,"label":489,"issuer":490,"region":163,"url":491,"description":492,"useCases":493,"indexable":220},"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":495,"label":496,"issuer":411,"region":157,"url":497,"description":498,"useCases":499,"indexable":220},"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":151,"label":501,"issuer":411,"region":157,"url":502,"description":503,"useCases":499,"indexable":220},"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":505,"label":506,"issuer":507,"region":430,"url":508,"description":509,"useCases":510,"indexable":220},"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":512,"label":513,"issuer":411,"region":157,"url":514,"description":515,"useCases":516,"indexable":220},"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":518,"label":519,"issuer":520,"region":430,"url":521,"description":522,"useCases":516,"indexable":220},"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":148,"label":524,"issuer":525,"region":163,"url":526,"description":527,"useCases":516,"indexable":220},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":150,"label":529,"issuer":411,"region":157,"url":530,"description":531,"useCases":532,"indexable":220},"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":534,"label":535,"issuer":536,"region":430,"url":537,"description":538,"useCases":532,"indexable":220},"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":540,"label":541,"issuer":456,"region":208,"url":542,"description":543,"useCases":544,"indexable":220},"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":546,"label":547,"issuer":411,"region":157,"url":548,"description":549,"useCases":544,"indexable":220},"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":551,"label":552,"issuer":411,"region":157,"url":553,"description":554,"useCases":544,"indexable":220},"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":556,"label":557,"issuer":558,"region":157,"url":559,"description":560,"useCases":561,"indexable":220},"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":563,"label":564,"issuer":565,"region":430,"url":566,"description":567,"useCases":568,"indexable":220},"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":570,"label":571,"issuer":411,"region":157,"url":572,"description":573,"useCases":568,"indexable":220},"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":575,"label":576,"issuer":411,"region":157,"url":577,"description":578,"useCases":339,"indexable":220},"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":580,"label":581,"issuer":582,"region":583,"url":584,"description":585,"useCases":320,"indexable":220},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":587,"label":588,"issuer":589,"region":157,"url":590,"description":591,"useCases":592,"indexable":220},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":594,"label":595,"issuer":168,"region":157,"url":169,"description":596,"useCases":592,"indexable":220},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":152,"label":598,"issuer":599,"region":208,"url":600,"description":601,"useCases":382,"indexable":220},"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":603,"label":604,"issuer":411,"region":157,"url":605,"description":606,"useCases":382,"indexable":220},"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":608,"label":609,"issuer":610,"region":430,"url":611,"description":612,"useCases":382,"indexable":220},"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.",1790598302107]