[{"data":1,"prerenderedAt":587},["ShallowReactive",2],{"uc-emergency-call-triage-support":3,"uc-regulations":383},{"useCase":4,"evidence":190,"blitsAiDeployments":281,"benchmarks":282,"indicative":283,"related":286,"indexability":381,"includeUnpublished":196},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":51,"macroEstimates":79,"feasibility":80,"implementation":92,"risk":135,"blitsAi":168,"faq":170,"related":180,"datePublished":185,"dateModified":185,"lastVerified":185,"changelog":186,"slug":189},"AI support for emergency call triage (112 and 911)","Emergency call triage support","AI for 911 and 112 emergency call triage","AI helps 911 and 112 call takers with transcripts, translation and alerts. In a Copenhagen trial, alerts did not significantly raise cardiac arrest recognition.","published","AI that supports emergency call takers and dispatchers during 112 and 911 calls, with live transcription, translation, summaries, location cues and alerts for critical conditions such as cardiac arrest, while the call taker keeps every triage and dispatch decision.",[12,13,14,15],"911 call taking AI","112 emergency call AI","dispatcher assist","AI for public safety answering points",[17,18],"government","healthcare",[20,21],"citizen-services","operations",[23,24,25,26],"speech-analytics","translation","summarization","prediction-and-scoring",[28,29],"voice","agent-desktop","employee-facing","assist","early-adopters","Emergency communications centres work under constant volume: Baltimore answers about 1.4 million\n911 calls a year, and at Galt Police Department a single dispatcher is sometimes on duty alone. Call\ntakers must understand panicked, noisy or non native callers, get an address, follow a protocol\nand type everything at once, often while also handling radio. Critical conditions are missed:\nCopenhagen EMS researchers note that dispatchers fail to identify roughly a quarter of out of\nhospital cardiac arrests. Callers who do not speak the local language wait for a telephone\ninterpreter. Quality assurance often covers only a sample of calls (roughly 30% in Baltimore\nbefore automation), with feedback arriving long after the call.\n\nThe stakes are high. A wrong alert, a mistranslation or a missed cue can cost a life, which is\nwhy the EU AI Act lists emergency call classification and dispatch as high risk.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"Copenhagen Emergency Medical Services researchers report that emergency medical dispatchers fail to identify approximately 25% of out of hospital cardiac arrests.","Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial","https://pmc.ncbi.nlm.nih.gov/articles/PMC7788469/",2021,"1. **Transcribe live.** Speech recognition produces a running transcript of the call on the call\n   taker's screen, with key details (address, weapons, symptoms) highlighted.\n2. **Translate.** For callers in another language, the system detects the language and shows a\n   translated transcript, or voices the call taker's questions in the caller's language.\n3. **Flag critical conditions.** A model listens for patterns of time critical conditions, such as\n   cardiac arrest, and alerts the call taker to consider the matching protocol.\n4. **Summarise and document.** At the end of the call, the system drafts a summary for the incident\n   record, which the call taker edits.\n5. **Review quality.** Automated QA checks every call against protocol and flags calls for\n   supervisor review and coaching.\n6. **Keep humans in charge.** Call takers decide the triage category, protocol and dispatch; the\n   AI never dispatches or downgrades a call.",[42,43,44,45],"risk-reduction","speed","inclusion-and-access","employee-productivity",[47,48,49,50],"accuracy","detection-rate-improvement","handling-time-reduction","time-saved-per-task",{"referenceOrg":52,"inputs":53,"formula":74,"currency":75,"period":76,"resultLabel":77,"caveat":78},"An emergency communications centre handling 1 million calls a year",[54,60,67],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"calls","Emergency calls per year",1000000,"calls per year","The reference centre; Baltimore answers about 1.4 million a year.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"minutesSaved","Call taker minutes saved per call on documentation",0.5,1.5,"minutes per call","Editorial assumption for summaries replacing manual narrative entry. No public benchmark states this yet.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"hourlyCost","Fully loaded call taker cost per hour",40,60,"USD per hour","Editorial assumption. Replace with your own cost.","calls * minutesSaved / 60 * hourlyCost","USD","per year","Call taker time released, valued at cost","Values documentation time only. It leaves out the value of faster recognition of critical conditions, interpreter costs avoided, full QA coverage and the cost of the system; released time in understaffed centres usually goes to answering calls faster, not to savings.",[],{"complexity":81,"complexityNote":82,"dataPrerequisites":83,"integrations":87},"high","Integration with call handling and CAD systems, real time latency, noisy audio and many languages, plus the need for clinical and operational validation of every alert, make this a demanding public sector deployment.",[84,85,86],"Recorded calls with outcomes for testing (for example confirmed cardiac arrest)","Current protocols (medical, fire, police) and local address data","Language mix of callers, to prioritise translation",[88,89,90,91],"Call handling system and audio stream","Computer aided dispatch (CAD) for incident records","Location services and maps","QA and training systems",{"steps":93,"guardrails":109,"humanInTheLoop":115,"kpisToInstrument":116,"failureModes":122},[94,97,100,103,106],{"title":95,"detail":96},"Start with transcription and summaries","Live transcripts and draft summaries support every call and change no decision; staff in Baltimore and Galt describe both as practical help with addresses and documentation.",{"title":98,"detail":99},"Add translation with a fallback","Offer machine translation for the most common languages, with a clear way to bring in a human interpreter when the call is complex or the translation is doubtful.",{"title":101,"detail":102},"Treat alerts as clinical interventions","Before switching on alerts for conditions such as cardiac arrest, run a controlled trial on your own calls. Copenhagen's randomized trial showed a model that beat dispatchers on sensitivity did not significantly improve dispatcher recognition.",{"title":104,"detail":105},"Design the alert for the call taker","A low positive predictive value floods call takers with false alarms. Tune thresholds with dispatchers and measure whether alerts are acted on.",{"title":107,"detail":108},"Use automated QA for coaching","Review every call against protocol and feed findings into training. Baltimore moved from a third party reviewing roughly 30% of calls to automated QA on all of them.",[110,111,112,113,114],"The AI never dispatches, downgrades or closes a call; call takers decide","Alerts are advisory, logged and evaluated against confirmed outcomes","Human interpreter always available as a fallback to machine translation","Transcripts and summaries edited and approved by the call taker before they enter the record","Fail safe design, so an outage of the AI never blocks call handling","Call takers and dispatchers make every triage and dispatch decision. Medical directors approve any clinical alert and its thresholds; supervisors review AI assisted QA findings before they reach staff files; every alert and translation is auditable against the call recording.",[117,118,119,120,121],"Recognition rate of target conditions with and without alerts, on confirmed outcomes","Alert positive predictive value and the share of alerts acted on","Time to address confirmation and to dispatch","Transcription and translation accuracy on a sampled set of calls","QA coverage and protocol compliance scores",[123,126,129,132],{"title":124,"detail":125},"Better model, same outcome","In Copenhagen the model had higher sensitivity for cardiac arrest than dispatchers (85.0% against 77.5%), but alerting dispatchers did not significantly improve their recognition. Measure the human outcome, not the model.",{"title":127,"detail":128},"Alert fatigue","Alerts with low positive predictive value (17.8% in the Copenhagen trial, against 55.8% for dispatchers) are easy to discount. Tune thresholds and track actions on alerts.",{"title":130,"detail":131},"Mistranslation under pressure","A wrong word in a translated address or symptom can send help to the wrong place. Show the original and translation, confirm critical details and keep interpreters available.",{"title":133,"detail":134},"Dependence during outages","Staff who rely on AI transcripts can lose practice in manual call taking. Keep manual procedures practised and the system fail safe.",{"euAiAct":136,"regulations":138,"guidance":144,"controls":161,"incidents":167},{"tier":81,"basis":137},"Annex III point 5(d): AI systems intended to evaluate and classify emergency calls or to dispatch or set priority for emergency first response services (police, fire, medical aid) are high risk. Pure transcription that performs a narrow procedural or preparatory task may fall outside it under the Article 6(3) exceptions, but alerts that influence triage are in scope. An AI agent that speaks with callers directly, for example on a non emergency line, must also tell them they are interacting with AI (Article 50).",[139,140,141,142,143],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr",[145,151,155],{"title":146,"issuer":147,"region":148,"url":149,"note":150},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(d) lists emergency call classification and dispatch as high risk.",{"title":152,"issuer":147,"region":148,"url":153,"note":154},"Article 27, fundamental rights impact assessment for high risk AI systems","https://artificialintelligenceact.eu/article/27/","Deployers that are bodies governed by public law, or private entities providing public services, must assess the impact on fundamental rights before first using a high risk system such as one covered by point 5(d).",{"title":156,"issuer":157,"region":158,"url":159,"note":160},"NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","A framework for mapping, measuring and managing risk that US public safety agencies can apply to call taking AI.",[162,163,164,165,166],"Controlled evaluation or trial on local calls before any alert goes live","Conformity assessment, risk management and logging as required for high risk AI in the EU","Continuous monitoring of alert performance against confirmed outcomes","Documented fallback procedures and regular drills without the AI","Clear records of which AI outputs the call taker saw on each call",[],{"howToBuild":169},"Blits.ai is not a computer aided dispatch system and should not sit in the 911 or 112 decision\npath. Where it fits is around it: its **self hosted transcription and speaker diarization**\n(WhisperX on GPU, processed on Blits.ai infrastructure) can transcribe recorded calls for\nquality review and training, and an **agent** grounded in the centre's protocols through the\n**knowledge base** can draft protocol checks that supervisors review.\n\nOn the **non emergency line** next to the emergency number, a **voice agent** on telephony with\nreal time streaming speech recognition can answer routine calls and **redirect the call** to the\ndispatcher when emergency language is detected, with **test suites** to check that\nbehaviour before go live. **Language detection and translation**, **PII masking**\nand **EU and UAE data residency** support multilingual and sovereign deployments; anything that\nclassifies emergency calls should be treated as high risk and validated with the medical director.",[171,174,177],{"question":172,"answer":173},"Does AI improve emergency call triage?","The best public evidence is mixed. In Copenhagen's randomized trial on 112 calls, a model flagged cardiac arrest with higher sensitivity than dispatchers (85.0% against 77.5%), but dispatchers who received alerts did not recognize significantly more cases. For transcription, translation, summaries and automated QA, the evidence on this page comes from vendor case studies from US centres such as Baltimore, which report practical benefits but no controlled comparison.",{"question":175,"answer":176},"Is emergency call AI high risk under the EU AI Act?","Yes, when it evaluates or classifies emergency calls or sets dispatch priority (Annex III point 5(d)). That brings risk management, logging, human oversight and conformity requirements, and public bodies must also carry out a fundamental rights impact assessment (Article 27).",{"question":178,"answer":179},"Where do centres start?","With live transcripts, summaries, translation and automated QA, as Baltimore did, and with AI on the non emergency line, as Galt Police Department did to keep routine calls away from dispatchers.",[181,182,183,184],"non-emergency-service-request-routing","public-service-translation","live-agent-assist","citizen-information-assistant","2026-09-27",[187],{"date":185,"note":188},"First published","emergency-call-triage-support",[191,223,256],{"title":192,"useCases":193,"organization":194,"vendors":198,"summary":199,"stage":200,"year":201,"channels":202,"languages":203,"metrics":205,"outcomeDisclosed":206,"sources":207,"verification":218,"grade":220,"id":221,"organizationSlug":222},"Copenhagen Emergency Medical Services: randomized trial of machine learning alerts for cardiac arrest on 112 calls",[189],{"name":195,"anonymized":196,"country":197,"region":148,"industry":17},"Copenhagen Emergency Medical Services",false,"DK",[],"From September 2018 to December 2019, a machine learning model listened to 112 emergency calls at Copenhagen EMS through speech recognition and flagged suspected out of hospital cardiac arrest. Because of downtime, it processed 169,049 of the 226,130 calls the service received (74.7%). It flagged 5,847 calls as suspected cardiac arrest, and the 5,242 eligible calls were randomized: dispatchers in one group saw an alert, the other group worked as usual. The model alone had higher sensitivity than dispatchers without alerts (85.0% against 77.5%) but a much lower positive predictive value (17.8% against 55.8%), and dispatchers with alerts did not recognize significantly more confirmed arrests (93.1% against 90.5%, P = .15). It shows that a model with higher sensitivity did not, in this trial, lead to better dispatcher recognition.","pilot",2018,[28,29],[204],"da",[],true,[208,211,214],{"url":209,"title":37,"publisher":210},"https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2774598","JAMA Network Open",{"url":38,"title":212,"publisher":213},"Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial (open access full text)","PubMed Central",{"url":215,"title":216,"publisher":217},"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:33404620%20AND%20SRC:MED&resultType=core&format=xml","Abstract record of the trial (PMID 33404620)","Europe PMC",{"level":219,"checkedAt":185},"source-verified","B","copenhagen-ems-cardiac-arrest-recognition-trial",null,{"title":224,"useCases":225,"organization":226,"vendors":229,"summary":233,"stage":234,"year":235,"channels":236,"languages":237,"metrics":239,"outcomeDisclosed":206,"sources":249,"verification":252,"grade":254,"id":255,"organizationSlug":222},"Galt Police Department: AI triage of non emergency calls and assistive 911 call taking",[181,189],{"name":227,"anonymized":196,"country":228,"region":158,"industry":17},"Galt Police Department","US",[230],{"name":231,"role":232},"Prepared","platform","Galt Police Department serves 26,000 residents with eight dispatch staff, who handle nearly 30,000 calls a year, more than 73% of them non emergency. Since 2024 an AI agent from Prepared answers the ten digit non emergency line, works out what the caller needs, resolves it or routes it to the right resource, and transfers any genuine emergency immediately. On 911 calls, dispatchers get a live transcript, an AI summary and key details highlighted as the call happens. During a shooting in Galt, the agent handled the incoming non emergency calls in the background while dispatchers managed the response.","production",2024,[28,29],[238],"en",[240],{"kpi":241,"value":242,"unit":243,"qualifier":244,"period":245,"claimant":246,"quote":247,"sourceUrl":248},"contact-deflection",73,"percent","exact","share of call volume handled before reaching a dispatcher","vendor","With 73% of call volume now handled before it reaches a dispatcher's headset, the calls that do come through are the ones that genuinely need a human.","https://www.prepared911.com/case-studies/galt-pd-assistive-ai",[250],{"url":248,"title":251,"publisher":231},"Galt PD: Making the Unmanageable Manageable with Assistive AI",{"level":219,"checkedAt":253},"2026-09-26","C","galt-police-department-non-emergency-call-triage",{"title":257,"useCases":258,"organization":259,"vendors":261,"summary":263,"stage":264,"year":265,"channels":266,"languages":267,"metrics":269,"outcomeDisclosed":206,"sources":276,"verification":279,"grade":254,"id":280,"organizationSlug":222},"Baltimore City 911: live transcripts, AI summaries, translation and automated QA",[189,182],{"name":260,"anonymized":196,"country":228,"region":158,"industry":17},"Baltimore City 911 (Emergency Communications)",[262],{"name":231,"role":232},"Baltimore's emergency communications centre answers about 1.4 million 911 calls a year. Since partnering with Prepared in early 2022, call takers see a live transcript, AI summaries and highlighted key details on every call, which helps with addresses and callers who are hard to understand. Non English calls are transcribed and translated in real time, and for Spanish calls operators can dial in an automated voice translator instead of a third party interpreter. Automated QA now reviews every call, where a contractor previously reviewed roughly 30%. The case study also carries the unattributed line that the tool is \"about 98% accurate\", without saying what was measured or how, so it is not recorded as an accuracy figure.","scaled",2022,[28,29],[238,268],"es",[270],{"kpi":271,"value":272,"unit":243,"qualifier":244,"period":273,"claimant":246,"quote":274,"sourceUrl":275},"quality-score-uplift",12,"QA scores since automated QA was deployed; the QA method changed at the same time (sampling of about 30% of calls replaced by automated review of all calls), so before and after scores may not be comparable","Since deploying Automated QA, Baltimore has seen a 12% improvement in QA scores.","https://www.prepared911.com/case-studies/baltimore-911-call-processing-assistive-ai",[277],{"url":275,"title":278,"publisher":231},"Baltimore 911: Improving call processing efficiency with Assistive AI",{"level":219,"checkedAt":185},"baltimore-911-assistive-call-taking",0,[],{"low":284,"high":285},333333.3333333334,1500000,[287,315,332,360],{"slug":181,"title":288,"shortTitle":289,"definition":290,"status":9,"industries":291,"functions":292,"patterns":295,"audience":300,"autonomy":301,"adoptionStage":32,"evidenceCount":302,"publicEvidenceCount":302,"organizations":303,"bestGrade":220,"headline":310,"lastVerified":253,"indexable":206},"AI for non emergency service requests and 311 routing","Non emergency service request routing","An AI agent on a city's 311 style phone, chat and messaging channels that answers routine municipal questions, takes service requests such as potholes, missed collections or broken street lights with the right location and details, creates the case in the work order system and routes anything urgent or complex to the right team.",[17],[20,293,294],"customer-service","case-management",[296,297,298,299],"conversational-agent","voice-agent","classification-and-routing","agentic-workflow","customer-facing","supervised-agent",7,[304,305,306,227,307,308,309],"Abu Dhabi Government (TAMM)","London Borough of Barnet","City of Kelowna","Montgomery County Government","Newcastle City Council","Rio de Janeiro City Data Office (Escritório de Dados)",{"kpi":47,"label":311,"unit":243,"n":312,"nUpTo":281,"kind":313,"value":314,"qualifier":244,"claimant":246,"organization":306,"vendorReported":206},"Accuracy",1,"reported",80,{"slug":182,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":320,"patterns":321,"audience":300,"autonomy":323,"adoptionStage":32,"evidenceCount":324,"publicEvidenceCount":324,"organizations":325,"bestGrade":220,"headline":222,"lastVerified":253,"indexable":206},"AI translation and interpretation for multilingual public services","Public service translation","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[17],[20,293,21],[24,296,23,322],"document-processing","copilot",8,[260,326,327,328,329,330,307,331],"Delaware County","European Commission","Federal Emergency Management Agency","Internal Revenue Service","Madrid Destino","U.S. Department of State (Bureau of Consular Affairs)",{"slug":183,"title":333,"shortTitle":334,"definition":335,"status":9,"industries":336,"functions":343,"patterns":344,"audience":30,"autonomy":31,"adoptionStage":347,"evidenceCount":302,"publicEvidenceCount":348,"organizations":349,"bestGrade":220,"headline":355,"lastVerified":185,"indexable":206},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[337,338,339,340,18,341,342],"cross-industry","banking","insurance","telecommunications","retail-and-ecommerce","technology",[293,21],[23,345,25,346],"rag-knowledge-assistant","content-generation","mainstream",5,[350,351,352,353,354],"DBS Bank","Definity","Oportun","SEB","SIGNAL IDUNA",{"kpi":356,"label":357,"unit":243,"n":358,"nUpTo":281,"kind":313,"value":359,"qualifier":244,"claimant":246,"organization":351,"vendorReported":206},"productivity-gain","Productivity gain",2,15,{"slug":184,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":365,"patterns":367,"audience":300,"autonomy":301,"adoptionStage":347,"evidenceCount":368,"publicEvidenceCount":369,"organizations":370,"bestGrade":220,"headline":377,"lastVerified":185,"indexable":206},"AI assistant for citizen information and government services","Citizen information assistant","An AI assistant that answers residents' and businesses' questions about government services in plain language, grounded only in official guidance with links to the source, points them to the right online service or office, and hands anything personal, urgent or outside its content to a human with the context attached.",[17],[20,293,366],"knowledge-management",[345,296,297,298],11,9,[304,371,372,373,374,375,376,330,307],"Driver and Vehicle Licensing Agency","Estonian Information System Authority (RIA)","Foreign, Commonwealth and Development Office","Gemeente Tilburg","Government Digital Service","Government of the City of Buenos Aires",{"kpi":47,"label":311,"unit":243,"n":312,"nUpTo":281,"kind":313,"value":378,"qualifier":379,"claimant":380,"organization":373,"vendorReported":196},76,"at-least","organization",{"indexable":206,"reasons":382},[],[384,389,394,401,404,410,416,423,431,438,445,451,458,464,470,475,482,487,493,499,504,510,516,521,526,532,538,543,549,556,563,569,576,581],{"id":139,"label":385,"issuer":147,"region":148,"url":386,"description":387,"useCases":388,"indexable":206},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":140,"label":390,"issuer":147,"region":148,"url":391,"description":392,"useCases":393,"indexable":206},"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":142,"label":395,"issuer":396,"region":397,"url":398,"description":399,"useCases":400,"indexable":206},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":141,"label":156,"issuer":157,"region":158,"url":159,"description":402,"useCases":403,"indexable":206},"Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":405,"label":406,"issuer":147,"region":148,"url":407,"description":408,"useCases":409,"indexable":206},"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":143,"label":411,"issuer":412,"region":148,"url":413,"description":414,"useCases":415,"indexable":206},"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":417,"label":418,"issuer":419,"region":148,"url":420,"description":421,"useCases":422,"indexable":206},"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":424,"label":425,"issuer":426,"region":427,"url":428,"description":429,"useCases":430,"indexable":206},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":432,"label":433,"issuer":434,"region":427,"url":435,"description":436,"useCases":437,"indexable":206},"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":439,"label":440,"issuer":441,"region":397,"url":442,"description":443,"useCases":444,"indexable":206},"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":446,"label":447,"issuer":448,"region":158,"url":449,"description":450,"useCases":444,"indexable":206},"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":452,"label":453,"issuer":454,"region":148,"url":455,"description":456,"useCases":457,"indexable":206},"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":459,"label":460,"issuer":461,"region":397,"url":462,"description":463,"useCases":359,"indexable":206},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":465,"label":466,"issuer":147,"region":148,"url":467,"description":468,"useCases":469,"indexable":206},"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":471,"label":472,"issuer":147,"region":148,"url":473,"description":474,"useCases":469,"indexable":206},"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":476,"label":477,"issuer":478,"region":158,"url":479,"description":480,"useCases":481,"indexable":206},"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":483,"label":484,"issuer":147,"region":148,"url":485,"description":486,"useCases":272,"indexable":206},"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.",{"id":488,"label":489,"issuer":490,"region":158,"url":491,"description":492,"useCases":272,"indexable":206},"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":494,"label":495,"issuer":496,"region":397,"url":497,"description":498,"useCases":272,"indexable":206},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":500,"label":501,"issuer":147,"region":148,"url":502,"description":503,"useCases":368,"indexable":206},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":505,"label":506,"issuer":507,"region":158,"url":508,"description":509,"useCases":368,"indexable":206},"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":511,"label":512,"issuer":426,"region":427,"url":513,"description":514,"useCases":515,"indexable":206},"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":517,"label":518,"issuer":147,"region":148,"url":519,"description":520,"useCases":515,"indexable":206},"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":522,"label":523,"issuer":147,"region":148,"url":524,"description":525,"useCases":515,"indexable":206},"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":527,"label":528,"issuer":529,"region":148,"url":530,"description":531,"useCases":369,"indexable":206},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":533,"label":534,"issuer":535,"region":158,"url":536,"description":537,"useCases":324,"indexable":206},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":539,"label":540,"issuer":147,"region":148,"url":541,"description":542,"useCases":324,"indexable":206},"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":544,"label":545,"issuer":147,"region":148,"url":546,"description":547,"useCases":548,"indexable":206},"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.",6,{"id":550,"label":551,"issuer":552,"region":553,"url":554,"description":555,"useCases":348,"indexable":206},"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":557,"label":558,"issuer":559,"region":148,"url":560,"description":561,"useCases":562,"indexable":206},"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":564,"label":565,"issuer":566,"region":148,"url":567,"description":568,"useCases":562,"indexable":206},"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":570,"label":571,"issuer":572,"region":427,"url":573,"description":574,"useCases":575,"indexable":206},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":577,"label":578,"issuer":147,"region":148,"url":579,"description":580,"useCases":575,"indexable":206},"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":582,"label":583,"issuer":584,"region":158,"url":585,"description":586,"useCases":575,"indexable":206},"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.",1790598306547]