[{"data":1,"prerenderedAt":610},["ShallowReactive",2],{"uc-epidemic-intelligence-signal-detection":3,"uc-regulations":381},{"useCase":4,"evidence":187,"blitsAiDeployments":263,"benchmarks":264,"indicative":270,"related":273,"indexability":379,"includeUnpublished":193},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":23,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":48,"macroEstimates":83,"feasibility":84,"implementation":96,"risk":139,"blitsAi":166,"faq":168,"related":181,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI epidemic intelligence for detecting public health threat signals in open source news and social media","Epidemic intelligence signals","AI epidemic intelligence signal detection","WHO's EIOS is used by more than 110 Member States; Canada's GPHIN pulled over 7,000 articles a day to flag outbreaks. Analysts review each signal.","published","Epidemic intelligence signal detection is AI that scans news, social media and feeds for early signs of outbreaks and other health threats, so analysts can verify and escalate them. Public health agencies use it to complement reports from laboratories and hospitals.",[12,13,14,15,16],"event based surveillance","open source epidemic intelligence","outbreak early warning AI","health threat signal detection","public health media monitoring",[18,19],"government","healthcare",[21,22],"risk-management","analytics-and-reporting",[24,25],"classification-and-routing","translation",[27,28],"internal-tools","email","employee-facing","assist","early-adopters","Formal surveillance depends on laboratories, hospitals and official reports, which reach a national\nagency once cases are confirmed and reported. News, social media and other public sources can\ncarry the first sign of an outbreak, in the local language, before a report arrives. Canada's GPHIN\nreview panel cites a case in which smartphone and Twitter data helped identify an Ebola outbreak in\nWest Africa three days before an official WHO announcement. A small team cannot read that flow by\nhand: the panel reports that GPHIN's queries pulled over 7,000 articles a day into its platform and\nthat about half were filtered out before analysts manually reviewed the rest.\n\nOpen source signals are noisy, and the panel notes that false positives are an accepted outcome of event based surveillance, because missing a real event\ncosts more than checking one that turns out to be nothing. The system has to raise the likely\nevents quickly, work across languages and leave verification and escalation to people.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"Canada's GPHIN review panel reports that smartphones and Twitter data helped public health professionals identify an Ebola outbreak in West Africa three days before an official WHO announcement.","Global Public Health Intelligence Network (GPHIN) Independent Review Panel Final Report","https:\u002F\u002Fwww.canada.ca\u002Fen\u002Fpublic-health\u002Fcorporate\u002Fmandate\u002Fabout-agency\u002Fexternal-advisory-bodies\u002Flist\u002Findependent-review-global-public-health-intelligence-network\u002Ffinal-report.html",2021,"1. **Define sources and queries.** The service licenses a news feed and adds RSS feeds from health\n   bodies and media, and selected social media accounts. Analysts write and refine the queries.\n   GPHIN drew 84 percent of its input from a single licensed news service.\n2. **Process automatically.** A pipeline flags near duplicate articles, adds metadata, classifies\n   each article against a hazard taxonomy, machine translates foreign language text and scores\n   relevance. GPHIN's automated stage has these five phases. WHO's EIOS 2.0 adds AI tools for\n   automated analysis and signal detection and processes radio broadcasts with automatic\n   transcription and translation.\n3. **Analysts triage.** Analysts read the filtered articles, check the source and decide whether\n   an item is a signal under the International Health Regulations criteria. They send alerts and a daily\n   report to subscribers.\n4. **Verify and assess.** Public health authorities verify the signal through official channels and\n   assess its risk. A signal can reach senior decision makers within two days: the pneumonia\n   signal GPHIN detected on 30 December 2019 was raised to senior tables across government by 1 January.\n5. **Maintain.** Analysts add new vocabulary to the taxonomy, for example the word \"COVID\" after it\n   appeared, and review false positives and missed events.",[41,42,43],"risk-reduction","speed","employee-productivity",[45,46,47],"users-served","interactions-handled","detection-rate-improvement",{"referenceOrg":49,"inputs":50,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A national public health agency whose analysts review open source articles every day",[51,58,65,71],{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57,"sourceUrl":37},"articlesPerDay","Articles collected per day",3000,7000,"articles per day","The review panel reports that GPHIN pulled over 7,000 articles a day into its platform. The low end is an editorial assumption for a smaller service.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64,"sourceUrl":37},"filteredShare","Share of articles filtered out before analysts read the rest",0.3,0.5,"fraction of articles","The panel reports that about half of the articles were filtered out before analysts manually reviewed the rest, without saying how much of that is automatic. Counting the filter as work the software does is an editorial assumption, as is the low end.",{"key":66,"label":67,"low":62,"high":68,"unit":69,"note":70},"minutesPerArticle","Analyst minutes to skim one article",1,"minutes per article","Editorial assumption. Replace with a time study of your own analysts.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"hourlyCost","Fully loaded cost of one analyst hour",40,70,"EUR per hour","Editorial assumption. Replace with your own cost.","articlesPerDay * filteredShare * minutesPerArticle \u002F 60 * 365 * hourlyCost","EUR","per year","Analyst reading time avoided by filtering","This values only the reading time that filtering saves. It does not value the earlier warning, which is the reason to run the service, and it leaves out the cost of the news licenses (GPHIN's main feed cost CAD 1.8 million every two years according to the review panel), the platform and the analysts' remaining work, which together can exceed the reading time saved.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"medium","A working service needs licensed news and monitoring feeds, a multilingual hazard taxonomy and query sets, a processing pipeline with deduplication, classification and machine translation, analysts who read the languages of the sources, and a channel to subscribers and to national and international health authorities. For GPHIN the main licensed news service accounted for 84 percent of input at a cost of CAD 1.8 million every two years, according to the review panel.",[88,89,90,91],"Licensed news feed and RSS sources with rights to process and share article links","A hazard taxonomy and multilingual query sets maintained by analysts","A history of verified signals and false alarms to measure the system against","Rules on which social media sources may be processed and how personal data is handled",[93,94,95],"News and media monitoring feeds","Signal and event management system for analysts","Email or portal distribution of alerts and daily reports to national and international partners",{"steps":97,"guardrails":113,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[98,101,104,107,110],{"title":99,"detail":100},"Name the decision the service supports","Write down which events must reach which decision makers in what time. WHO presents EIOS as a complement to formal channels such as laboratories and hospitals, with authorities verifying and assessing what the system surfaces.",{"title":102,"detail":103},"Secure sources before models","List the feeds, the licenses and the languages first. GPHIN's panel recommended that the team continue to explore incorporating further social media, and was told that the platform lacked the technical capacity to monitor open source social media in full.",{"title":105,"detail":106},"Keep analysts in the loop and invest in language coverage","GPHIN is a partially moderated system: machine learning and algorithmic filtering do the first pass and analysts working in nine languages decide what is a signal. The panel judged that language ability is one reason GPHIN's algorithms work.",{"title":108,"detail":109},"Treat the platform as a product","Those closest to GPHIN's system told the panel that it was unstable, often slow and prone to crashes, and the panel notes that fixes made by the National Research Council have not moved to production since fall 2018. The panel heard differences of opinion about the state of the system. Assign an owner, a release process and a budget.",{"title":111,"detail":112},"Evaluate against outcomes and peers","The panel found that earlier attempts to evaluate the performance of PHAC's event based and indicator based surveillance systems appear to have relied primarily on self assessments. Compare detection times with other systems and record false positives and misses.",[114,115,116,117,118],"A signal is a lead, not a confirmed event; nothing is published as fact before verification","Analysts check the source and apply a written criteria set before an alert goes out","Personal data from social media is minimized and processing is limited to what the service needs","Machine translations are checked by an analyst before they inform an alert","A named owner approves changes to queries, taxonomy and models","Analysts read the filtered articles, decide what counts as a signal and write the alerts. Epidemiologists verify and assess a signal with national and international partners before any response. People decide whether an event exists; the software raises leads.",[121,122,123,124,125],"Articles collected, filtered out and reviewed per day","Share of alerts confirmed as events and share judged false positives","Time from first public report to alert, against peer systems","Share of health events first detected through the tool","Active users and share of countries or teams using it in routine work",[127,130,133,136],{"title":128,"detail":129},"Platform decay","GPHIN's review panel described a system analysts called unstable, with fixes not migrated to production since 2018. A tool that is slow or crashes loses analysts' trust and time.",{"title":131,"detail":132},"Source concentration","When one licensed service supplies 84 percent of input, a price rise or a license change changes what the system sees. Keep a plan for the second source.",{"title":134,"detail":135},"Alert fatigue","Accepting some false positives is intended in this field, but a daily flood of weak alerts lowers the value of each one. Tune relevance scores against analysts' decisions.",{"title":137,"detail":138},"Unclear mandate and governance","The panel found GPHIN's mandate, governance and information flows needed work, even though GPHIN's analyst on duty had detected the pneumonia signal. A technical upgrade does not fix who decides, who is told and who pays.",{"euAiAct":140,"regulations":143,"guidance":148,"controls":159,"incidents":165},{"tier":141,"basis":142},"minimal","Processing public text and broadcasts to find health events is not a use listed in Annex III and makes no decision about a person. GDPR still applies where social media posts or other personal data are processed, and the assessment would change if the system were used to profile individuals.",[144,145,146,147],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001",[149,153],{"title":36,"issuer":150,"region":151,"url":37,"note":152},"Public Health Agency of Canada, external review panel","north-america","Independent review of a long running system, with findings on mandate, governance, technology and analysts, and recommendations.",{"title":154,"issuer":155,"region":156,"url":157,"note":158},"WHO upgrades its public health intelligence system to boost global health security","World Health Organization","global","https:\u002F\u002Fwho.int\u002Fnews\u002Fitem\u002F13-10-2025-who-upgrades-its-public-health-intelligence-system-to-boost-global-health-security","Describes EIOS 2.0, its AI tools, who uses it and how authorities verify what it surfaces.",[160,161,162,163,164],"Written criteria for what counts as a signal, applied by analysts","Verification step with named epidemiologists before any response","Source register with licenses and a second source plan","Review of false positives and missed events each quarter","Data protection rules for social media sources",[],{"howToBuild":167},"The news feeds, the signal pipeline and the surveillance case system usually live in the health\nagency's own environment. Blits.ai adds an analyst facing layer. An **AI agent** can answer\nanalysts' questions about the day's signals through **custom functions** and a **SQL knowledge\nbase** over the signal log, and from guidance documents such as the International Health Regulations criteria\nthrough a **knowledge base** with hybrid retrieval. Model agnostic routing lets the team pick the\nmodel that translates a given language best.\n\nAn **agentic workflow** can draft the daily report from analysts' selected signals, with **human in\nthe loop approval** before it is sent by email or in Microsoft Teams. **Guardrails** keep the agent\nto the log and to cited sources, **PII masking** limits personal data from social media,\n**monitors** check answers on a schedule and **test suites** replay past signals. Run Blits.ai with EU or UAE\ndata residency.",[169,172,175,178],{"question":170,"answer":171},"What is event based surveillance?","It is the monitoring of unstructured sources such as news, social media and reports for signs of health events, as a complement to indicator based surveillance from laboratories and hospitals. GPHIN's review panel describes GPHIN as a public health event based system, and WHO describes EIOS as complementing formal channels.",{"question":173,"answer":174},"Does AI replace the analysts?","No deployment on this page does. GPHIN is partially moderated: machine learning and algorithmic filtering do the first pass and analysts work in nine languages. WHO says authorities verify and assess what EIOS identifies.",{"question":176,"answer":177},"Who uses it and how widely?","WHO reports that more than 110 Member States and around 30 organizations and networks use EIOS. PAHO reports that 88 percent of 17 trained countries in the Americas used it in routine surveillance within a year of training, though 29 percent only as ad hoc users.",{"question":179,"answer":180},"Did it detect COVID early?","GPHIN's analyst detected a signal about a pneumonia outbreak on 30 December 2019, and the review panel found the timing in line with similar systems, with BlueDot and ProMED identifying the outbreak on the same day. The panel saw no evidence that earlier detection would have been possible.",[],"2026-10-14",[184],{"date":182,"note":185},"First published","epidemic-intelligence-signal-detection",[188,225,246],{"title":189,"useCases":190,"organization":191,"vendors":196,"summary":197,"stage":198,"year":38,"channels":199,"languages":200,"metrics":209,"outcomeDisclosed":216,"sources":217,"verification":220,"grade":222,"id":223,"organizationSlug":224},"Public Health Agency of Canada: GPHIN open source event based surveillance",[186],{"name":192,"anonymized":193,"country":194,"region":151,"industry":18,"wikidata":195},"Public Health Agency of Canada (PHAC)",false,"CA","Q2826604",[],"The Global Public Health Intelligence Network (GPHIN) is a Canadian early warning system that scans global news in nine languages for chemical, biological, radiological and nuclear and other public health threats. An independent review panel described it as partially moderated: an automated stage flags duplicates, adds metadata, categorizes and translates articles and scores their relevance, using machine learning and algorithmic filtering, and analysts review the rest and write alerts and daily reports. GPHIN's analyst on duty detected a signal about a pneumonia outbreak on 30 December 2019, which the panel found came at about the same time as other surveillance systems; BlueDot and ProMED flagged the outbreak the same day. The panel also found the platform unstable, in use in its 2018 version with later fixes not migrated, and recommended changes to mandate, governance, technology and the role of analysts. The panel's report is dated May 2021 and was published in July 2021.","production",[27,28],[201,202,203,204,205,206,207,208],"ar","zh","en","fr","ru","es","pt","fa",[210],{"kpi":46,"value":55,"unit":211,"qualifier":212,"period":213,"claimant":214,"quote":215,"sourceUrl":37},"count","at-least","articles pulled into the system per day","independent","Analysts have created (and continue to refine) sets of complex logical queries that pull over 7,000 articles a day into the system.",true,[218],{"url":37,"title":36,"publisher":150,"date":219},"2021-07-12",{"level":221,"checkedAt":182},"source-verified","A","phac-gphin-event-based-surveillance",null,{"title":226,"useCases":227,"organization":228,"vendors":231,"summary":232,"stage":198,"year":233,"channels":234,"languages":235,"metrics":236,"outcomeDisclosed":216,"sources":237,"verification":243,"grade":244,"id":245,"organizationSlug":224},"Pan American Health Organization: EIOS use for early detection in the Americas",[186],{"name":229,"anonymized":193,"region":156,"industry":18,"wikidata":230},"Pan American Health Organization (PAHO\u002FWHO)","Q1856835",[],"PAHO has trained countries in the Americas on the WHO Epidemic Intelligence from Open Sources (EIOS) system since 2019. A published analysis covering 17 countries trained between August 2019 and October 2023, after 105 PAHO training sessions, found that most of them put EIOS into routine event based surveillance within a year of training, at different intensity: 24 percent became frequent users, 18 percent moderate, 18 percent minimal and 29 percent ad hoc users. PAHO reports that in countries using EIOS the share of health events first detected and reported by national authorities rose from 68 percent to 85 percent in the first year after training, while it fell in countries with minimal or no use. PAHO reads this as evidence that EIOS strengthens early warning; the release is PAHO's own summary of the analysis.",2025,[27],[],[],[238],{"url":239,"title":240,"publisher":241,"date":242},"https:\u002F\u002Fwww.paho.org\u002Fen\u002Fnews\u002F28-4-2025-paho-highlights-impact-eios-system-early-detection-public-health-threats-americas","PAHO Highlights the Impact of the EIOS System for Early Detection of Public Health Threats in the Americas","Pan American Health Organization","2025-04-28",{"level":221,"checkedAt":182},"B","paho-eios-early-detection-americas",{"title":247,"useCases":248,"organization":249,"vendors":252,"summary":253,"stage":254,"year":233,"channels":255,"languages":256,"metrics":257,"outcomeDisclosed":216,"sources":258,"verification":261,"grade":244,"id":262,"organizationSlug":224},"World Health Organization: EIOS epidemic intelligence from open sources",[186],{"name":250,"anonymized":193,"region":156,"industry":18,"wikidata":251},"World Health Organization (WHO Hub for Pandemic and Epidemic Intelligence)","Q7817",[],"The Epidemic Intelligence from Open Sources (EIOS) system, hosted at the WHO Hub in Berlin, lets ministries of health and public health agencies find relevant content in websites, social media and other public sources to spot health events early, which authorities then verify and assess. WHO offers it free of charge to its Member States and eligible organizations, with training and communities of practice, to complement formal channels such as laboratories and hospitals. On 13 October 2025 WHO launched version 2.0, which adds new data sources, AI powered tools for automated analysis and signal detection and processing of radio broadcasts with automatic transcription and translation. WHO reports that more than 110 Member States and around 30 organizations and networks use it.","scaled",[27],[],[],[259],{"url":157,"title":154,"publisher":155,"date":260},"2025-10-13",{"level":221,"checkedAt":182},"who-eios-epidemic-intelligence-platform",0,[265],{"kpi":46,"label":266,"unit":211,"aggregate":193,"higherIsBetter":216,"n":68,"nUpTo":263,"median":55,"min":55,"max":55,"byClaimant":267,"vendorOnly":193,"points":268},"Interactions handled",{"organization":263,"vendor":263,"regulator":263,"independent":68},[269],{"evidenceId":223,"organization":192,"value":55,"qualifier":212,"claimant":214,"grade":222,"pooled":216},{"low":271,"high":272},109500,1490416.6666666667,[274,297,331,355],{"slug":275,"title":276,"shortTitle":277,"definition":278,"status":9,"industries":279,"functions":282,"patterns":285,"audience":288,"autonomy":289,"adoptionStage":31,"evidenceCount":290,"publicEvidenceCount":290,"organizations":291,"bestGrade":244,"headline":224,"lastVerified":296,"indexable":216},"procurement-spend-classification","AI spend classification and spend analytics for procurement","Spend classification","Spend classification is AI that reads purchase orders, invoices, card transactions and contracts and assigns each line of spend to a category, for procurement teams. It assigns spend to the organization's taxonomy and to the right supplier, so procurement can see what is bought, from whom and where to consolidate or negotiate.",[280,18,281,19],"cross-industry","manufacturing",[283,284,22],"procurement","finance-and-accounting",[24,286,287],"document-processing","summarization","back-office","supervised-agent",4,[292,293,294,295],"U.S. General Services Administration","Internal Revenue Service","U.S. Department of Agriculture","Veterans Health Administration","2026-09-27",{"slug":298,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":306,"patterns":308,"audience":29,"autonomy":312,"adoptionStage":31,"segment":313,"evidenceCount":314,"publicEvidenceCount":315,"organizations":316,"bestGrade":244,"headline":323,"lastVerified":296,"indexable":216},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","AI deal sourcing and due diligence is an AI assistant that screens the market for acquisition or investment targets and speeds up due diligence for a deal team. It builds company profiles, reads data room documents, extracts key terms and risks, and drafts the investment or diligence memo, for the team to verify and decide.",[303,304,305],"capital-markets","wealth-and-asset-management","professional-services",[22,307,21],"legal",[286,287,309,310,311],"rag-knowledge-assistant","agentic-workflow","prediction-and-scoring","copilot","front-office",7,6,[317,318,319,320,321,322],"Datasite","EQT","Freshfields","Ice Miller","Orrick","Rogo",{"kpi":324,"label":325,"unit":326,"n":263,"nUpTo":68,"kind":327,"value":328,"qualifier":329,"claimant":330,"organization":317,"vendorReported":216},"productivity-gain","Productivity gain","percent","reported",80,"up-to","vendor",{"slug":332,"title":333,"shortTitle":334,"definition":335,"status":9,"industries":336,"functions":338,"patterns":340,"audience":29,"autonomy":30,"adoptionStage":31,"evidenceCount":315,"publicEvidenceCount":315,"organizations":341,"bestGrade":244,"headline":348,"lastVerified":296,"indexable":216},"clinical-trial-patient-matching","AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI that compares a patient's health record against the eligibility criteria of open clinical trials to help research staff and treating clinicians find candidates. It reads structured data and clinical notes, then gives staff a ranked list of likely eligible patients with the evidence for each criterion, so that people confirm eligibility and invite the patient.",[19,337],"pharma-and-life-sciences",[339,22],"operations",[286,24],[342,343,344,345,346,347],"Advocate Health","Cleveland Clinic","Mayo Clinic","Mount Sinai Health System","Northwestern Medicine","Yale Cancer Center",{"kpi":349,"label":350,"unit":326,"n":351,"nUpTo":263,"kind":327,"value":352,"qualifier":353,"claimant":354,"organization":343,"vendorReported":193},"accuracy","Accuracy",2,100,"exact","organization",{"slug":356,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":361,"patterns":362,"audience":29,"autonomy":30,"adoptionStage":31,"segment":364,"evidenceCount":314,"publicEvidenceCount":314,"organizations":365,"bestGrade":244,"headline":373,"lastVerified":378,"indexable":216},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI command center for hospital bed and staff capacity planning predicts patient admissions, discharges and transfers, and helps a team of coordinators sequence bed assignments, staffing and moves. The coordinators sit in one room and act on real time predictions across a hospital or health system, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[19],[339,22],[311,24,363],"anomaly-detection","hospital operations",[366,367,368,369,370,371,372],"Children's Mercy Kansas City","Humber River Health","Johns Hopkins Medicine","King Faisal Specialist Hospital and Research Centre (KFSHRC)","Providence Swedish","The Queen's Health Systems","Tampa General Hospital",{"kpi":374,"label":375,"unit":326,"n":314,"nUpTo":263,"kind":376,"value":377,"qualifier":353,"claimant":354,"organization":224,"vendorReported":193},"processing-time-reduction","Cycle time reduction","median",34,"2026-09-28",{"indexable":216,"reasons":380},[],[382,389,394,400,406,413,419,426,434,441,447,453,459,466,473,479,485,491,497,504,509,514,520,527,534,539,544,550,557,562,569,576,581,587,594,599,604],{"id":144,"label":383,"issuer":384,"region":385,"url":386,"description":387,"useCases":388,"indexable":216},"EU AI Act","European Union","europe","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.",295,{"id":145,"label":390,"issuer":384,"region":385,"url":391,"description":392,"useCases":393,"indexable":216},"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.",265,{"id":147,"label":395,"issuer":396,"region":156,"url":397,"description":398,"useCases":399,"indexable":216},"ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":146,"label":401,"issuer":402,"region":151,"url":403,"description":404,"useCases":405,"indexable":216},"NIST AI Risk Management Framework","NIST","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":407,"label":408,"issuer":409,"region":385,"url":410,"description":411,"useCases":412,"indexable":216},"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.",84,{"id":414,"label":415,"issuer":384,"region":385,"url":416,"description":417,"useCases":418,"indexable":216},"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":420,"label":421,"issuer":422,"region":385,"url":423,"description":424,"useCases":425,"indexable":216},"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":427,"label":428,"issuer":429,"region":430,"url":431,"description":432,"useCases":433,"indexable":216},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":435,"label":436,"issuer":437,"region":430,"url":438,"description":439,"useCases":440,"indexable":216},"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":442,"label":443,"issuer":384,"region":385,"url":444,"description":445,"useCases":446,"indexable":216},"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":448,"label":449,"issuer":450,"region":156,"url":451,"description":452,"useCases":446,"indexable":216},"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":454,"label":455,"issuer":456,"region":151,"url":457,"description":458,"useCases":446,"indexable":216},"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":460,"label":461,"issuer":462,"region":151,"url":463,"description":464,"useCases":465,"indexable":216},"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.",20,{"id":467,"label":468,"issuer":469,"region":385,"url":470,"description":471,"useCases":472,"indexable":216},"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":474,"label":475,"issuer":384,"region":385,"url":476,"description":477,"useCases":478,"indexable":216},"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":480,"label":481,"issuer":482,"region":156,"url":483,"description":484,"useCases":478,"indexable":216},"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":486,"label":487,"issuer":488,"region":151,"url":489,"description":490,"useCases":478,"indexable":216},"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.",{"id":492,"label":493,"issuer":384,"region":385,"url":494,"description":495,"useCases":496,"indexable":216},"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":498,"label":499,"issuer":500,"region":151,"url":501,"description":502,"useCases":503,"indexable":216},"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":505,"label":506,"issuer":384,"region":385,"url":507,"description":508,"useCases":503,"indexable":216},"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":510,"label":511,"issuer":384,"region":385,"url":512,"description":513,"useCases":503,"indexable":216},"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":515,"label":516,"issuer":517,"region":156,"url":518,"description":519,"useCases":503,"indexable":216},"telecom-consumer-rules","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":521,"label":522,"issuer":523,"region":151,"url":524,"description":525,"useCases":526,"indexable":216},"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":528,"label":529,"issuer":530,"region":385,"url":531,"description":532,"useCases":533,"indexable":216},"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":535,"label":536,"issuer":384,"region":385,"url":537,"description":538,"useCases":533,"indexable":216},"eecc","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":540,"label":541,"issuer":384,"region":385,"url":542,"description":543,"useCases":533,"indexable":216},"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":545,"label":546,"issuer":429,"region":430,"url":547,"description":548,"useCases":549,"indexable":216},"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":551,"label":552,"issuer":553,"region":151,"url":554,"description":555,"useCases":556,"indexable":216},"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":558,"label":559,"issuer":384,"region":385,"url":560,"description":561,"useCases":556,"indexable":216},"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":563,"label":564,"issuer":565,"region":566,"url":567,"description":568,"useCases":315,"indexable":216},"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":570,"label":571,"issuer":572,"region":385,"url":573,"description":574,"useCases":575,"indexable":216},"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.",5,{"id":577,"label":578,"issuer":384,"region":385,"url":579,"description":580,"useCases":290,"indexable":216},"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":582,"label":583,"issuer":584,"region":385,"url":585,"description":586,"useCases":290,"indexable":216},"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":588,"label":589,"issuer":590,"region":430,"url":591,"description":592,"useCases":593,"indexable":216},"au-scams-prevention-framework","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.",3,{"id":595,"label":596,"issuer":384,"region":385,"url":597,"description":598,"useCases":593,"indexable":216},"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":600,"label":601,"issuer":429,"region":430,"url":602,"description":603,"useCases":593,"indexable":216},"mas-shared-responsibility-framework","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":605,"label":606,"issuer":607,"region":151,"url":608,"description":609,"useCases":593,"indexable":216},"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.",1792049861011]