[{"data":1,"prerenderedAt":588},["ShallowReactive",2],{"uc-plant-operator-and-maintenance-copilot":3,"uc-regulations":380},{"useCase":4,"evidence":200,"blitsAiDeployments":291,"benchmarks":292,"indicative":299,"related":302,"indexability":378,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":23,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":51,"macroEstimates":90,"feasibility":91,"implementation":104,"risk":147,"blitsAi":179,"faq":181,"related":191,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI copilot for plant operators and maintenance technicians","Plant operator and maintenance copilot","AI maintenance assistant for plant technicians","An AI assistant answers questions on machine faults from manuals, fault logs and sensor data. BMW, Georgia-Pacific and Textron Aviation run one for technicians.","published","A generative AI assistant for the people who run and repair machines in plants, workshops and service centres: it answers fault and procedure questions from equipment manuals, fault reports, shift logs and live machine data, in the technician's language, with links to the sources, so faults are diagnosed faster and expert knowledge is not lost when experienced staff retire.",[12,13,14,15,16],"industrial copilot","shop floor AI assistant","maintenance troubleshooting assistant","operator assistant for manufacturing","aircraft maintenance AI assistant",[18,19],"manufacturing","automotive",[21,22],"operations","knowledge-management",[24,25,26,27],"rag-knowledge-assistant","conversational-agent","summarization","translation",[29,30],"internal-tools","mobile-app","employee-facing","assist","early-adopters","production","When a machine stops, every minute counts, and the answer usually exists somewhere: in\ndocumentation that can run to tens of thousands of pages (Textron Aviation counts more than\n60,000 pages for over 50 aircraft models), in last month's shift log, in a fault report from\nanother plant, or in the head of a technician who is on a different shift. Junior operators and\ntechnicians spend their time searching binders and portals or phoning the one expert who knows,\nwhile the line waits.\n\nThe knowledge is also leaving. Georgia-Pacific describes equipment that is 50 years old and, for\nmany machines, no proper documentation of operating procedures: the know how sits with\nexperienced employees, and they are retiring. New plants add a language problem: BMW notes that manuals are often\nnot available in Hungarian at its Debrecen plant. Keyword search over document portals does not\nsolve this, because the question is phrased as a symptom, not as a document title.",[],"1. **Gather the knowledge.** Equipment manuals, work instructions, fault reports, maintenance\n   records and shift logs are loaded and refreshed daily; interviews with retiring experts can be\n   recorded and turned into procedure documents.\n2. **Ask in plain language.** The operator or technician describes the symptom or error code on a\n   tablet, phone or line terminal, in their own language.\n3. **Retrieve and combine.** The assistant finds the relevant passages and, where connected, reads\n   the machine's current state and recent trends from sensor data.\n4. **Answer with sources.** It summarises the likely causes and the steps to check, with links to\n   the exact manual page or video frame, and answers follow up questions.\n5. **Hand over when needed.** Safety critical work, lockout procedures and anything the sources do\n   not cover go to the responsible engineer, and confirmed fixes are written back as new knowledge.",[39,40,41,42],"employee-productivity","speed","risk-reduction","cost-to-serve",[44,45,46,47,48,49,50],"time-saved-per-task","search-time-reduction","mttr-reduction","first-time-fix-rate","employee-adoption","time-to-proficiency-reduction","accuracy",{"referenceOrg":52,"inputs":53,"formula":85,"currency":86,"period":87,"resultLabel":88,"caveat":89},"A manufacturing site with 300 operators and maintenance technicians",[54,60,67,72,78],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"staff","Operators and technicians who use the assistant",300,"people","The reference site.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"lookupsPerWeek","Troubleshooting or procedure lookups per person per week",3,5,"lookups per person per week","Editorial assumption, replace with your own estimate from help desk calls or a short time study.",{"key":68,"label":69,"low":70,"high":70,"unit":68,"note":71},"weeks","Working weeks per year",46,"Editorial assumption.",{"key":73,"label":74,"low":64,"high":75,"unit":76,"note":77},"minutesSaved","Minutes saved per lookup",15,"minutes","Conservative against the benchmark on this page (Microsoft reports that at Textron Aviation troubleshooting that took up to 20 minutes takes one to two minutes), because many lookups are shorter than a full troubleshooting search.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"hourlyCost","Fully loaded cost per technician hour",40,60,"USD per hour","Editorial assumption, replace with your own labour cost.","staff * lookupsPerWeek * weeks * minutesSaved / 60 * hourlyCost","USD","per year","Technician time released","Values technician time only. It leaves out the usually larger value of shorter machine downtime and less off quality production, the cost of preparing and maintaining the content, and licence and model costs.",[],{"complexity":92,"complexityNote":93,"dataPrerequisites":94,"integrations":99},"medium","A first version over manuals and fault reports is quick to build. The effort is in document quality and ownership per site, in connecting live machine data safely, and in keeping answers about safety procedures correct.",[95,96,97,98],"Equipment manuals and work instructions per machine and site, with owners","Fault reports, maintenance records and shift logs in machine readable form","An asset register that links machines, documents and sensor tags","Optionally, recorded interviews with experienced staff",[100,101,102,103],"Document management and maintenance systems (enterprise asset management)","Plant historian or IoT platform for live machine data","Identity and access management for plant staff","Tablets, phones or line terminals on the shop floor",{"steps":105,"guardrails":121,"humanInTheLoop":127,"kpisToInstrument":128,"failureModes":134},[106,109,112,115,118],{"title":107,"detail":108},"Start where downtime is expensive and knowledge is thin","Pick one line or equipment family with frequent faults, many new staff and scattered documentation, and collect the questions technicians actually ask.",{"title":110,"detail":111},"Clean and own the sources","Load manuals, work instructions and recent fault reports with an owner and review date per document, remove outdated versions and mark safety critical procedures.",{"title":113,"detail":114},"Test with your best technicians","Let senior technicians put their hardest questions to it and score the answers, as Textron Aviation did before scaling, and fix the gaps in the content rather than the prompt.",{"title":116,"detail":117},"Add live machine data carefully","Connect sensor data for the assets in scope so answers can reflect the machine's current state and recent trends, as Georgia-Pacific does. Keep the connection read only, so the assistant has no write access to controls.",{"title":119,"detail":120},"Roll out plant by plant with local content","Reuse the platform, add each plant's documents and languages, and measure adoption and time to repair per site.",[122,123,124,125,126],"Answers only from approved sources, with a link to the source and a refusal when none is found","Safety critical procedures (lockout, high voltage, confined spaces) quoted from the approved document, never paraphrased","Read only access to machine data; no control actions from the assistant","Access by role and plant, so confidential process data stays with the right people","In regulated maintenance such as aviation, the assistant points to the approved maintenance data and never replaces it as the basis for the work","The technician decides and does the work; the assistant only informs. Maintenance engineers own the content per equipment family, review answers that technicians flag as wrong, and approve new documents before they are loaded. Safety procedures stay under the plant's safety management, and in aviation the work is still signed off against the approved maintenance data.",[129,130,131,132,133],"Mean time to repair on equipment in scope, before and after","Time to find an answer, from telemetry or a time study","Share of answers rated helpful, and flagged wrong answers per week","Weekly active users among operators and technicians","Weeks for new technicians to work independently",[135,138,141,144],{"title":136,"detail":137},"Outdated or conflicting manuals","The assistant faithfully quotes an old revision. Keep one current version per document and retire the rest.",{"title":139,"detail":140},"A paraphrased safety step","A summarised lockout procedure drops a step. Quote safety procedures verbatim and link the source.",{"title":142,"detail":143},"Pilot that never reaches the second plant","Each plant builds its own tool. BMW consolidated parallel plant pilots into one company wide application; plan for a shared platform early.",{"title":145,"detail":146},"Adoption stalls on the shop floor","The assistant is only available on office PCs. Put it on the devices technicians carry and in their language.",{"euAiAct":148,"regulations":151,"guidance":156,"controls":173,"incidents":178},{"tier":149,"basis":150},"limited","Article 50(1): staff must know they are interacting with an AI system, unless that is obvious from the context. Answering maintenance questions is not an Annex III use. It would become high risk under Annex III point 4(b) if the usage data were used to monitor and evaluate the performance of individual workers, so keep usage analytics aggregated.",[152,153,154,155],"eu-ai-act","gdpr","iso-42001","nist-ai-rmf",[157,163,167],{"title":158,"issuer":159,"region":160,"url":161,"note":162},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":164,"issuer":159,"region":160,"url":165,"note":166},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers AI used to monitor and evaluate the performance and behaviour of workers.",{"title":168,"issuer":169,"region":170,"url":171,"note":172},"AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary framework for mapping and managing AI risk, including the reliability of generated answers.",[174,175,176,177],"Document ownership, review dates and version control for every source","Logging of questions, retrieved sources and answers for review","Role based access by plant and function","Regression tests with real technician questions before each content or model change",[],{"howToBuild":180},"On Blits.ai this is an **AI agent** over a **knowledge base** that ingests manuals, work\ninstructions and fault reports (PDF, Word, spreadsheets and images) with version control, and\nretrieves with hybrid search so exact error codes and part numbers are found as well as\nsymptoms. A **SQL knowledge base** or read only **custom functions** add live machine data and\nwork order history, and **multi language** support with automatic language detection lets each\ntechnician ask in their own language.\n\nTechnicians reach the agent in **Microsoft Teams**, the web widget on a tablet or through the\nREST API channel inside existing shop floor apps, with voice input for hands busy work.\n**Guardrails** check answers against the plant's policies, **human handover** passes open\nquestions to a person on the responsible team, **test suites** replay real technician questions on every change and\nanalytics show which questions go unanswered. The platform is model agnostic, with EU and UAE\ndata residency.",[182,185,188],{"question":183,"answer":184},"Who uses generative AI assistants for maintenance?","BMW made its Factory Genius assistant available across its plants in 2025, Georgia-Pacific runs ChatGP for machine operators on Amazon Bedrock with live sensor data, and Textron Aviation built TAMI for aircraft technicians on Azure OpenAI Service.",{"question":186,"answer":187},"How much time does it save?","Microsoft reports that at Textron Aviation troubleshooting that took up to 20 minutes now takes one to two minutes, the benchmark recorded on this page. Few other figures are public, so measure time to repair and time to find an answer on your own equipment before and after.",{"question":189,"answer":190},"How is this different from a field service copilot?","A field service copilot supports technicians who visit customer sites and includes dispatch. This assistant serves operators and maintenance staff on machines inside a plant, workshop or service centre, and often combines documents with live machine data.",[192,193,194],"field-technician-copilot-and-dispatch","industrial-asset-predictive-maintenance","enterprise-knowledge-search","2026-09-27",[197],{"date":195,"note":198},"First published","plant-operator-and-maintenance-copilot",[201,230,257],{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":211,"stage":34,"year":212,"channels":213,"languages":214,"metrics":218,"outcomeDisclosed":206,"sources":219,"verification":225,"grade":227,"id":228,"organizationSlug":229},"BMW Group: Factory Genius, a generative AI assistant for troubleshooting production equipment",[199],{"name":205,"anonymized":206,"country":207,"region":160,"industry":19},"BMW Group",false,"DE",[209],{"name":205,"role":210},"in-house","When production equipment fails in a BMW plant, maintenance staff can ask Factory Genius, an in house assistant that searches equipment manuals, quality data, internal fault reports, planning documents and daily updated shift logs, shows the top matches with links to the sources, summarises the maintenance instructions and answers follow up questions in a chat. It also translates, which helped at the new plant in Debrecen where manuals are often not available in Hungarian. It grew out of a 2024 pilot in the Dingolfing body shop and parallel work in Spartanburg and Rosslyn, and BMW says it can now be used globally through an internal platform, in its initial development phase.",2025,[29],[215,216,217],"de","en","hu",[],[220],{"url":221,"title":222,"publisher":223,"date":224},"https://www.press.bmwgroup.com/global/article/detail/T0451072EN/%E2%80%9Cjust-ask-factory-genius-%E2%80%9D:-how-ai-helps-maintain-manufacturing-equipment?language=en","“Just ask Factory Genius!”: How AI helps maintain manufacturing equipment","BMW Group PressClub","2025-07-02",{"level":226,"checkedAt":195},"source-verified","B","bmw-group-factory-genius-maintenance-assistant","bmw-group",{"title":231,"useCases":232,"organization":233,"vendors":236,"summary":243,"stage":34,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":248,"sources":249,"verification":253,"grade":254,"id":255,"organizationSlug":256},"Georgia-Pacific: ChatGP, a generative AI assistant for machine operators that combines documents with live machine data",[199],{"name":234,"anonymized":206,"country":235,"region":170,"industry":18},"Georgia-Pacific","US",[237,240],{"name":238,"role":239},"Amazon Web Services","platform",{"name":241,"role":242},"Anthropic","model-provider","Georgia-Pacific built ChatGP with AWS Professional Services on Amazon Bedrock, using Anthropic's Claude, to give junior operators and maintenance technicians one place to ask about their machines. It answers from documents, maintenance records and Internet of Things sensor data streamed through Amazon Kinesis, so a question about a machine issue can draw on the machine's current state and recent trends and return step by step guidance tailored to that facility. The company also records conversations with experienced or retired experts and has a language model turn them into procedure documents. AWS reports that ChatGP reduced off quality production and machine downtime, and Georgia-Pacific planned to extend it to more facilities by the end of 2024.",2024,[29],[216],[],true,[250],{"url":251,"title":252,"publisher":238},"https://aws.amazon.com/solutions/case-studies/georgia-pacific-optimizes-operator-efficiency-case-study/","Georgia-Pacific Optimizes Operator Efficiency Using Generative AI on AWS",{"level":226,"checkedAt":195},"C","georgia-pacific-chatgp-operator-assistant",null,{"title":258,"useCases":259,"organization":260,"vendors":262,"summary":265,"stage":266,"year":244,"channels":267,"languages":268,"metrics":269,"outcomeDisclosed":248,"sources":277,"verification":289,"grade":254,"id":290,"organizationSlug":256},"Textron Aviation: TAMI, a generative AI assistant for aircraft maintenance technicians",[199],{"name":261,"anonymized":206,"country":235,"region":170,"industry":18},"Textron Aviation",[263],{"name":264,"role":239},"Microsoft","Textron Aviation, maker of Cessna and Beechcraft aircraft, built TAMI (Textron Aviation Maintenance Intelligence) on Azure OpenAI Service so that technicians in its service centres can query more than 60,000 pages of maintenance documentation for over 50 aircraft models in natural language, in several languages. Microsoft reports that troubleshooting that took up to 20 minutes now takes one to two minutes. A CIO.com profile of the company's CIO describes a pay as you go proof of concept in which senior mechanics' test questions were answered correctly 19 times out of 20, followed by a rollout to more than 1,500 mechanics across global service centres.","scaled",[29],[216],[270],{"kpi":44,"value":271,"unit":76,"qualifier":272,"baseline":273,"claimant":274,"quote":275,"sourceUrl":276},18,"up-to","Up to 20 minutes per troubleshooting search before TAMI; one to two minutes after","vendor","Troubleshooting that previously took up to 20 minutes can now be accomplished in one to two minutes.","https://www.microsoft.com/en/customers/story/23024-textron-aviation-azure-open-ai-service",[278,280,285],{"url":276,"title":279,"publisher":264},"Textron Aviation enhances maintenance efficiency with Azure AI",{"url":281,"title":282,"publisher":283,"date":284},"https://www.cio.com/article/4025048/textron-takes-flight-with-gen-ai.html","Textron takes flight with gen AI","CIO.com","2025-07-21",{"url":286,"title":287,"publisher":288,"date":284},"https://www.metisstrategy.com/textron-takes-flight-with-gen-ai/","Textron takes flight with Gen AI","Metis Strategy",{"level":226,"checkedAt":195},"textron-aviation-tami-maintenance-assistant",0,[293],{"kpi":44,"label":294,"unit":76,"aggregate":248,"higherIsBetter":248,"n":291,"nUpTo":295,"median":256,"min":256,"max":256,"byClaimant":296,"vendorOnly":206,"points":297},"Time saved per task",1,{"organization":291,"vendor":291,"regulator":291,"independent":291},[298],{"evidenceId":290,"organization":261,"value":271,"qualifier":272,"claimant":274,"grade":254,"pooled":206},{"low":300,"high":301},138000,1035000,[303,322,335,355],{"slug":192,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":309,"patterns":312,"audience":31,"autonomy":315,"adoptionStage":316,"segment":317,"evidenceCount":318,"publicEvidenceCount":318,"organizations":319,"bestGrade":227,"headline":256,"lastVerified":195,"indexable":248},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[308],"telecommunications",[310,21,311],"field-service","customer-service",[313,24,25,314],"prediction-and-scoring","classification-and-routing","copilot","emerging","network",2,[320,321],"nbn","Openreach",{"slug":193,"title":323,"shortTitle":324,"definition":325,"status":9,"industries":326,"functions":328,"patterns":329,"audience":31,"autonomy":32,"adoptionStage":33,"segment":331,"evidenceCount":63,"publicEvidenceCount":63,"organizations":332,"bestGrade":227,"headline":256,"lastVerified":195,"indexable":248},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[327,18],"energy-and-utilities",[21,310],[330,313],"anomaly-detection","asset-management",[333,234,334],"Duke Energy","Shell",{"slug":194,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":346,"patterns":347,"audience":31,"autonomy":32,"adoptionStage":348,"evidenceCount":349,"publicEvidenceCount":349,"organizations":350,"bestGrade":227,"headline":256,"lastVerified":195,"indexable":248},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[340,341,342,343,344,345],"cross-industry","banking","wealth-and-asset-management","insurance","government","professional-services",[22,21,311],[24,25,26],"mainstream",4,[351,352,353,354],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"slug":356,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":362,"patterns":363,"audience":31,"autonomy":315,"adoptionStage":348,"evidenceCount":63,"publicEvidenceCount":63,"organizations":366,"bestGrade":227,"headline":370,"lastVerified":195,"indexable":248},"ambient-clinical-documentation","AI ambient scribe for clinical documentation","Ambient clinical documentation","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[361],"healthcare",[21,22],[364,26,365],"speech-analytics","content-generation",[367,368,369],"Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","US Department of Veterans Affairs, Veterans Health Administration",{"kpi":371,"label":372,"unit":373,"n":295,"nUpTo":291,"kind":374,"value":375,"qualifier":376,"claimant":377,"organization":367,"vendorReported":206},"handling-time-reduction","Handling time reduction","percent","reported",8.2,"exact","organization",{"indexable":248,"reasons":379},[],[381,386,391,398,402,408,415,422,430,437,444,450,457,463,469,474,481,487,493,499,505,511,517,522,527,534,541,546,552,559,565,571,577,582],{"id":152,"label":382,"issuer":159,"region":160,"url":383,"description":384,"useCases":385,"indexable":248},"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":153,"label":387,"issuer":159,"region":160,"url":388,"description":389,"useCases":390,"indexable":248},"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":154,"label":392,"issuer":393,"region":394,"url":395,"description":396,"useCases":397,"indexable":248},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":155,"label":399,"issuer":169,"region":170,"url":171,"description":400,"useCases":401,"indexable":248},"NIST AI Risk Management Framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":403,"label":404,"issuer":159,"region":160,"url":405,"description":406,"useCases":407,"indexable":248},"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":409,"label":410,"issuer":411,"region":160,"url":412,"description":413,"useCases":414,"indexable":248},"uk-gdpr","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":416,"label":417,"issuer":418,"region":160,"url":419,"description":420,"useCases":421,"indexable":248},"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":423,"label":424,"issuer":425,"region":426,"url":427,"description":428,"useCases":429,"indexable":248},"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":431,"label":432,"issuer":433,"region":426,"url":434,"description":435,"useCases":436,"indexable":248},"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":438,"label":439,"issuer":440,"region":394,"url":441,"description":442,"useCases":443,"indexable":248},"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":445,"label":446,"issuer":447,"region":170,"url":448,"description":449,"useCases":443,"indexable":248},"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":451,"label":452,"issuer":453,"region":160,"url":454,"description":455,"useCases":456,"indexable":248},"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":458,"label":459,"issuer":460,"region":394,"url":461,"description":462,"useCases":75,"indexable":248},"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":464,"label":465,"issuer":159,"region":160,"url":466,"description":467,"useCases":468,"indexable":248},"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":470,"label":471,"issuer":159,"region":160,"url":472,"description":473,"useCases":468,"indexable":248},"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":475,"label":476,"issuer":477,"region":170,"url":478,"description":479,"useCases":480,"indexable":248},"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":482,"label":483,"issuer":159,"region":160,"url":484,"description":485,"useCases":486,"indexable":248},"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":488,"label":489,"issuer":490,"region":170,"url":491,"description":492,"useCases":486,"indexable":248},"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":394,"url":497,"description":498,"useCases":486,"indexable":248},"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":159,"region":160,"url":502,"description":503,"useCases":504,"indexable":248},"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.",11,{"id":506,"label":507,"issuer":508,"region":170,"url":509,"description":510,"useCases":504,"indexable":248},"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":512,"label":513,"issuer":425,"region":426,"url":514,"description":515,"useCases":516,"indexable":248},"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":518,"label":519,"issuer":159,"region":160,"url":520,"description":521,"useCases":516,"indexable":248},"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":523,"label":524,"issuer":159,"region":160,"url":525,"description":526,"useCases":516,"indexable":248},"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":528,"label":529,"issuer":530,"region":160,"url":531,"description":532,"useCases":533,"indexable":248},"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":535,"label":536,"issuer":537,"region":170,"url":538,"description":539,"useCases":540,"indexable":248},"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":542,"label":543,"issuer":159,"region":160,"url":544,"description":545,"useCases":540,"indexable":248},"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":547,"label":548,"issuer":159,"region":160,"url":549,"description":550,"useCases":551,"indexable":248},"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":553,"label":554,"issuer":555,"region":556,"url":557,"description":558,"useCases":64,"indexable":248},"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":560,"label":561,"issuer":562,"region":160,"url":563,"description":564,"useCases":349,"indexable":248},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":566,"label":567,"issuer":568,"region":160,"url":569,"description":570,"useCases":349,"indexable":248},"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":572,"label":573,"issuer":574,"region":426,"url":575,"description":576,"useCases":63,"indexable":248},"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.",{"id":578,"label":579,"issuer":159,"region":160,"url":580,"description":581,"useCases":63,"indexable":248},"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":583,"label":584,"issuer":585,"region":170,"url":586,"description":587,"useCases":63,"indexable":248},"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.",1790598298243]