[{"data":1,"prerenderedAt":556},["ShallowReactive",2],{"uc-field-technician-copilot-and-dispatch":3,"uc-regulations":344},{"useCase":4,"evidence":186,"blitsAiDeployments":240,"benchmarks":241,"indicative":242,"related":245,"indexability":342,"includeUnpublished":192},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":28,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":49,"macroEstimates":77,"feasibility":78,"implementation":92,"risk":134,"blitsAi":163,"faq":165,"related":175,"datePublished":181,"dateModified":181,"lastVerified":181,"changelog":182,"slug":185},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI copilot for field technicians and dispatch","AI can help decide if a fault needs a visit and predict the work. Openreach says AI updates to customers help prevent over 3,000 cancelled orders a month.","published","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.",[12,13,14,15,16],"field service copilot","technician assistant","truck roll avoidance","AI dispatch optimization","field workforce copilot",[18],"telecommunications",[20,21,22],"field-service","operations","customer-service",[24,25,26,27],"prediction-and-scoring","rag-knowledge-assistant","conversational-agent","classification-and-routing",[29,30,31],"mobile-app","sms","internal-tools","employee-facing","copilot","emerging","network","Technician visits are expensive: a van, a skilled person and often a customer who took time off to\nbe at home. Some visits should not happen at all, because the fault could have been fixed remotely\nor was not a fault. Others happen but fail: the\ntechnician finds a blocked duct, a missing part or a job that needs a different skill, and the\ncustomer has to wait for a second appointment.\n\nWhen installations run into trouble, customers can be left without clear information. Openreach\ndescribes how issues such as blocked underground ducts, complex build requirements or changed survey\nfindings could in the past mean missed appointments, repeat visits and uncertainty. nbn made the same point\nfrom the other side in 2017: it wanted its field workforce to connect more homes rather than attend\nto problems that do not exist. Meanwhile technicians on site search manuals, call colleagues or phone a help desk\nfor answers that already exist somewhere in the company.",[],"1. **Triage before dispatch.** When a fault is reported, a model uses line tests, device telemetry\n   and history to decide whether it can be fixed remotely, needs a visit, or needs a specific skill\n   or part.\n2. **Predict the job.** For visits, the system predicts the likely work type, duration and parts,\n   so the right technician arrives with the right kit, and flags jobs likely to need follow on work.\n3. **Plan and communicate.** Scheduling uses the predictions to build realistic routes and slots;\n   when a job hits a snag, the customer and the retail provider get a clear update with the\n   expected next step and timing, and can ask questions in their own words.\n4. **Assist on site.** The technician asks a copilot on a phone or tablet for procedures, wiring\n   diagrams, known issues and similar past jobs, and gets step by step diagnosis grounded in\n   approved documentation.\n5. **Close the loop.** Job notes, photos and outcomes are summarised into the ticket automatically\n   and feed back into triage and prediction models.",[40,41,42,43],"cost-to-serve","employee-productivity","customer-experience","speed",[45,46,47,48],"cost-savings","processing-time-reduction","productivity-gain","customer-satisfaction",{"referenceOrg":50,"inputs":51,"formula":72,"currency":73,"period":74,"resultLabel":75,"caveat":76},"A fixed line operator that dispatches 1 million technician visits a year",[52,58,65],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"visits","Technician visits per year",1000000,"visits per year","The reference operator.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"avoidedShare","Share of visits avoided through remote fixes, better triage and fewer repeat visits",0.02,0.06,"fraction of visits","Editorial assumption. The evidence on this page reports fewer cancellations and better triage but no verified share of visits avoided; replace with your own repeat visit and no fault found rates.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"costPerVisit","Fully loaded cost of a technician visit",100,200,"USD per visit","Editorial assumption. Replace with your own cost per visit.","visits * avoidedShare * costPerVisit","USD","per year","Technician visit cost avoided","Visit cost only. It leaves out time saved on site, fewer customer contacts about delayed jobs, fewer cancelled orders, and the cost of the platform and device rollout for technicians.",[],{"complexity":79,"complexityNote":80,"dataPrerequisites":81,"integrations":86},"medium","Triage and job prediction need joined up data from fault, test, workforce and order systems. The copilot for technicians is simpler but only works if manuals and procedures are current and usable offline or on weak coverage.",[82,83,84,85],"Fault and order history with visit outcomes, including no fault found and repeat visits","Line test and device telemetry results before dispatch","Job notes and completion codes from technicians","Current procedures, manuals and wiring diagrams in a searchable form",[87,88,89,90,91],"Workforce management and scheduling","Fault management and line test systems","Order management and customer notification channels","Technician mobile apps","Knowledge management for field procedures",{"steps":93,"guardrails":109,"humanInTheLoop":114,"kpisToInstrument":115,"failureModes":121},[94,97,100,103,106],{"title":95,"detail":96},"Measure wasted visits","Quantify no fault found, repeat and failed visits by fault type. That tells you where triage and prediction will pay back first.",{"title":98,"detail":99},"Start with triage before dispatch","Use tests and history to recommend remote fix or visit, and let dispatchers compare the recommendation with their decision before automating anything.",{"title":101,"detail":102},"Predict jobs that will go wrong","Predict when an installation will need extra work and use the prediction to update customers and plan the follow up early.",{"title":104,"detail":105},"Give technicians a grounded copilot","Put procedures and past job knowledge behind a copilot on the technician's device, with answers that cite the source and work on weak coverage.",{"title":107,"detail":108},"Keep humans in charge of people decisions","Use the models to plan work, not to rank or discipline individual technicians, and consult works councils or unions early.",[110,111,112,113],"Dispatch recommendations are advisory until measured against human decisions","No automated performance scoring or disciplinary use of technician data","Copilot answers cite approved procedures and refuse on safety critical steps without a source","Customer updates generated from predictions are reviewed for tone and accuracy on a sample basis","Dispatchers and team leaders own dispatch decisions and schedules, technicians own the work on site and can overrule the copilot, and safety procedures always follow the official manual. A sample of remote fix decisions is checked each week against later repeat faults.",[116,117,118,119,120],"Visits avoided through remote resolution, with repeat faults within 14 days counted against them","First time fix rate and repeat visit rate","Missed and cancelled appointments","Time on site per job type","Technician satisfaction with the copilot",[122,125,128,131],{"title":123,"detail":124},"Remote fixes that do not stick","The model keeps customers off the visit list but the fault returns. Count repeat faults against avoided visits.",{"title":126,"detail":127},"Predictions nobody tells the customer about","The system knows a job will slip but customers still wait at home. Connect predictions to proactive updates.",{"title":129,"detail":130},"Copilot that fails in the field","Answers depend on coverage the technician does not have. Design for weak signal and offline use.",{"title":132,"detail":133},"Surveillance by stealth","Job data starts being used to rate individuals, and trust collapses. Set and publish purpose limits.",{"euAiAct":135,"regulations":138,"guidance":142,"controls":157,"incidents":162},{"tier":136,"basis":137},"context-dependent","Triage and a knowledge copilot for technicians are normally minimal risk. Annex III point 4(b) lists AI systems that allocate tasks based on individual behaviour or personal traits, or that monitor and evaluate the performance and behaviour of workers, as high risk, so dispatch systems that do this need the full high risk controls, and under Article 26(7) employers must inform workers' representatives and the affected workers before using them. A conversational assistant that answers customers directly carries the Article 50 duty to tell people they are dealing with AI.",[139,140,141],"eu-ai-act","gdpr","nist-ai-rmf",[143,149,153],{"title":144,"issuer":145,"region":146,"url":147,"note":148},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4 covers employment and worker management, including task allocation based on individual behaviour and performance monitoring.",{"title":150,"issuer":145,"region":146,"url":151,"note":152},"Article 50, transparency obligations for providers and deployers of certain AI systems","https://artificialintelligenceact.eu/article/50/","AI systems that interact directly with people must be designed so those people know they are dealing with an AI system, unless that is obvious.",{"title":154,"issuer":145,"region":146,"url":155,"note":156},"Article 26, obligations of deployers of high risk AI systems","https://artificialintelligenceact.eu/article/26/","Paragraph 7 requires employers to inform workers' representatives and the affected workers before putting a high risk AI system into use at the workplace.",[158,159,160,161],"Documented purpose limits for technician data, agreed with employee representatives where required","Data protection impact assessment for location and job performance data","Measurement of dispatch recommendations against outcomes before automation","Review and ownership of field procedures used by the copilot",[],{"howToBuild":164},"On Blits.ai the technician copilot is an **AI agent** with a **knowledge base** of procedures,\nmanuals and resolved jobs using **hybrid retrieval**, reachable from the technician's phone through\nthe **REST API channel** inside the operator's own app, **Microsoft Teams** or **WhatsApp**, or\nthrough the **web chat widget** with **voice input** for moments when hands are busy. **Custom functions** read job details, line tests\nand device status from the operator's workforce and fault systems.\n\nFor customers, an agent on **SMS** or **WhatsApp**, built with **flows**, answers questions about a\ndelayed job in the customer's own words using the latest job status, with **human handover** to the\nservice team. **Agentic workflows** triggered via API when a job changes can prepare the update.\n**Guardrails** check inputs and outputs against the operator's policies, **PII masking**\nprotects customer data in prompts, and **test suites** replay real questions before every\nchange.",[166,169,172],{"question":167,"answer":168},"Can AI decide whether a technician needs to visit?","It can recommend it. In 2017 nbn described a machine learning Tech Lab meant to help determine whether a fault can be dealt with remotely or needs a field technician, so its workforce spends time connecting homes rather than attending problems that do not exist. The 2017 post reports no results, so start with dispatchers comparing the recommendation with their own decision.",{"question":170,"answer":171},"What happens when an installation runs into problems?","Openreach uses a prediction tool, Crystal Ball, to predict what work a delayed installation will need and whether it will take more or less than ten days. That drives a text message update to the customer, typically within 24 hours of the engineer reporting the issue. Openreach says its AI tools help prevent more than 3,000 cancellations a month.",{"question":173,"answer":174},"Is AI dispatch high risk under the EU AI Act?","It depends on the design. Planning jobs by fault type and location is normally not high risk; allocating work based on individual technicians' behaviour or monitoring their performance falls under Annex III point 4 and is high risk. A chatbot that answers customers must also be designed so they know they are talking to AI, unless that is obvious (Article 50).",[176,177,178,179,180],"predictive-network-maintenance","device-and-connectivity-troubleshooting-agent","network-fault-triage-copilot","network-outage-communication-agent","enterprise-knowledge-search","2026-09-27",[183],{"date":181,"note":184},"First published","field-technician-copilot-and-dispatch",[187,216],{"title":188,"useCases":189,"organization":190,"vendors":194,"summary":198,"stage":199,"year":200,"channels":201,"languages":202,"metrics":204,"outcomeDisclosed":205,"sources":206,"verification":211,"grade":213,"id":214,"organizationSlug":215},"Openreach: Crystal Ball and Ask Me Anything for delayed fibre installations",[185],{"name":191,"anonymized":192,"country":193,"region":146,"industry":18},"Openreach",false,"GB",[195],{"name":196,"role":197},"CXone Proactive AI Agent","platform","When an Openreach engineer finds a problem during a full fibre installation, a prediction tool called Crystal Ball predicts what type of work is likely to be needed and whether it will take more or less than ten days. That prediction drives a clear text message update to the customer, typically within 24 hours. A generative AI capability, Ask Me Anything, lets customers ask questions about their installation in their own words. Both are part of Openreach's work with the CXone Proactive AI Agent platform. Openreach says the tools help prevent more than 3,000 cancelled orders a month.","production",2026,[30],[203],"en",[],true,[207],{"url":208,"title":209,"publisher":191,"date":210},"https://www.openreach.com/news/openreach-ai-tools-help-cut-cancelled-full-fibre-orders-by-three-thousand-a-month/","Openreach AI tools help cut cancelled full fibre orders by three thousand a month","2026-09-15",{"level":212,"checkedAt":181},"source-verified","B","openreach-crystal-ball-installation-prediction",null,{"title":217,"useCases":218,"organization":219,"vendors":223,"summary":227,"stage":228,"year":229,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":192,"sources":233,"verification":238,"grade":213,"id":239,"organizationSlug":215},"nbn: machine learning Tech Lab to decide between remote fixes and technician visits",[185],{"name":220,"anonymized":192,"country":221,"region":222,"industry":18},"nbn","AU","asia-pacific",[224],{"name":225,"role":226},"nbn (in house Tech Lab)","in-house","In a 2017 blog post, nbn described a Tech Lab that would use big data and machine learning, including survey data from consenting end users, to improve the experience of its access network. One stated goal was to help teams determine whether a fault can be fixed remotely or needs a field technician to visit; another was to spot trends in failed activations so problems can be anticipated before a technician arrives. The post describes the programme's aims and reports no results.","announced",2017,[31],[203],[],[234],{"url":235,"title":236,"publisher":220,"date":237},"https://www.nbnco.com.au/blog/the-nbn-project/nbns-tech-labs-using-machine-learning-to-improve-network-experience","nbn’s Tech Lab: improving network experience through machine learning","2017-09-22",{"level":212,"checkedAt":181},"nbn-tech-lab-fault-dispatch-prediction",0,[],{"low":243,"high":244},2000000,12000000,[246,267,292,310,321],{"slug":176,"title":247,"shortTitle":248,"definition":249,"status":9,"industries":250,"functions":251,"patterns":253,"audience":256,"autonomy":257,"adoptionStage":258,"segment":35,"evidenceCount":259,"publicEvidenceCount":259,"organizations":260,"bestGrade":213,"headline":215,"lastVerified":181,"indexable":205},"AI for predictive network maintenance in telecom","Predictive network maintenance","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[18],[252,20,21],"network-operations",[254,24,255],"anomaly-detection","agentic-workflow","back-office","supervised-agent","early-adopters",6,[261,262,263,264,265,266],"KDDI","Orange","Telefónica España","Telstra","Verizon","Vodafone",{"slug":177,"title":268,"shortTitle":269,"definition":270,"status":9,"industries":271,"functions":272,"patterns":273,"audience":276,"autonomy":257,"adoptionStage":258,"segment":277,"evidenceCount":278,"publicEvidenceCount":278,"organizations":279,"bestGrade":213,"headline":283,"lastVerified":291,"indexable":205},"AI agent for device and connectivity troubleshooting on voice and chat","Device and connectivity troubleshooting","An AI agent that diagnoses and fixes a customer's broadband, mobile, TV or device problem by conversation on the phone or in chat, running line tests and remote resets through the operator's systems, guiding the customer step by step, and booking an engineer or handing over to a technician when the fault needs a person.",[18],[22,20],[26,274,255,25,275],"voice-agent","computer-vision","customer-facing","front-office",4,[280,281,282,266],"Singtel","Virgin Media O2","Vodafone Germany",{"kpi":284,"label":285,"unit":286,"n":287,"nUpTo":240,"kind":288,"value":289,"qualifier":290,"claimant":215,"organization":215,"vendorReported":192},"containment-rate","Containment rate","percent",3,"median",70,"exact","2026-09-26",{"slug":178,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":297,"patterns":298,"audience":32,"autonomy":33,"adoptionStage":258,"segment":35,"evidenceCount":259,"publicEvidenceCount":259,"organizations":300,"bestGrade":213,"headline":303,"lastVerified":181,"indexable":205},"AI copilot for network operations centre fault triage","NOC fault triage copilot","AI in the network operations centre (NOC) that correlates alarms and performance data from radio, transport, core and fixed networks into a small number of probable faults, ranks them by customer impact, proposes the likely root cause and fix from runbooks, vendor documentation and past tickets, and routes the ticket to the right team, while an engineer decides what to change.",[18],[252,21],[254,27,25,299,255],"summarization",[301,302,261,262,264,266],"Bell Canada","Deutsche Telekom",{"kpi":46,"label":304,"unit":286,"n":305,"nUpTo":240,"kind":306,"value":307,"qualifier":308,"claimant":309,"organization":302,"vendorReported":192},"Cycle time reduction",1,"reported",95,"at-least","organization",{"slug":179,"title":311,"shortTitle":312,"definition":313,"status":9,"industries":314,"functions":315,"patterns":316,"audience":276,"autonomy":257,"adoptionStage":34,"segment":277,"evidenceCount":318,"publicEvidenceCount":305,"organizations":319,"bestGrade":213,"headline":215,"lastVerified":181,"indexable":205},"AI agent for network outage detection and customer communication","Outage communication","An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[18],[22,252,20],[254,27,317,26,274],"content-generation",2,[320],"Comcast",{"slug":180,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":332,"patterns":334,"audience":32,"autonomy":335,"adoptionStage":336,"evidenceCount":278,"publicEvidenceCount":278,"organizations":337,"bestGrade":213,"headline":215,"lastVerified":181,"indexable":205},"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.",[326,327,328,329,330,331],"cross-industry","banking","wealth-and-asset-management","insurance","government","professional-services",[333,21,22],"knowledge-management",[25,26,299],"assist","mainstream",[338,339,340,341],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"indexable":205,"reasons":343},[],[345,350,355,363,370,376,383,390,397,404,411,417,424,431,437,442,449,455,461,467,473,479,485,490,495,502,509,514,519,527,533,539,545,550],{"id":139,"label":346,"issuer":145,"region":146,"url":347,"description":348,"useCases":349,"indexable":205},"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":351,"issuer":145,"region":146,"url":352,"description":353,"useCases":354,"indexable":205},"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":356,"label":357,"issuer":358,"region":359,"url":360,"description":361,"useCases":362,"indexable":205},"iso-42001","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":364,"issuer":365,"region":366,"url":367,"description":368,"useCases":369,"indexable":205},"NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":371,"label":372,"issuer":145,"region":146,"url":373,"description":374,"useCases":375,"indexable":205},"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":377,"label":378,"issuer":379,"region":146,"url":380,"description":381,"useCases":382,"indexable":205},"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":384,"label":385,"issuer":386,"region":146,"url":387,"description":388,"useCases":389,"indexable":205},"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":391,"label":392,"issuer":393,"region":222,"url":394,"description":395,"useCases":396,"indexable":205},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":398,"label":399,"issuer":400,"region":222,"url":401,"description":402,"useCases":403,"indexable":205},"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":405,"label":406,"issuer":407,"region":359,"url":408,"description":409,"useCases":410,"indexable":205},"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":412,"label":413,"issuer":414,"region":366,"url":415,"description":416,"useCases":410,"indexable":205},"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":418,"label":419,"issuer":420,"region":146,"url":421,"description":422,"useCases":423,"indexable":205},"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":425,"label":426,"issuer":427,"region":359,"url":428,"description":429,"useCases":430,"indexable":205},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":432,"label":433,"issuer":145,"region":146,"url":434,"description":435,"useCases":436,"indexable":205},"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":438,"label":439,"issuer":145,"region":146,"url":440,"description":441,"useCases":436,"indexable":205},"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":443,"label":444,"issuer":445,"region":366,"url":446,"description":447,"useCases":448,"indexable":205},"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":450,"label":451,"issuer":145,"region":146,"url":452,"description":453,"useCases":454,"indexable":205},"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":456,"label":457,"issuer":458,"region":366,"url":459,"description":460,"useCases":454,"indexable":205},"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":462,"label":463,"issuer":464,"region":359,"url":465,"description":466,"useCases":454,"indexable":205},"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":468,"label":469,"issuer":145,"region":146,"url":470,"description":471,"useCases":472,"indexable":205},"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":474,"label":475,"issuer":476,"region":366,"url":477,"description":478,"useCases":472,"indexable":205},"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":480,"label":481,"issuer":393,"region":222,"url":482,"description":483,"useCases":484,"indexable":205},"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":486,"label":487,"issuer":145,"region":146,"url":488,"description":489,"useCases":484,"indexable":205},"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":491,"label":492,"issuer":145,"region":146,"url":493,"description":494,"useCases":484,"indexable":205},"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":496,"label":497,"issuer":498,"region":146,"url":499,"description":500,"useCases":501,"indexable":205},"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":503,"label":504,"issuer":505,"region":366,"url":506,"description":507,"useCases":508,"indexable":205},"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":510,"label":511,"issuer":145,"region":146,"url":512,"description":513,"useCases":508,"indexable":205},"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":515,"label":516,"issuer":145,"region":146,"url":517,"description":518,"useCases":259,"indexable":205},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":520,"label":521,"issuer":522,"region":523,"url":524,"description":525,"useCases":526,"indexable":205},"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.",5,{"id":528,"label":529,"issuer":530,"region":146,"url":531,"description":532,"useCases":278,"indexable":205},"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":534,"label":535,"issuer":536,"region":146,"url":537,"description":538,"useCases":278,"indexable":205},"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":540,"label":541,"issuer":542,"region":222,"url":543,"description":544,"useCases":287,"indexable":205},"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":546,"label":547,"issuer":145,"region":146,"url":548,"description":549,"useCases":287,"indexable":205},"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":551,"label":552,"issuer":553,"region":366,"url":554,"description":555,"useCases":287,"indexable":205},"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.",1790598297874]