[{"data":1,"prerenderedAt":727},["ShallowReactive",2],{"uc-utility-billing-and-move-agent":3,"uc-regulations":518},{"useCase":4,"evidence":206,"blitsAiDeployments":366,"benchmarks":367,"indicative":403,"related":406,"indexability":516,"includeUnpublished":212},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":57,"macroEstimates":98,"feasibility":99,"implementation":114,"risk":157,"blitsAi":179,"faq":181,"related":194,"datePublished":200,"dateModified":200,"lastVerified":201,"changelog":202,"slug":205},"AI agent for utility billing, payments, meter readings and move in or move out","Utility billing and home moves","AI agents for utility billing and home moves","AI agents explain energy bills, take meter readings and payments, and handle home moves. PolyAI reports 67% containment at PG&E; Octopus Energy's Arlo got 76% CSAT.","published","An AI agent for energy and water customers that explains bills and tariffs, takes meter readings, sets up or changes payments, and handles move in and move out (final reads, closing one account and opening the next), across phone, messaging, email and the app, while anyone in payment difficulty, in a vulnerable situation or with a complaint is handed to a person.",[12,13,14,15,16],"energy billing chatbot","utility customer service agent","move in move out agent","meter reading assistant","energy supplier AI assistant",[18],"energy-and-utilities",[20,21],"customer-service","operations",[23,24,25,26],"conversational-agent","voice-agent","agentic-workflow","rag-knowledge-assistant",[28,29,30,31,32,33],"voice","web-chat","mobile-app","whatsapp","email","sms","customer-facing","supervised-agent","early-adopters","Alongside outage calls, utility contact centres handle a steady stream of routine account tasks:\nwhy is my bill so high, when will I be charged, here is my meter reading, I am moving house. Each\none needs the customer's own account, meter and tariff data, which is why static FAQs and old IVR\nmenus rarely finish the job. Volumes are also spiky. PolyAI reports that PG&E saw daily calls in the\ntens of thousands during weather emergencies, and Aydem Energy says its call volumes rise sharply in\nseasonal peaks, faster than it can add staff.\n\nHome moves are among the most involved routine tasks. The supplier needs the right date and final\nreadings, has to close one account and open the next without a gap in supply, and often has to\ndeal with an unknown new occupant. Errors here can turn into estimated bills, back billing and\ncomplaints months later. Billing is already the largest complaint category at the UK Energy\nOmbudsman.",[39],{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"The UK Energy Ombudsman reports that billing related disputes remain the most common complaint category, accounting for 58% of the 46,532 cases it accepted in the first half of 2026.","Energy Ombudsman H1 Data 2026","https://www.energyombudsman.org/news/energy-ombudsman-h1-data-2026",2026,"1. **Identify the customer and the account.** The agent verifies the customer and finds the supply\n   points, meters and tariffs involved, matching an inbound phone number to the account where the\n   rules allow.\n2. **Explain from the customer's own data.** Bill questions are answered from the actual bill lines,\n   readings, tariff and payment history, not from generic content, and the agent shows how the\n   amount was calculated.\n3. **Take readings and payments.** It validates a meter reading against the expected range, asks for\n   a photo when the value looks wrong, and sets up or changes a payment date or amount within the\n   limits the supplier allows.\n4. **Run the move as a workflow.** For a move out or move in it collects the date, final or opening\n   readings and the forwarding address, closes or opens the account and confirms each step, with a\n   human check before anything irreversible.\n5. **Answer policy questions from approved content.** Tariff terms, price cap rules and support\n   schemes come from retrieval over the supplier's approved documents.\n6. **Hand over at the right moments.** Signs of payment difficulty or vulnerability, disputes,\n   complaints and safety issues (such as a gas smell) go straight to a person or the emergency line,\n   with the conversation attached.",[46,47,48,49],"cost-to-serve","customer-experience","speed","inclusion-and-access",[51,52,53,54,55,56],"containment-rate","contact-deflection","interactions-handled","customer-satisfaction","customer-satisfaction-uplift","hours-saved",{"referenceOrg":58,"inputs":59,"formula":93,"currency":94,"period":95,"resultLabel":96,"caveat":97},"An energy retailer with 1 million residential accounts",[60,65,72,79,86],{"key":61,"label":62,"low":63,"high":63,"unit":61,"note":64},"accounts","Residential accounts",1000000,"The reference retailer.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"contactsPerAccount","Assisted contacts per account per year",1,2,"contacts per account per year","Editorial assumption, replace with your own contact volume.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"inScopeShare","Share of contacts about bills, payments, readings and moves",0.4,0.6,"fraction of contacts","Editorial assumption; billing is the largest complaint category at the UK Energy Ombudsman, but check your own contact reasons.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"containment","Share of in scope contacts the agent resolves",0.25,0.5,"fraction of in scope contacts","Conservative against the evidence on this page (PolyAI reports 67% containment at PG&E and Aydem Energy reports 75% of WhatsApp inquiries resolved), because moves and disputes are harder than FAQs.",{"key":87,"label":88,"low":89,"high":90,"unit":91,"note":92},"costPerContact","Cost of a human handled contact",4,8,"EUR per contact","Editorial assumption for a blended phone, email and chat contact. Replace with your own fully loaded cost.","accounts * contactsPerAccount * inScopeShare * containment * costPerContact","EUR","per year","Human handled contact cost avoided","Gross avoided contact cost only. It leaves out the cost of the AI and the billing system integration, fewer estimated bills and complaints from better readings and cleaner moves, and the peak capacity the agent adds during outages and price changes.",[],{"complexity":100,"complexityNote":101,"dataPrerequisites":102,"integrations":108},"medium","Answering is simple; acting on the account is where the work lies. Moves touch the billing system, meter data, industry registration processes and sometimes a credit check, and every write needs clear limits and a way back.",[103,104,105,106,107],"Account, bill line, tariff and payment data reachable through APIs","Meter data with expected reading ranges per meter","Approved tariff terms, regulatory rules and support scheme content","Vulnerability and priority services flags with the rules for using them","Contact reason data to choose the first intents",[109,110,111,112,113],"Billing and customer information system","Meter data management","Payments and direct debit platform","Industry registration or switching processes for moves","Contact centre platform for handover and callbacks",{"steps":115,"guardrails":131,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[116,119,122,125,128],{"title":117,"detail":118},"Start with readings, bill explanations and payment dates","These are high volume, low risk and easy to check. Octopus Energy started its email assistant on tariff renewals, payment dates and account details and kept complex complaints, sensitive cases and vulnerable customers with people.",{"title":120,"detail":121},"Ground every bill answer in the account","Give the agent tools that return the bill lines, readings and tariff for this customer, and make it show the calculation. Generic answers about \"how bills work\" do not reduce repeat contact.",{"title":123,"detail":124},"Treat moves as a workflow with checkpoints","Model move in and move out as steps with validation (date, readings, address) and a confirmation at each step, and keep a human approval for account closure until error rates are known.",{"title":126,"detail":127},"Build vulnerability detection into the flow","Define the phrases and signals (missed payments, health conditions, distress) that route to a specialist, and check the Priority Services Register or its local equivalent before any change.",{"title":129,"detail":130},"Plan for the peak","Outages and price changes bring the surges. Make sure the agent can carry outage updates at volume, as Aydem Energy and PG&E do, so billing questions still get through.",[132,133,134,135,136],"No disconnection, debt or payment plan decisions by the agent; those go to trained staff","Payment changes only within limits the supplier sets, with confirmation to the customer","Readings outside the expected range rejected or sent for review, never billed as given","Safety issues such as a gas smell routed to the emergency line immediately","AI disclosure on every channel and an easy route to a person","Specialists handle payment difficulty, vulnerable customers, disputes, complaints and failed moves. Operations leads approve each new intent and action, and a sample of contained conversations and AI written emails is reviewed every week. Octopus Energy says its human experts keep checking the messages its assistant sends.",[139,140,141,142,143],"Containment per intent, counting repeat contact within seven days as not contained","Estimated bills and back billing cases after agent handled moves","Customer satisfaction for AI handled versus human handled contacts","Handover rate and reasons, including vulnerability referrals","Complaints that mention the assistant",[145,148,151,154],{"title":146,"detail":147},"Wrong reading, wrong bill","A mistyped or misread meter value flows into billing. Validate against expected ranges and ask for a photo when in doubt.",{"title":149,"detail":150},"Broken moves","The old account is closed before the new one is confirmed, or readings are missing. Use a workflow with checkpoints and human approval for closures.",{"title":152,"detail":153},"Missing vulnerability","A customer in difficulty is handled like any other and put on a payment plan they cannot keep. Detect signals early and hand over.",{"title":155,"detail":156},"Generic bill explanations","The agent explains how bills work in general instead of this bill, and the customer calls anyway. Give it the bill data and measure repeat contact.",{"euAiAct":158,"regulations":161,"guidance":165,"controls":172,"incidents":178},{"tier":159,"basis":160},"context-dependent","A customer service agent for bills, readings and moves falls under the transparency duty for systems that interact with people (Article 50(1)): customers must be told they are talking to AI. If the agent assesses creditworthiness, for example to set a deposit when a new customer moves in, that part falls under Annex III point 5(b) and is high risk; keep credit decisions in separately governed systems. The agent is not a safety component in the operation of the gas, water or electricity supply (Annex III point 2), so safety reports such as a gas smell go straight to the emergency line rather than being handled by the agent.",[162,163,164],"eu-ai-act","gdpr","uk-gdpr",[166],{"title":167,"issuer":168,"region":169,"url":170,"note":171},"Ethical AI use in the energy sector","Ofgem","europe","https://www.ofgem.gov.uk/guidance/ethical-ai-use-energy-sector","Good practice guidance for energy companies, updated in May 2026, including a section on AI in consumer interactions with transparency and proportionate explainability.",[173,174,175,176,177],"AI disclosure and a documented route to a human on every channel","Action allow list with limits for payments, readings and account changes","Audit trail of every account change the agent made, with the verification used","Vulnerability and priority services checks before any change","Change control and regression tests for each new intent",[],{"howToBuild":180},"On Blits.ai this is an **AI agent** with **custom functions** that call the billing, meter data\nand payment systems over REST, so every bill answer is grounded in the customer's own data. Tariff\nterms and support schemes sit in a **knowledge base** with hybrid retrieval. Moves run as an\n**agentic workflow** with validation steps and **human in the loop** approval before an account is\nclosed, while a **flow** handles verification and the fixed steps of a meter reading.\n\nThe same agent serves **voice**, **WhatsApp**, **SMS**, **email**, **web chat** and the supplier's\nown app through the **REST API channel**, which lets outage and price change peaks move to messaging. **Guardrails** stop the agent from making\npayment plan or disconnection decisions, **PII masking** protects account data, and **human\nhandover** routes payment difficulty and vulnerability to specialists. **Test suites** replay moves\nand bill questions on every change, and **analytics** show containment and handover reasons per\nintent.",[182,185,188,191],{"question":183,"answer":184},"Do customers accept AI answers from their energy supplier?","Early evidence suggests so, when the AI is transparent and a human is one reply away. Octopus Energy reports that its email assistant Arlo scored 76% customer satisfaction in a trial, against 72% for comparable human replies, with every AI written email labelled.",{"question":186,"answer":187},"What share of utility calls can an AI agent resolve?","PolyAI reports 67% containment for PG&E's voice agent, which handles outage and billing calls, and Aydem Energy reports that 75% of the inquiries reaching its WhatsApp assistant are resolved without an agent. Moves and disputes are likely to resolve less often than outage updates and FAQs.",{"question":189,"answer":190},"Should the agent handle move in and move out end to end?","It can collect everything and run the steps, but keep a human check before closing an account until you know the error rate. Public outcome data for AI handled moves is still scarce; PolyAI lists start and stop service at PG&E as a use case being built, not as a result.",{"question":192,"answer":193},"Can AI help with bill confusion without a chatbot?","Yes. EDF's AI Bill Explainer breaks each bill down step by step in the customer account, and EDF reports 5.6% fewer billing related contacts among customers who used it in the pilot.",[195,196,197,198,199],"bill-explanation-and-billing-dispute-agent","collections-and-hardship-agent","outbound-reminder-and-confirmation-agent","first-line-contact-centre-agent","complaints-handling-agent","2026-09-27","2026-09-26",[203],{"date":200,"note":204},"First published","utility-billing-and-move-agent",[207,239,273,309,340],{"title":208,"useCases":209,"organization":210,"vendors":214,"summary":215,"stage":216,"year":43,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":229,"sources":230,"verification":234,"grade":236,"id":237,"organizationSlug":238},"EDF: AI Bill Explainer that walks customers through their energy bill",[205],{"name":211,"anonymized":212,"country":213,"region":169,"industry":18},"EDF",false,"GB",[],"EDF in the UK launched Bill Explainer, an AI powered tool in the customer account that breaks each bill down step by step with explanations tailored to the individual account. After a pilot made available to more than 58,000 customers, EDF reports that billing related contacts fell by 5.6% among customers who used it, and it announced a rollout to its 3.4 million residential and small business customers.","production",[],[219],"en",[221],{"kpi":52,"value":222,"unit":223,"qualifier":224,"period":225,"claimant":226,"quote":227,"sourceUrl":228},5.6,"percent","exact","billing related contacts among pilot customers who used the tool","organization","Early results indicate a positive shift in how customers engage with their bills, with billing related contacts decreasing by 5.6% among those who used the tool.","https://www.edfenergy.com/media-centre/edf-harnesses-ai-help-customers-overcome-one-their-most-common-questions",true,[231],{"url":228,"title":232,"publisher":211,"date":233},"EDF harnesses AI to help customers overcome one of their most common questions","2026-05-07",{"level":235,"checkedAt":201},"source-verified","B","edf-ai-bill-explainer",null,{"title":240,"useCases":241,"organization":242,"vendors":244,"summary":248,"stage":216,"year":43,"channels":249,"languages":250,"metrics":251,"outcomeDisclosed":229,"sources":263,"verification":271,"grade":236,"id":272,"organizationSlug":238},"Octopus Energy: Arlo, an AI assistant that answers routine customer emails",[205],{"name":243,"anonymized":212,"country":213,"region":169,"industry":18},"Octopus Energy",[245],{"name":246,"role":247},"Kraken","platform","Octopus Energy and Kraken built Arlo, an AI assistant that answers straightforward customer emails about tariff renewals, payment dates and account details. Every AI written email is labelled, the customer can ask for a human at any time, and vulnerable customers, sensitive cases and complex complaints always go to the human team. In a three month UK trial Arlo handled around 8,000 emails a week (4% of customer emails) and scored 76% customer satisfaction against 72% for comparable human replies; Octopus then started a wider rollout while human experts keep reviewing its messages.",[32],[219],[252,257],{"kpi":54,"value":253,"unit":223,"qualifier":224,"period":254,"claimant":226,"quote":255,"sourceUrl":256},76,"three month trial, versus 72% for comparable human replies","Arlo achieved a 76% customer satisfaction score, beating comparable responses from human advisors, which scored 72%.","https://octopus.energy/press/more-news-press-releases/octopus-energy-s-ai-trial-wins-customer-approval/",{"kpi":53,"value":258,"unit":259,"qualifier":260,"period":261,"claimant":226,"quote":262,"sourceUrl":256},8000,"count","approximately","per week during the trial","During the three-month trial, Arlo handled around 8,000 emails a week",[264,267],{"url":256,"title":265,"publisher":243,"date":266},"Octopus Energy's AI trial wins customer approval","2026-07-07",{"url":268,"title":269,"publisher":243,"date":270},"https://octopus.energy/blog/arlo-ai-assistant/","How Arlo, our AI assistant, is helping our humans help you","2026-08-06",{"level":235,"checkedAt":201},"octopus-energy-arlo-email-assistant",{"title":274,"useCases":275,"organization":276,"vendors":279,"summary":286,"stage":216,"year":287,"channels":288,"languages":289,"metrics":291,"outcomeDisclosed":229,"sources":302,"verification":306,"grade":307,"id":308,"organizationSlug":238},"Aydem Energy: generative AI WhatsApp assistant for bills, meter readings and outages",[205],{"name":277,"anonymized":212,"country":278,"region":169,"industry":18},"Aydem Energy","TR",[280,283],{"name":281,"role":282},"Softtech","integrator",{"name":284,"role":285},"Microsoft","model-provider","Aydem Energy, which supplies electricity to around 6 million customers in Türkiye, built a WhatsApp assistant on Azure OpenAI with Softtech. It opens with data protection consent and an AI disclosure, matches the caller's phone number to CRM data, gives location specific outage updates, guides meter reading submission, checks outstanding bills and processes compensation claims, and escalates urgent or angry customers to a human. Aydem reports about 1,000 inquiries a day, 75% of them resolved without an agent, and it restricts the assistant to the knowledge relevant to each scenario to control cost.",2024,[31],[290],"tr",[292,297],{"kpi":51,"value":293,"unit":223,"qualifier":224,"period":294,"claimant":226,"quote":295,"sourceUrl":296},75,"WhatsApp inquiries","Of the inquiries that come through WhatsApp, 75% are fully resolved by the digital assistant without the need for live agent support","https://www.microsoft.com/en/customers/story/20845-aydem-energy-azure-open-ai-service",{"kpi":53,"value":298,"unit":259,"qualifier":260,"period":299,"claimant":300,"quote":301,"sourceUrl":296},1000,"per day","vendor","Today, the digital assistant manages about 1,000 customer inquiries daily through WhatsApp, which represents 10% of total customers calling customer service.",[303],{"url":296,"title":304,"publisher":284,"date":305},"Aydem Energy manages multiple-fold seasonal call surges with an Azure OpenAI-powered digital assistant","2024-12-27",{"level":235,"checkedAt":201},"C","aydem-energy-whatsapp-assistant",{"title":310,"useCases":311,"organization":312,"vendors":316,"summary":319,"stage":320,"year":287,"channels":321,"languages":322,"metrics":324,"outcomeDisclosed":229,"sources":335,"verification":338,"grade":307,"id":339,"organizationSlug":238},"PG&E: generative voice agent for outage and billing calls",[205],{"name":313,"anonymized":212,"country":314,"region":315,"industry":18},"Pacific Gas and Electric Company","US","north-america",[317],{"name":318,"role":247},"PolyAI","PG&E, which receives about 16 million calls a year with sharp peaks during storms and outages, deployed a PolyAI voice agent named Peggy. It authenticates customers, gives location based outage updates, answers billing questions and FAQs in English and Spanish and texts links for self service, with integrations into Oracle, Cisco and in house systems. PolyAI reports 67% overall containment, 35,000 labour hours saved and a 22% increase in CSAT on outage calls. Start and stop service, appointment setting and expanded billing were named as the next use cases, not yet reported as live.","scaled",[28,33],[219,323],"es",[325,329,332],{"kpi":51,"value":326,"unit":223,"qualifier":224,"claimant":300,"quote":327,"sourceUrl":328},67,"35,000 labor hours have been saved by the PolyAI agent, increasing CSAT by 22% and achieving 67% containment, 6% higher than the legacy IVR","https://poly.ai/customers/pge",{"kpi":56,"value":330,"unit":331,"qualifier":224,"claimant":300,"quote":327,"sourceUrl":328},35000,"hours",{"kpi":55,"value":333,"unit":223,"qualifier":224,"period":334,"claimant":300,"quote":327,"sourceUrl":328},22,"outage calls",[336],{"url":328,"title":337,"publisher":318},"How Pacific Gas and Electric saved 35,000 labor hours with PolyAI",{"level":235,"checkedAt":201},"pge-polyai-voice-agent-billing-and-outages",{"title":341,"useCases":342,"organization":343,"vendors":347,"summary":349,"stage":320,"year":350,"channels":351,"languages":352,"metrics":353,"outcomeDisclosed":229,"sources":360,"verification":364,"grade":307,"id":365,"organizationSlug":238},"Dubai Electricity and Water Authority: Rammas customer service chatbot",[205],{"name":344,"anonymized":212,"country":345,"region":346,"industry":18},"Dubai Electricity and Water Authority","AE","middle-east",[348],{"name":284,"role":247},"DEWA introduced its Rammas chatbot in 2017, which Microsoft describes as the first chatbot based on Microsoft AI at a government utility, and later integrated Azure OpenAI Service to make its answers more accurate and more natural. Microsoft reports that Rammas has responded to more than 9.2 million customer inquiries autonomously since launch, for a utility with more than 1.2 million customers. DEWA also runs AI based high water usage alerts, and is exploring voice conversations for customers.",2017,[],[],[354],{"kpi":53,"value":355,"unit":259,"qualifier":356,"period":357,"claimant":300,"quote":358,"sourceUrl":359},9200000,"at-least","cumulative since 2017","Since then, Rammas has responded to over 9.2 million customer inquiries autonomously.","https://www.microsoft.com/en/customers/story/1803548215767581643-dewa-azure-ai-services-government-en-united-arab-emirates",[361],{"url":359,"title":362,"publisher":284,"date":363},"DEWA pioneers the use of Azure AI Services in delivering utility services","2024-08-21",{"level":235,"checkedAt":201},"dewa-rammas-customer-chatbot",0,[368,376,383,388,393,398],{"kpi":53,"label":369,"unit":259,"aggregate":212,"higherIsBetter":229,"n":370,"nUpTo":366,"median":258,"min":298,"max":355,"byClaimant":371,"vendorOnly":212,"points":372},"Interactions handled",3,{"organization":68,"vendor":69,"regulator":366,"independent":366},[373,374,375],{"evidenceId":365,"organization":344,"value":355,"qualifier":356,"claimant":300,"grade":307,"pooled":229},{"evidenceId":272,"organization":243,"value":258,"qualifier":260,"claimant":226,"grade":236,"pooled":229},{"evidenceId":308,"organization":277,"value":298,"qualifier":260,"claimant":300,"grade":307,"pooled":229},{"kpi":51,"label":377,"unit":223,"aggregate":229,"higherIsBetter":229,"n":69,"nUpTo":366,"median":378,"min":326,"max":293,"byClaimant":379,"vendorOnly":212,"points":380},"Containment rate",71,{"organization":68,"vendor":68,"regulator":366,"independent":366},[381,382],{"evidenceId":308,"organization":277,"value":293,"qualifier":224,"claimant":226,"grade":307,"pooled":229},{"evidenceId":339,"organization":313,"value":326,"qualifier":224,"claimant":300,"grade":307,"pooled":229},{"kpi":52,"label":384,"unit":223,"aggregate":229,"higherIsBetter":229,"n":68,"nUpTo":366,"median":222,"min":222,"max":222,"byClaimant":385,"vendorOnly":212,"points":386},"Contact deflection",{"organization":68,"vendor":366,"regulator":366,"independent":366},[387],{"evidenceId":237,"organization":211,"value":222,"qualifier":224,"claimant":226,"grade":236,"pooled":229},{"kpi":54,"label":389,"unit":223,"aggregate":229,"higherIsBetter":229,"n":68,"nUpTo":366,"median":253,"min":253,"max":253,"byClaimant":390,"vendorOnly":212,"points":391},"Customer satisfaction",{"organization":68,"vendor":366,"regulator":366,"independent":366},[392],{"evidenceId":272,"organization":243,"value":253,"qualifier":224,"claimant":226,"grade":236,"pooled":229},{"kpi":56,"label":394,"unit":331,"aggregate":212,"higherIsBetter":229,"n":68,"nUpTo":366,"median":330,"min":330,"max":330,"byClaimant":395,"vendorOnly":229,"points":396},"Hours saved",{"organization":366,"vendor":68,"regulator":366,"independent":366},[397],{"evidenceId":339,"organization":313,"value":330,"qualifier":224,"claimant":300,"grade":307,"pooled":229},{"kpi":55,"label":399,"unit":223,"aggregate":229,"higherIsBetter":229,"n":68,"nUpTo":366,"median":333,"min":333,"max":333,"byClaimant":400,"vendorOnly":229,"points":401},"Satisfaction uplift",{"organization":366,"vendor":68,"regulator":366,"independent":366},[402],{"evidenceId":339,"organization":313,"value":333,"qualifier":224,"claimant":300,"grade":307,"pooled":229},{"low":404,"high":405},400000,4800000,[407,427,448,464,496],{"slug":195,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":413,"patterns":415,"audience":34,"autonomy":35,"adoptionStage":36,"segment":416,"evidenceCount":89,"publicEvidenceCount":89,"organizations":417,"bestGrade":236,"headline":422,"lastVerified":200,"indexable":229},"AI agent for telecom bill explanation and billing disputes","Bill explanation and disputes","An AI agent that explains a customer's telecom bill line by line, in plain language and on any channel, answers why a charge changed or appeared, corrects clear errors within set limits and opens a billing dispute with the evidence attached when a human has to decide.",[412],"telecommunications",[20,414],"case-management",[23,26,25,24],"front-office",[418,419,420,421],"BT Group","Mobily","Verizon","Vodafone",{"kpi":423,"label":424,"unit":223,"n":68,"nUpTo":366,"kind":425,"value":426,"qualifier":224,"claimant":226,"organization":421,"vendorReported":212},"first-contact-resolution","First contact resolution","reported",60,{"slug":196,"title":428,"shortTitle":429,"definition":430,"status":9,"industries":431,"functions":437,"patterns":439,"audience":34,"autonomy":35,"adoptionStage":36,"segment":441,"evidenceCount":370,"publicEvidenceCount":69,"organizations":442,"bestGrade":307,"headline":445,"lastVerified":200,"indexable":229},"AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[432,433,434,412,18,435,436],"cross-industry","banking","payments","automotive","professional-services",[438,20],"collections-and-recovery",[24,23,25,440],"classification-and-routing","lending",[443,444],"Day Knight & Associates","SameDay Auto Finance",{"kpi":446,"label":447,"unit":223,"n":69,"nUpTo":366,"kind":425,"value":293,"qualifier":224,"claimant":300,"organization":444,"vendorReported":229},"cost-reduction","Cost reduction",{"slug":197,"title":449,"shortTitle":450,"definition":451,"status":9,"industries":452,"functions":455,"patterns":456,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":458,"publicEvidenceCount":89,"organizations":459,"bestGrade":236,"headline":238,"lastVerified":201,"indexable":229},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[432,453,454],"healthcare","government",[20,21],[24,23,25,457],"prediction-and-scoring",5,[460,461,462,463],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health",{"slug":198,"title":465,"shortTitle":466,"definition":467,"status":9,"industries":468,"functions":472,"patterns":473,"audience":34,"autonomy":35,"adoptionStage":474,"segment":416,"evidenceCount":475,"publicEvidenceCount":476,"organizations":477,"bestGrade":236,"headline":492,"lastVerified":200,"indexable":229},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[432,433,434,412,469,470,471],"travel-and-hospitality","retail-and-ecommerce","wealth-and-asset-management",[20],[23,24,26,440],"mainstream",25,18,[478,479,480,481,418,482,483,484,485,486,419,487,488,489,490,491,421],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany",{"kpi":51,"label":377,"unit":223,"n":493,"nUpTo":366,"kind":494,"value":495,"qualifier":224,"claimant":238,"organization":238,"vendorReported":212},7,"median",47,{"slug":199,"title":497,"shortTitle":498,"definition":499,"status":9,"industries":500,"functions":502,"patterns":504,"audience":507,"autonomy":508,"adoptionStage":36,"segment":509,"evidenceCount":69,"publicEvidenceCount":69,"organizations":510,"bestGrade":236,"headline":512,"lastVerified":200,"indexable":229},"AI agent for complaints recognition, investigation and response","Complaints handling","An AI agent that recognizes when a customer interaction is a complaint, logs it against the regulatory definition, classifies its root cause and severity, gathers the evidence, drafts the acknowledgement and the response for a human handler to approve, and tracks every statutory deadline until the case is closed.",[432,433,434,501,412],"insurance",[414,20,503],"regulatory-compliance",[440,505,506,25,26],"summarization","content-generation","employee-facing","copilot","middle-office",[511,487],"Lloyds Banking Group",{"kpi":513,"label":514,"unit":515,"n":68,"nUpTo":366,"kind":425,"value":458,"qualifier":260,"claimant":226,"organization":511,"vendorReported":212},"time-saved-per-task","Time saved per task","minutes",{"indexable":229,"reasons":517},[],[519,525,530,538,545,551,557,563,571,577,584,590,597,604,610,615,622,628,634,640,646,652,658,663,668,675,681,686,692,698,704,710,716,721],{"id":162,"label":520,"issuer":521,"region":169,"url":522,"description":523,"useCases":524,"indexable":229},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":163,"label":526,"issuer":521,"region":169,"url":527,"description":528,"useCases":529,"indexable":229},"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":531,"label":532,"issuer":533,"region":534,"url":535,"description":536,"useCases":537,"indexable":229},"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":539,"label":540,"issuer":541,"region":315,"url":542,"description":543,"useCases":544,"indexable":229},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","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":546,"label":547,"issuer":521,"region":169,"url":548,"description":549,"useCases":550,"indexable":229},"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":164,"label":552,"issuer":553,"region":169,"url":554,"description":555,"useCases":556,"indexable":229},"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":558,"label":559,"issuer":560,"region":169,"url":561,"description":562,"useCases":495,"indexable":229},"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.",{"id":564,"label":565,"issuer":566,"region":567,"url":568,"description":569,"useCases":570,"indexable":229},"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":572,"label":573,"issuer":574,"region":567,"url":575,"description":576,"useCases":475,"indexable":229},"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.",{"id":578,"label":579,"issuer":580,"region":534,"url":581,"description":582,"useCases":583,"indexable":229},"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":585,"label":586,"issuer":587,"region":315,"url":588,"description":589,"useCases":583,"indexable":229},"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":591,"label":592,"issuer":593,"region":169,"url":594,"description":595,"useCases":596,"indexable":229},"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":598,"label":599,"issuer":600,"region":534,"url":601,"description":602,"useCases":603,"indexable":229},"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":605,"label":606,"issuer":521,"region":169,"url":607,"description":608,"useCases":609,"indexable":229},"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":611,"label":612,"issuer":521,"region":169,"url":613,"description":614,"useCases":609,"indexable":229},"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":616,"label":617,"issuer":618,"region":315,"url":619,"description":620,"useCases":621,"indexable":229},"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":623,"label":624,"issuer":521,"region":169,"url":625,"description":626,"useCases":627,"indexable":229},"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":629,"label":630,"issuer":631,"region":315,"url":632,"description":633,"useCases":627,"indexable":229},"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":635,"label":636,"issuer":637,"region":534,"url":638,"description":639,"useCases":627,"indexable":229},"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":641,"label":642,"issuer":521,"region":169,"url":643,"description":644,"useCases":645,"indexable":229},"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":647,"label":648,"issuer":649,"region":315,"url":650,"description":651,"useCases":645,"indexable":229},"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":653,"label":654,"issuer":566,"region":567,"url":655,"description":656,"useCases":657,"indexable":229},"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":659,"label":660,"issuer":521,"region":169,"url":661,"description":662,"useCases":657,"indexable":229},"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":664,"label":665,"issuer":521,"region":169,"url":666,"description":667,"useCases":657,"indexable":229},"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":669,"label":670,"issuer":671,"region":169,"url":672,"description":673,"useCases":674,"indexable":229},"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":676,"label":677,"issuer":678,"region":315,"url":679,"description":680,"useCases":90,"indexable":229},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":682,"label":683,"issuer":521,"region":169,"url":684,"description":685,"useCases":90,"indexable":229},"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":687,"label":688,"issuer":521,"region":169,"url":689,"description":690,"useCases":691,"indexable":229},"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":693,"label":694,"issuer":695,"region":346,"url":696,"description":697,"useCases":458,"indexable":229},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":699,"label":700,"issuer":701,"region":169,"url":702,"description":703,"useCases":89,"indexable":229},"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":705,"label":706,"issuer":707,"region":169,"url":708,"description":709,"useCases":89,"indexable":229},"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":711,"label":712,"issuer":713,"region":567,"url":714,"description":715,"useCases":370,"indexable":229},"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":717,"label":718,"issuer":521,"region":169,"url":719,"description":720,"useCases":370,"indexable":229},"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":722,"label":723,"issuer":724,"region":315,"url":725,"description":726,"useCases":370,"indexable":229},"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.",1790598296168]