[{"data":1,"prerenderedAt":695},["ShallowReactive",2],{"uc-bill-explanation-and-billing-dispute-agent":3,"uc-regulations":491},{"useCase":4,"evidence":206,"blitsAiDeployments":342,"benchmarks":343,"indicative":365,"related":368,"indexability":489,"includeUnpublished":213},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":51,"macroEstimates":84,"feasibility":85,"implementation":99,"risk":145,"blitsAi":182,"faq":184,"related":194,"datePublished":201,"dateModified":201,"lastVerified":201,"changelog":202,"slug":205},"AI agent for telecom bill explanation and billing disputes","Bill explanation and disputes","AI agent for telecom bill questions and disputes","AI agents explain telecom bills line by line and route disputes to people. BT Group's generative AI platform behind EE's Aimee explains billing charges.","published","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.",[12,13,14,15,16],"bill explainer","billing inquiry chatbot","bill shock assistant","telco billing assistant","billing dispute intake",[18],"telecommunications",[20,21],"customer-service","case-management",[23,24,25,26],"conversational-agent","rag-knowledge-assistant","agentic-workflow","voice-agent",[28,29,30,31],"mobile-app","web-chat","whatsapp","voice","customer-facing","supervised-agent","early-adopters","front-office","\"Why is my bill higher this month?\" is a routine, high volume question for telecom operators.\nNiCE Cognigy's Mobily case study, for example, lists billing questions first among the\nrepetitive requests Mobily's contact centres handled before it automated them. Bills combine prorated plan changes, roaming and\npremium charges, device instalments, discounts that expire and annual price rises, and the lines\noften come from different systems. The customer sees one total that moved and no explanation, so\nthey call.\n\nHuman agents then spend minutes opening the billing system, the order history and the tariff\nrules to reconstruct what happened. Answers can differ by agent, credits can be given\ninconsistently, and a customer who feels misled can become a complaint, a regulator escalation or\na churn risk. A chatbot without access to the customer's own bill data can only describe how\nbills work in general; it cannot explain this customer's charges.",[],"1. **Authenticate and fetch the bill.** After login or a one time passcode, the agent reads the\n   current and previous bills, recent orders and plan changes through read only billing APIs.\n2. **Explain the difference.** It compares the bills, identifies what changed (a prorated\n   upgrade, roaming, an ended discount, a price rise) and explains each line in plain language,\n   citing the tariff or contract term from approved content.\n3. **Fix what is clearly wrong, within limits.** Where a rule shows an obvious error, such as a\n   duplicate charge, the agent applies a correction up to a set amount and confirms it.\n4. **Open a dispute when judgment is needed.** Anything above the limit, contested or unclear\n   becomes a dispute case with the bill lines, the explanation given and the customer's reason,\n   routed to a billing specialist. Complaint signals follow the complaint process.\n5. **Hand over with context.** A human who takes over sees the bill analysis and the\n   conversation, so the customer does not explain again.",[40,41,42,43],"cost-to-serve","customer-experience","compliance","employee-productivity",[45,46,47,48,49,50],"first-contact-resolution","automation-rate","containment-rate","response-time-reduction","interactions-handled","customer-satisfaction",{"referenceOrg":52,"inputs":53,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A mobile and broadband operator with 5 million consumer customers",[54,59,66,72],{"key":55,"label":56,"low":57,"high":57,"unit":55,"note":58},"customers","Consumer customers",5000000,"The reference operator.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"billContactRate","Billing contacts per customer per year",0.3,0.6,"contacts per customer per year","Editorial assumption, replace with the billing share of your contact reason report.",{"key":67,"label":68,"low":62,"high":69,"unit":70,"note":71},"resolvedShare","Share of billing contacts the agent resolves without a human",0.4,"fraction of billing contacts","Editorial assumption. BT Group reports automation success approaching 50% on several unnamed types of Aimee journey, not specifically billing; replace with your own billing containment, keeping in mind that disputes and complaints must still reach people.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerContact","Cost of a human handled billing contact",4,8,"USD per contact","Editorial assumption for a blended chat and phone contact, replace with your own fully loaded cost.","customers * billContactRate * resolvedShare * costPerContact","USD","per year","Human handled billing contact cost avoided","Gross avoided contact cost only. It leaves out the cost of the AI and the billing integrations, credits the agent gives within its limits, fewer complaints and regulator escalations, and the retention effect of customers who understand their bill.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Explaining a bill well needs clean, read only access to billing, order and tariff data, which in many operators sit in several legacy systems. Corrections and disputes add write access and financial limits, which need their own controls and audit.",[89,90,91,92],"Structured bill data per line item for at least the last two bills","Current and historical tariff, discount and price rise rules in approved content","A billing contact reason report with volumes per cause","A written policy on which corrections the agent may make and up to what amount",[94,95,96,97,98],"Billing and charging system (read, and write for corrections)","Order management and CRM for plan changes and history","Identity and authentication (app login, one time passcode)","Case or dispute management for billing disputes","Contact centre platform for handover with context",{"steps":100,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[101,104,107,110,113,116],{"title":102,"detail":103},"Map the top reasons bills change","From the contact reason report and a sample of calls, list the ten most common reasons a bill differs from the last one (prorating, roaming, expired discounts, price rises) and write the explanation and evidence for each.",{"title":105,"detail":106},"Build a bill comparison tool, not a prompt","Give the agent a function that returns the structured difference between two bills. The model explains; the numbers come from the billing system, never from the model's arithmetic.",{"title":108,"detail":109},"Set correction limits and dispute routing","Agree with finance which errors the agent may correct and up to what amount, and route everything else to a dispute case with the bill lines and the customer's reason attached.",{"title":111,"detail":112},"Ground every explanation in approved terms","Load current tariffs, contract terms and price rise notices with owners and review dates, and make the agent refuse rather than guess when a charge is not covered.",{"title":114,"detail":115},"Test on real bills","Replay anonymised bills with known causes, including edge cases such as mid cycle plan changes and roaming, and check each explanation against the correct answer before launch and on every change.",{"title":117,"detail":118},"Launch in the app, then widen","Start where customers are already logged in, measure first contact resolution and repeat contacts per cause, then add messaging and voice.",[120,121,122,123,124],"Figures and dates come only from billing system tools, never from model generated arithmetic","Corrections only within documented amount limits, with every credit logged","Complaint and vulnerability signals route to the complaint process and a human","Answers about terms only from approved tariff and contract content, with refusal when not covered","Masking of payment card data and personal data before text reaches a model or the logs","Billing specialists decide every dispute above the agent's limit and every contested charge. A quality team reviews a weekly sample of explanations against the bill data and signs off new correction rules and price rise explanations before they go live.",[127,128,129,130,131],"First contact resolution for billing contacts, counting a repeat billing contact within 30 days as unresolved","Share of billing conversations resolved without a human, per cause","Accuracy of explanations on a weekly checked sample","Credits issued by the agent, in count and value, against limits","Billing complaints and regulator escalations per 10,000 customers",[133,136,139,142],{"title":134,"detail":135},"Confident wrong numbers","The model does its own arithmetic and explains a charge that does not exist. Keep all numbers in tools and test explanations against real bills.",{"title":137,"detail":138},"Explaining away a genuine error","The agent justifies an overcharge because a rule seems to allow it. Give it a clear path to open a dispute and measure disputes upheld later.",{"title":140,"detail":141},"Credits as a containment tactic","Goodwill credits used to end conversations cost more than the contacts saved. Log and cap credits, and review them weekly.",{"title":143,"detail":144},"Price rise conversations without care","Customers angry about a price rise need their rights explained, including any right to exit. Route those signals to trained people.",{"euAiAct":146,"regulations":149,"guidance":155,"controls":171,"incidents":177},{"tier":147,"basis":148},"limited","A customer facing assistant must be designed so that people know they are interacting with AI (Article 50(1)). Explaining bills, correcting clear errors and opening disputes are not listed in Annex III. The tier changes only if the system is also used to evaluate customers' creditworthiness, for example to set credit limits, which Annex III point 5(b) lists as high risk.",[150,151,152,153,154],"eu-ai-act","gdpr","telecom-consumer-rules","pci-dss","eecc",[156,162,166],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"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":163,"issuer":158,"region":159,"url":164,"note":165},"European Electronic Communications Code (Directive (EU) 2018/1972)","https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32018L1972","Sets EU end user rights for electronic communications contracts, including contract information and consumption monitoring (Article 102(5)), the right to exit without cost when the provider changes contract conditions (Article 105(4)) and itemised billing (Annex VI Part A), which billing explanations must be consistent with.",{"title":167,"issuer":168,"region":159,"url":169,"note":170},"Quicker complaints resolution for telecoms customers, under new Ofcom rules","Ofcom","https://www.ofcom.org.uk/phones-and-broadband/service-quality/quicker-complaints-resolution-for-telecoms-customers-under-new-ofcom-rules","From 8 April 2026, UK providers must tell customers about their right to independent dispute resolution when a complaint is still unresolved after six weeks (previously eight), so an agent must not hold a billing dispute in automation.",[172,173,174,175,176],"AI disclosure at the start of every conversation","Documented correction limits with finance sign off and an audit trail per credit","Regression tests on real bill scenarios for every change to prompts, tools or model","Complaint recognition that starts statutory complaint clocks and dispute resolution letters","Monthly review of disputes that were upheld after the agent's explanation",[178],{"title":179,"url":180,"note":181},"Incident 639: Air Canada chatbot reportedly provides inaccurate bereavement fare information, leading to customer overpayment","https://incidentdatabase.ai/cite/639/","A Canadian small claims tribunal ordered the airline to pay damages after its chatbot gave wrong fare information, a reminder that an operator owns every billing explanation its agent gives.",{"howToBuild":183},"On Blits.ai this is an **AI agent** with **custom functions** that call the billing, order and\ncase systems through REST, including a bill comparison function that returns structured\ndifferences so the model explains but never calculates. A **knowledge base** with hybrid\nretrieval holds only approved tariff and contract content. A **flow** runs corrections and\ndispute creation as fixed steps with amount limits, backed by custom functions that call the\noperator's identity and case systems.\n\nThe same agent runs in **web chat, WhatsApp and voice**, and inside the operator's mobile app\nthrough the **REST or WebSocket API channel**. **Guardrails** check input and output, **PII\nmasking** and card number tokenization run at the gateway, a flow gets the conversation history\n(optionally AI summarized) and the **agent handover** block escalates the conversation to a\nbilling specialist, who can also take over live from the admin console, and **test suites**\nreplay real bill scenarios on every change. Analytics dashboards with custom widgets track\noutcomes per billing cause, and the platform is model agnostic with EU and UAE data residency.",[185,188,191],{"question":186,"answer":187},"How many billing questions can an AI agent resolve?","It depends on how much of the bill it can see and explain. BT Group reports automation success approaching 50% on several types of journey in its EE assistant Aimee, without naming them, and says generative AI on the same platform gives detailed explanations of billing charges. Vodafone reports that initial SuperTOBi tests at one call centre showed a 50% improvement in first time resolution of critical journeys such as complex billing inquiries.",{"question":189,"answer":190},"Should the agent be allowed to give credits?","Only for clear errors, within an amount limit agreed with finance, with every credit logged. Contested or larger amounts should become a dispute case for a human, so the agent never uses credits to end a conversation.",{"question":192,"answer":193},"Is a billing assistant high risk under the EU AI Act?","No. Explaining bills and handling disputes is not listed in Annex III, so it is a limited risk system with an Article 50 duty to tell people they are talking to AI. It would need a new assessment if it were also used to evaluate customers' creditworthiness, for example to set credit limits, which Annex III lists as high risk.",[195,196,197,198,199,200],"device-and-connectivity-troubleshooting-agent","complaints-handling-agent","churn-prediction-and-retention-offers","plan-upgrade-and-sales-assistant","collections-and-hardship-agent","utility-billing-and-move-agent","2026-09-27",[203],{"date":201,"note":204},"First published","bill-explanation-and-billing-dispute-agent",[207,237,274,311],{"title":208,"useCases":209,"organization":211,"vendors":216,"summary":217,"stage":218,"year":219,"channels":220,"languages":221,"metrics":223,"outcomeDisclosed":213,"sources":224,"verification":232,"grade":234,"id":235,"organizationSlug":236},"Verizon: AI powered Verizon Assistant and Customer Champion service model",[205,198,210],"order-to-activation-and-esim-onboarding-assistant",{"name":212,"anonymized":213,"country":214,"region":215,"industry":18},"Verizon",false,"US","north-america",[],"In June 2025 Verizon announced a customer experience overhaul: a Customer Champion who owns complex issues end to end, drawing on Google Cloud AI including Gemini models, 24/7 live chat with human agents, and a new My Verizon app, which includes an AI powered Verizon Assistant, in which customers can become a customer, manage upgrades, add lines and ask billing questions. Verizon describes the assistant as voice enabled for mobile customers. Verizon's chief executive framed the programme as a way to build loyalty and improve retention. No outcome figures were published.","production",2025,[28,31],[222],"en",[],[225,229],{"url":226,"title":227,"publisher":212,"date":228},"https://www.verizon.com/about/news/verizon-launches-industry-leading-ai-powered-customer-experience","Verizon, America's Most Reliable 5G Network, Launches Industry-Leading, AI Powered Customer Experience Innovations","2025-06-24",{"url":230,"title":231,"publisher":212},"https://www.verizon.com/about/customer-experience","Verizon Customer Experience",{"level":233,"checkedAt":201},"source-verified","B","verizon-ai-customer-experience-transformation","verizon",{"title":238,"useCases":239,"organization":241,"vendors":244,"summary":248,"stage":249,"year":250,"channels":251,"languages":252,"metrics":253,"outcomeDisclosed":266,"sources":267,"verification":271,"grade":234,"id":272,"organizationSlug":273},"BT Group: EE virtual assistant Aimee with generative AI",[205,240],"first-line-contact-centre-agent",{"name":242,"anonymized":213,"country":243,"region":159,"industry":18},"BT Group","GB",[245],{"name":246,"role":247},"Sprinklr","platform","BT Group runs the EE virtual assistant Aimee on Sprinklr's customer experience platform, drawing on BT Group data for personalised answers. The platform lets BT Group use generative AI for EE and BT customers, for example in an Aimee journey that prepares customers for international travel and in billing support, where generative AI gives detailed explanations of billing charges. BT Group says Aimee handles up to 60,000 conversations a week, double the volume of two years earlier, that the travel journey halved the need for chat support, and that it stays model agnostic behind a private cloud instance with safeguards against attempts to make the AI misbehave.","scaled",2024,[29],[222],[254,262],{"kpi":49,"value":255,"unit":256,"qualifier":257,"period":258,"claimant":259,"quote":260,"sourceUrl":261},60000,"count","up-to","per week","organization","EE virtual assistant Aimee now handles up to 60,000 customer conversations per week, with automation success rates on several types of customer journey now approaching 50%, freeing time for guides to focus on more complex issues","https://newsroom.bt.com/bt-group-leans-on-ai-to-transform-customer-service-experience/",{"kpi":46,"value":263,"unit":264,"qualifier":257,"period":265,"claimant":259,"quote":260,"sourceUrl":261},50,"percent","several types of customer journey",true,[268],{"url":261,"title":269,"publisher":242,"date":270},"BT Group leans on AI to transform customer service experience","2024-12-12",{"level":233,"checkedAt":201},"bt-group-ee-aimee-virtual-assistant","bt-group",{"title":275,"useCases":276,"organization":277,"vendors":279,"summary":282,"stage":218,"year":250,"channels":283,"languages":284,"metrics":287,"outcomeDisclosed":266,"sources":300,"verification":308,"grade":234,"id":309,"organizationSlug":310},"Vodafone: SuperTOBi generative AI assistant and SuperAgent",[205,240],{"name":278,"anonymized":213,"country":243,"region":159,"industry":18},"Vodafone",[280],{"name":281,"role":247},"Microsoft","Vodafone rebuilt its TOBi chatbot on Azure OpenAI as SuperTOBi, launched in Italy and Portugal, with Germany and Turkey announced to follow from July 2024 and other markets later that year. A companion SuperAgent helps human agents search the company knowledge base and, in Ireland, sends the human agent a summary of the online customer conversation so customers do not repeat themselves. Vodafone reports that initial tests at one of its call centres showed a 50% improvement in first time resolution of critical journeys such as complex billing inquiries, and that in Portugal first time resolution on appointment booking rose from 15% to 60%, with billing journeys being added next.",[29],[285,286],"it","pt",[288,295],{"kpi":45,"value":289,"unit":264,"qualifier":290,"period":291,"baseline":292,"claimant":259,"quote":293,"sourceUrl":294},60,"exact","appointment booking journey, Vodafone Portugal","15% before SuperTOBi","As a result, the first-time resolution rate has increased from 15% to 60% and Vodafone’s online net promoter scores (where respondents are asked to rate their experience) improved by 14 points to 64 points – anything above 50 points is considered a strong result.","https://www.vodafone.com/news/newsroom/technology/meet-super-tobi-vodafone-s-new-generative-ai-virtual-assistant-now-serving-customers-in-multiple-countries",{"kpi":296,"value":297,"unit":298,"qualifier":290,"period":299,"claimant":259,"quote":293,"sourceUrl":294},"nps-change",14,"points","online NPS, Vodafone Portugal",[301,304],{"url":294,"title":302,"publisher":278,"date":303},"Meet SuperTOBI, Vodafone's new Generative AI virtual assistant now serving customers in multiple countries","2024-07-04",{"url":305,"title":306,"publisher":278,"date":307},"https://www.vodafone.com/news/newsroom/technology/vodafone-supercharging-customer-experience-with-microsoft-s-gen-ai-tools","Vodafone supercharging customer experience with Microsoft's GenAI tools","2024-05-23",{"level":233,"checkedAt":201},"vodafone-supertobi-generative-ai-assistant","vodafone",{"title":312,"useCases":313,"organization":314,"vendors":318,"summary":321,"stage":249,"year":322,"channels":323,"languages":325,"metrics":326,"outcomeDisclosed":266,"sources":334,"verification":338,"grade":339,"id":340,"organizationSlug":341},"Mobily: AI self service agents across eight messaging channels",[205,240,198],{"name":315,"anonymized":213,"country":316,"region":317,"industry":18},"Mobily","SA","middle-east",[319],{"name":320,"role":247},"NiCE Cognigy","Mobily deployed customer facing AI agents on eight channels, including WhatsApp, Twitter and Apple Business Chat, connected to its internal systems. The agents answer billing, balance and data usage questions, change subscriptions, sell add ons, take payments and recharges, and handle feedback and complaints, with a warm handover to a specialist who can take over or hand back. NiCE Cognigy reports that the first response time fell from 20 minutes to about 6 seconds. The deployment was already live in 2022, when the case study described it as conversational AI; the current version presents it as agentic AI.",2022,[30,324],"social-messaging",[],[327],{"kpi":48,"value":328,"unit":264,"qualifier":290,"period":329,"baseline":330,"claimant":331,"quote":332,"sourceUrl":333},99.5,"first response time on messaging channels","first response up to 20 minutes before","vendor","An AI agent picks up any inquiry in around 6 seconds, reducing first response times significantly from the previous 20 minutes: a 99,5% improvement.","https://www.cognigy.com/en/case-study/mobily",[335],{"url":333,"title":336,"publisher":320,"archivedUrl":337},"Mobily: 99.5% faster response times with Agentic AI","https://web.archive.org/web/20220413151348/https://www.cognigy.com/en/case-study/mobily",{"level":233,"checkedAt":201},"C","mobily-agentic-ai-self-service",null,0,[344,350,355,360],{"kpi":45,"label":345,"unit":264,"aggregate":266,"higherIsBetter":266,"n":346,"nUpTo":342,"median":289,"min":289,"max":289,"byClaimant":347,"vendorOnly":213,"points":348},"First contact resolution",1,{"organization":346,"vendor":342,"regulator":342,"independent":342},[349],{"evidenceId":309,"organization":278,"value":289,"qualifier":290,"claimant":259,"grade":234,"pooled":266},{"kpi":48,"label":351,"unit":264,"aggregate":266,"higherIsBetter":266,"n":346,"nUpTo":342,"median":328,"min":328,"max":328,"byClaimant":352,"vendorOnly":266,"points":353},"Response time reduction",{"organization":342,"vendor":346,"regulator":342,"independent":342},[354],{"evidenceId":340,"organization":315,"value":328,"qualifier":290,"claimant":331,"grade":339,"pooled":266},{"kpi":46,"label":356,"unit":264,"aggregate":266,"higherIsBetter":266,"n":342,"nUpTo":346,"median":341,"min":341,"max":341,"byClaimant":357,"vendorOnly":213,"points":358},"Automation rate",{"organization":342,"vendor":342,"regulator":342,"independent":342},[359],{"evidenceId":272,"organization":242,"value":263,"qualifier":257,"claimant":259,"grade":234,"pooled":213},{"kpi":49,"label":361,"unit":256,"aggregate":213,"higherIsBetter":266,"n":342,"nUpTo":346,"median":341,"min":341,"max":341,"byClaimant":362,"vendorOnly":213,"points":363},"Interactions handled",{"organization":342,"vendor":342,"regulator":342,"independent":342},[364],{"evidenceId":272,"organization":242,"value":255,"qualifier":257,"claimant":259,"grade":234,"pooled":213},{"low":366,"high":367},1800000,9600000,[369,388,417,439,455,473],{"slug":195,"title":370,"shortTitle":371,"definition":372,"status":9,"industries":373,"functions":374,"patterns":376,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":75,"publicEvidenceCount":75,"organizations":378,"bestGrade":234,"headline":382,"lastVerified":387,"indexable":266},"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],[20,375],"field-service",[23,26,25,24,377],"computer-vision",[379,380,381,278],"Singtel","Virgin Media O2","Vodafone Germany",{"kpi":47,"label":383,"unit":264,"n":384,"nUpTo":342,"kind":385,"value":386,"qualifier":290,"claimant":341,"organization":341,"vendorReported":213},"Containment rate",3,"median",70,"2026-09-26",{"slug":196,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":397,"patterns":399,"audience":403,"autonomy":404,"adoptionStage":34,"segment":405,"evidenceCount":406,"publicEvidenceCount":406,"organizations":407,"bestGrade":234,"headline":410,"lastVerified":201,"indexable":266},"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.",[393,394,395,396,18],"cross-industry","banking","payments","insurance",[21,20,398],"regulatory-compliance",[400,401,402,25,24],"classification-and-routing","summarization","content-generation","employee-facing","copilot","middle-office",2,[408,409],"Lloyds Banking Group","NatWest Group",{"kpi":411,"label":412,"unit":413,"n":346,"nUpTo":342,"kind":414,"value":415,"qualifier":416,"claimant":259,"organization":408,"vendorReported":213},"time-saved-per-task","Time saved per task","minutes","reported",5,"approximately",{"slug":197,"title":418,"shortTitle":419,"definition":420,"status":9,"industries":421,"functions":422,"patterns":426,"audience":429,"autonomy":33,"adoptionStage":430,"segment":405,"evidenceCount":75,"publicEvidenceCount":75,"organizations":431,"bestGrade":234,"headline":435,"lastVerified":201,"indexable":266},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[18],[423,20,424,425],"marketing","sales","analytics-and-reporting",[427,428,23],"prediction-and-scoring","recommendation-and-personalization","back-office","mainstream",[432,433,380,434],"Etisalat","Telenet","Vodafone UK",{"kpi":436,"label":437,"unit":264,"n":406,"nUpTo":342,"kind":414,"value":438,"qualifier":290,"claimant":331,"organization":433,"vendorReported":266},"churn-reduction","Churn reduction",20,{"slug":198,"title":440,"shortTitle":441,"definition":442,"status":9,"industries":443,"functions":444,"patterns":445,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":446,"publicEvidenceCount":446,"organizations":447,"bestGrade":234,"headline":451,"lastVerified":387,"indexable":266},"AI assistant for telecom plan upgrades, add ons and sales","Plan upgrade and sales assistant","An AI assistant that helps existing and prospective customers choose, compare and buy the right mobile, broadband or TV plan, device or extra, in the app, in messaging, on the phone or through a human advisor, using the customer's usage and eligibility and the operator's current offers, and that completes the order or passes a ready quote to a person.",[18],[424,20,423],[428,23,24,26,25],9,[448,315,449,379,450,433,212,380,278],"Reliance Jio","Orange France","T-Mobile",{"kpi":452,"label":453,"unit":264,"n":346,"nUpTo":342,"kind":414,"value":454,"qualifier":290,"claimant":331,"organization":433,"vendorReported":266},"conversion-rate-uplift","Conversion uplift",75,{"slug":199,"title":456,"shortTitle":457,"definition":458,"status":9,"industries":459,"functions":463,"patterns":465,"audience":32,"autonomy":33,"adoptionStage":34,"segment":466,"evidenceCount":384,"publicEvidenceCount":406,"organizations":467,"bestGrade":339,"headline":470,"lastVerified":201,"indexable":266},"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.",[393,394,395,18,460,461,462],"energy-and-utilities","automotive","professional-services",[464,20],"collections-and-recovery",[26,23,25,400],"lending",[468,469],"Day Knight & Associates","SameDay Auto Finance",{"kpi":471,"label":472,"unit":264,"n":406,"nUpTo":342,"kind":414,"value":454,"qualifier":290,"claimant":331,"organization":469,"vendorReported":266},"cost-reduction","Cost reduction",{"slug":200,"title":474,"shortTitle":475,"definition":476,"status":9,"industries":477,"functions":478,"patterns":480,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":481,"publicEvidenceCount":415,"organizations":482,"bestGrade":234,"headline":488,"lastVerified":387,"indexable":266},"AI agent for utility billing, payments, meter readings and move in or move out","Utility billing and home moves","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.",[460],[20,479],"operations",[23,26,25,24],6,[483,484,485,486,487],"Aydem Energy","Dubai Electricity and Water Authority","EDF","Octopus Energy","Pacific Gas and Electric Company",{"kpi":47,"label":383,"unit":264,"n":406,"nUpTo":342,"kind":414,"value":454,"qualifier":290,"claimant":259,"organization":483,"vendorReported":213},{"indexable":266,"reasons":490},[],[492,497,502,510,517,523,530,537,545,552,557,563,570,577,582,587,594,600,606,611,616,622,628,633,638,644,650,655,660,666,672,678,684,689],{"id":150,"label":493,"issuer":158,"region":159,"url":494,"description":495,"useCases":496,"indexable":266},"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":151,"label":498,"issuer":158,"region":159,"url":499,"description":500,"useCases":501,"indexable":266},"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":503,"label":504,"issuer":505,"region":506,"url":507,"description":508,"useCases":509,"indexable":266},"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":511,"label":512,"issuer":513,"region":215,"url":514,"description":515,"useCases":516,"indexable":266},"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":518,"label":519,"issuer":158,"region":159,"url":520,"description":521,"useCases":522,"indexable":266},"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":524,"label":525,"issuer":526,"region":159,"url":527,"description":528,"useCases":529,"indexable":266},"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":531,"label":532,"issuer":533,"region":159,"url":534,"description":535,"useCases":536,"indexable":266},"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":538,"label":539,"issuer":540,"region":541,"url":542,"description":543,"useCases":544,"indexable":266},"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":546,"label":547,"issuer":548,"region":541,"url":549,"description":550,"useCases":551,"indexable":266},"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":153,"label":553,"issuer":554,"region":506,"url":555,"description":556,"useCases":438,"indexable":266},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":558,"label":559,"issuer":560,"region":215,"url":561,"description":562,"useCases":438,"indexable":266},"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":564,"label":565,"issuer":566,"region":159,"url":567,"description":568,"useCases":569,"indexable":266},"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":571,"label":572,"issuer":573,"region":506,"url":574,"description":575,"useCases":576,"indexable":266},"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":578,"label":579,"issuer":158,"region":159,"url":580,"description":581,"useCases":297,"indexable":266},"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.",{"id":583,"label":584,"issuer":158,"region":159,"url":585,"description":586,"useCases":297,"indexable":266},"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":588,"label":589,"issuer":590,"region":215,"url":591,"description":592,"useCases":593,"indexable":266},"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":595,"label":596,"issuer":158,"region":159,"url":597,"description":598,"useCases":599,"indexable":266},"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":601,"label":602,"issuer":603,"region":215,"url":604,"description":605,"useCases":599,"indexable":266},"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":152,"label":607,"issuer":608,"region":506,"url":609,"description":610,"useCases":599,"indexable":266},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":154,"label":612,"issuer":158,"region":159,"url":613,"description":614,"useCases":615,"indexable":266},"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":617,"label":618,"issuer":619,"region":215,"url":620,"description":621,"useCases":615,"indexable":266},"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":623,"label":624,"issuer":540,"region":541,"url":625,"description":626,"useCases":627,"indexable":266},"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":629,"label":630,"issuer":158,"region":159,"url":631,"description":632,"useCases":627,"indexable":266},"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":634,"label":635,"issuer":158,"region":159,"url":636,"description":637,"useCases":627,"indexable":266},"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":639,"label":640,"issuer":641,"region":159,"url":642,"description":643,"useCases":446,"indexable":266},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":645,"label":646,"issuer":647,"region":215,"url":648,"description":649,"useCases":76,"indexable":266},"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":651,"label":652,"issuer":158,"region":159,"url":653,"description":654,"useCases":76,"indexable":266},"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":656,"label":657,"issuer":158,"region":159,"url":658,"description":659,"useCases":481,"indexable":266},"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":661,"label":662,"issuer":663,"region":317,"url":664,"description":665,"useCases":415,"indexable":266},"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":667,"label":668,"issuer":669,"region":159,"url":670,"description":671,"useCases":75,"indexable":266},"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":673,"label":674,"issuer":675,"region":159,"url":676,"description":677,"useCases":75,"indexable":266},"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":679,"label":680,"issuer":681,"region":541,"url":682,"description":683,"useCases":384,"indexable":266},"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":685,"label":686,"issuer":158,"region":159,"url":687,"description":688,"useCases":384,"indexable":266},"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":690,"label":691,"issuer":692,"region":215,"url":693,"description":694,"useCases":384,"indexable":266},"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.",1790598295994]