[{"data":1,"prerenderedAt":640},["ShallowReactive",2],{"uc-churn-prediction-and-retention-offers":3,"uc-regulations":433},{"useCase":4,"evidence":201,"blitsAiDeployments":305,"benchmarks":306,"indicative":321,"related":324,"indexability":431,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":24,"channels":28,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":45,"indicativeValue":50,"macroEstimates":91,"feasibility":92,"implementation":107,"risk":150,"blitsAi":178,"faq":180,"related":190,"datePublished":196,"dateModified":196,"lastVerified":196,"changelog":197,"slug":200},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom churn prediction and retention","Telecom AI scores churn risk and picks a retention action. Pega reports 20% less churn at Telenet and 15% in Etisalat's SMB unit, with no published control group.","published","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.",[12,13,14,15,16],"churn prediction model","retention next best action","save desk assistant","customer retention AI","proactive retention",[18],"telecommunications",[20,21,22,23],"marketing","customer-service","sales","analytics-and-reporting",[25,26,27],"prediction-and-scoring","recommendation-and-personalization","conversational-agent",[29,30,31,32,33],"agent-desktop","mobile-app","sms","email","voice","back-office","supervised-agent","mainstream","middle-office","In mature mobile and broadband markets, growth often depends on keeping customers as much as on\nwinning new ones. Customers leave for a better price, after repeated faults, after a bill shock or\nat the end of a contract, often without contacting the operator first. Traditional retention reacts\nonly when the customer calls to cancel, when the decision is already made, and relies on a save\ndesk that offers the same discount to everyone.\n\nBlanket discounts are expensive and can teach customers that threatening to leave pays. What operators need is\nearlier warning, a reason for the risk, and an action that fits: fixing the fault that annoyed\nthe customer can be worth more than any discount. At the same time, regulators fine operators\nwho make leaving hard (Ofcom fined Virgin Media £28 million in July 2026), so retention has to help customers, not trap them.",[],"1. **Score the risk.** A model scores every customer regularly, and in real time on events\n   such as a failed repair or a price rise, from usage, network quality, billing, contact history\n   and contract end dates.\n2. **Explain the reason.** For each high risk customer it gives the main drivers (repeated\n   faults, a better competitor price, a bill shock) so the action matches the cause.\n3. **Choose the next best action.** A decisioning engine picks the action with the best value for\n   customer and operator within budget: resolve the fault, move to a better fitting plan, a\n   loyalty benefit, or an offer, and sometimes no action at all.\n4. **Deliver it in the right channel.** The action appears in the app, in a message, as a prompt\n   to the advisor on the next call, or in a conversation with an AI agent, subject to consent.\n5. **Learn from the outcome.** Accepted and ignored offers and actual churn feed back into the\n   models, measured against a control group.",[42,43,44],"revenue-growth","customer-experience","cost-to-serve",[46,47,48,49],"churn-reduction","conversion-rate-uplift","revenue-uplift","customer-satisfaction",{"referenceOrg":51,"inputs":52,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"An operator with 2 million postpaid mobile and broadband customers",[53,58,65,72,79],{"key":54,"label":55,"low":56,"high":56,"unit":54,"note":57},"customers","Postpaid customers",2000000,"The reference operator.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"churnRate","Annual churn rate",0.12,0.18,"fraction of customers per year","Editorial assumption, replace with your own annual postpaid churn.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"churnReduction","Relative reduction in churn among customers the programme reaches",0.03,0.08,"fraction of churn","Conservative against the benchmarks on this page (Pega reports a 20% churn reduction at Telenet and 15% at Etisalat, a figure it first reported for Etisalat's SMB division), because those are vendor figures without a published control group.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"annualRevenue","Annual revenue per customer",250,400,"USD per customer per year","Editorial assumption, replace with your own average revenue per postpaid account.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"retainedMarginShare","Share of retained revenue kept after the cost of retention offers",0.5,0.7,"fraction of retained revenue","Editorial assumption covering discounts and benefits given to retained customers.","customers * churnRate * churnReduction * annualRevenue * retainedMarginShare","USD","per year","First year revenue retained, net of retention offers","Counts only one year of retained revenue net of offer costs. It leaves out the lifetime value of retained customers, savings from fixing root causes, the cost of models and decisioning, and customers who would have stayed anyway, which only a control group can remove.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":101},"medium","Churn models are well understood; the hard parts are joining network, billing and contact data per customer in near real time, running a decisioning engine with offer budgets, and delivering actions in every channel with consent and a measurable control group.",[96,97,98,99,100],"At least two years of customer history with churn outcomes and reasons","Network quality, fault and repair data linked to customers","Billing events, price changes and contract end dates","Contact history and complaint records","Marketing consent and contact preferences",[102,103,104,105,106],"Data platform joining network, billing, CRM and contact data","Decisioning or next best action engine with offer rules and budgets","Agent desktop and store systems for advisor prompts","App, messaging and email channels for digital actions","Campaign management with consent and contact frequency rules",{"steps":108,"guardrails":124,"humanInTheLoop":130,"kpisToInstrument":131,"failureModes":137},[109,112,115,118,121],{"title":110,"detail":111},"Define churn and the outcomes you will measure","Agree on what counts as churn (port out, cancellation, downgrade) and set up a permanent control group before the first model goes live.",{"title":113,"detail":114},"Build reasons, not only scores","Train the model to expose its main drivers per customer, so actions can address the cause. A fault driven risk needs a fix, not a discount.",{"title":116,"detail":117},"Put actions under a decisioning engine with budgets","Define the available actions, eligibility and cost, and let the engine choose within budget, including the option to do nothing for customers who will stay anyway.",{"title":119,"detail":120},"Bring it into the conversation","Show the reason and recommended action to advisors on every call, and give AI agents the same recommendation, so a customer who says they want to leave gets a relevant answer.",{"title":122,"detail":123},"Keep cancellation easy","Design retention so a customer who still wants to leave can do so in the same conversation. Treat that as a hard requirement, tested like any other journey.",[125,126,127,128,129],"A customer who confirms they want to leave is helped to leave in the same contact","Offers only within approved budgets and eligibility rules","Marketing consent and contact frequency limits enforced before any outbound action","Protected characteristics and proxies excluded from features, with regular fairness checks","Vulnerable customers routed to trained people, not to automated offers","Retention advisors decide on offers above standard limits and handle vulnerable customers. The commercial team owns the action catalogue and budgets, and an analytics team reviews model performance, fairness and the control group results monthly.",[132,133,134,135,136],"Churn in treated customers versus the control group","Offer acceptance and cost per retained customer","Share of high risk customers whose root cause was fixed","Time to complete a cancellation for customers who still want to leave","Complaints about retention contacts or cancellation",[138,141,144,147],{"title":139,"detail":140},"Paying customers who would have stayed","Offers go to customers with a high score who were never leaving. Use uplift models and a control group.",{"title":142,"detail":143},"Making it hard to leave","Retention becomes obstruction and a regulatory breach. Measure and protect the cancellation path.",{"title":145,"detail":146},"Discount addiction","Customers learn that threatening to leave gets a discount. Favour fixing root causes and limit repeat offers.",{"title":148,"detail":149},"Unfair outcomes","Better offers go systematically to some groups. Exclude protected attributes and proxies and audit outcomes.",{"euAiAct":151,"regulations":154,"guidance":159,"controls":171,"incidents":177},{"tier":152,"basis":153},"context-dependent","Churn scoring and offer selection for marketing are not listed in Annex III, so a back office design that only scores customers and prompts human advisors is minimal risk, with no specific obligations. When an AI agent delivers the offer to the customer in chat, messaging or voice, the system is limited risk: Article 50 requires telling customers they are dealing with AI. A design that used manipulative techniques or exploited vulnerabilities to keep customers from leaving could fall under the Article 5 prohibitions. GDPR rules on profiling and the right to object to direct marketing (Article 21) apply in full.",[155,156,157,158],"eu-ai-act","gdpr","telecom-consumer-rules","eecc",[160,166],{"title":161,"issuer":162,"region":163,"url":164,"note":165},"Ofcom fines Virgin Media £28m for repeatedly preventing customers from cancelling contracts","Ofcom","europe","https://www.ofcom.org.uk/phones-and-broadband/switching-provider/ofcom-fines-virgin-media-28m-for-repeatedly-preventing-customers-from-cancelling-contracts","A £28 million fine (July 2026) for retention practices, including a two tier cancellation process and agents rewarded for deterring cancellations, that caused customers unreasonable effort when trying to leave; the line any AI retention design must not cross.",{"title":167,"issuer":168,"region":163,"url":169,"note":170},"Directive on privacy and electronic communications (Directive 2002/58/EC)","European Union","https://eur-lex.europa.eu/eli/dir/2002/58/oj","Article 13 sets the rules for unsolicited electronic marketing, which apply to proactive retention offers by messaging, email or automated calls. Automated calls need prior consent; under the Article 13(2) soft opt in, an operator may email or message its existing customers about its own similar products or services, provided they can object free of charge with every message.",[172,173,174,175,176],"Model inventory entry with owner, features, validation and fairness results","Permanent control group and monthly incrementality reporting","Documented action catalogue with budgets and approval","Consent, frequency and vulnerability checks enforced in the decisioning engine","Audit of cancellation journeys for unreasonable barriers",[],{"howToBuild":179},"On Blits.ai the churn score and next best action usually come from the operator's own models or\ndecisioning engine; Blits.ai delivers the conversation. An **AI agent** reads the risk reason and\nrecommended action through a **custom function**, answers questions from a **knowledge base**\nof approved offers and terms, and completes a plan change or offer acceptance through a\n**flow** with explicit confirmation. **Agentic workflows**, run on a schedule or triggered\nthrough the API, can prepare retention actions and hand them off through a custom function to\nthe operator's messaging platform, with **human in the loop approval** for offers above a\nthreshold.\n\nThe agent works on **WhatsApp, SMS, email and voice**, and inside the operator's own app through\nthe **API channel**, and **human handover** passes customers who want to leave, or who seem\nvulnerable, to a trained advisor with the context. **Guardrails** with policies written by the\noperator check answers before they reach the customer, **test suites** evaluate retention\nconversations before release, **analytics** tracks them in production, and the platform is\nmodel agnostic with EU and UAE data residency.",[181,184,187],{"question":182,"answer":183},"How much can AI reduce telecom churn?","The public figures are vendor reported: Pega reports a 20% reduction in churn at Telenet, and a 15% reduction at Etisalat that it first published for Etisalat's small and medium business division. Neither comes with a published baseline or control group, so plan conservatively and measure against your own holdout.",{"question":185,"answer":186},"Is it legal to use AI to persuade customers to stay?","Yes, if it helps rather than obstructs. Offers must respect marketing consent and profiling rules, and a customer who wants to leave must be able to. In July 2026 Ofcom fined Virgin Media £28 million for retention practices that made cancelling unreasonably hard.",{"question":188,"answer":189},"Where should retention actions be delivered?","Wherever the customer is: as a prompt to the advisor on a call (Virgin Media O2 piloted its Lumi AI advisor prompts with care, telesales and retentions teams in 2025), in the app, or in a conversation with an AI agent that has the same recommendation.",[191,192,193,194,195],"plan-upgrade-and-sales-assistant","bill-explanation-and-billing-dispute-agent","personalized-marketing-at-scale","insurance-renewal-and-retention","customer-feedback-analysis","2026-09-27",[198],{"date":196,"note":199},"First published","churn-prediction-and-retention-offers",[202,230,264,282],{"title":203,"useCases":204,"organization":205,"vendors":209,"summary":212,"stage":213,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":207,"sources":219,"verification":224,"grade":227,"id":228,"organizationSlug":229},"Virgin Media O2: Lumi AI prompts for care, telesales and retention advisors",[200,191],{"name":206,"anonymized":207,"country":208,"region":163,"industry":18},"Virgin Media O2",false,"GB",[210],{"name":206,"role":211},"in-house","Virgin Media O2 built its own tool, Lumi AI, that analyses a live conversation and prompts the advisor with resolutions that worked for similar customers and with the products and services most likely to interest this customer. In July 2025 it was in pilot with a cohort of advisors in care, telesales and retentions, with a wider rollout planned. Alongside it the operator uses an AI contact centre service from Amazon Web Services that routes callers by their stated reason, software that flags potentially vulnerable customers, and automatic call summaries. The retention effect of Lumi AI has not been published.","pilot",2025,[29,33],[217],"en",[],[220],{"url":221,"title":222,"publisher":206,"date":223},"https://news.virginmediao2.co.uk/virgin-media-o2-launches-new-ai-tools-to-supercharge-customer-services-and-better-help-most-vulnerable-customers/","Virgin Media O2 launches new AI tools to supercharge customer services and better help most vulnerable customers","2025-07-28",{"level":225,"checkedAt":226},"source-verified","2026-09-26","B","virgin-media-o2-lumi-ai-advisor-assistant","virgin-media-o2",{"title":231,"useCases":232,"organization":233,"vendors":236,"summary":240,"stage":241,"year":242,"channels":243,"languages":244,"metrics":245,"outcomeDisclosed":256,"sources":257,"verification":260,"grade":261,"id":262,"organizationSlug":263},"Telenet: AI decisioning for next best action, churn and upgrades",[200,191],{"name":234,"anonymized":207,"country":235,"region":163,"industry":18},"Telenet","BE",[237],{"name":238,"role":239},"Pega","platform","Telenet, a provider of connectivity and entertainment services in Belgium, uses Pega's AI based Customer Decision Hub as a single decisioning system that responds to customer signals in real time, anticipates how behaviour may change and proposes the next best action, such as personalised upgrades and solutions, with the stated goals of reducing churn and raising offer acceptance. Pega reports a 20% reduction in churn, a 75% increase in offer acceptance and a 33% increase in cross sell. These figures cover the whole decisioning programme, including upgrades and cross sell, not retention alone.","scaled",2023,[],[],[246,253],{"kpi":46,"value":247,"unit":248,"qualifier":249,"claimant":250,"quote":251,"sourceUrl":252},20,"percent","exact","vendor","20% reduction in churn","https://www.pega.com/customers/telenet-customer-decision-hub",{"kpi":47,"value":254,"unit":248,"qualifier":249,"claimant":250,"quote":255,"sourceUrl":252},75,"75% increase in offer acceptance",true,[258],{"url":252,"title":259,"publisher":238},"Anticipating customer needs with AI-powered decisioning",{"level":225,"checkedAt":226},"C","telenet-next-best-action-decisioning",null,{"title":265,"useCases":266,"organization":267,"vendors":269,"summary":271,"stage":272,"year":242,"channels":273,"languages":274,"metrics":275,"outcomeDisclosed":207,"sources":276,"verification":280,"grade":261,"id":281,"organizationSlug":263},"Vodafone UK: always on next best action for retention",[200],{"name":268,"anonymized":207,"country":208,"region":163,"industry":18},"Vodafone UK",[270],{"name":238,"role":239},"Vodafone UK's Always on Marketing programme runs on Pega Customer Decision Hub. Customer interactions and events are processed as they happen and mapped to common intents, so one central system presents the next best action for each customer at every touchpoint instead of pushing products. Pega titles the case study as improving customer retention and says the programme was halfway through its transformation, but publishes no figures.","production",[],[217],[],[277],{"url":278,"title":279,"publisher":238},"https://www.pega.com/customers/vodafone-uk-customer-decision-hub","Vodafone UK: Always-on marketing improves customer retention",{"level":225,"checkedAt":226},"vodafone-uk-always-on-marketing",{"title":283,"useCases":284,"organization":285,"vendors":289,"summary":291,"stage":241,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":256,"sources":300,"verification":303,"grade":261,"id":304,"organizationSlug":263},"Etisalat: AI next best action to retain at risk customers",[200],{"name":286,"anonymized":207,"country":287,"region":288,"industry":18},"Etisalat","AE","middle-east",[290],{"name":238,"role":239},"Etisalat in the UAE uses Pega Customer Decision Hub as its central decisioning engine, with predictive and adaptive models that identify each customer's context and orchestrate personalised next best actions across inbound, outbound and agent assisted channels, moving from a focus on sales to a focus on incremental value. During the COVID-19 pandemic it used the system to identify at risk customers and offer practical help such as a free VPN and its online collaboration platform. Pega reports a 15% reduction in churn and a 20% increase in renewals, and its executive quote describes a 20% year on year increase in incremental value; the churn and renewal figures first appeared in Pega's earlier case study on Etisalat's small and medium business (SMB) division, which used next best action to prioritise outbound sales calls and offers, not in connection with the COVID-19 work. All figures cover the whole decisioning programme, including sales, not retention alone.",2021,[],[],[296],{"kpi":46,"value":297,"unit":248,"qualifier":249,"claimant":250,"quote":298,"sourceUrl":299},15,"15% reduction in customer churn","https://www.pega.com/customers/etisalat-decision-hub",[301],{"url":299,"title":302,"publisher":238},"Etisalat revolutionizes customer experience with AI",{"level":225,"checkedAt":196},"etisalat-next-best-action-retention",0,[307,315],{"kpi":46,"label":308,"unit":248,"aggregate":256,"higherIsBetter":256,"n":309,"nUpTo":305,"median":310,"min":297,"max":247,"byClaimant":311,"vendorOnly":256,"points":312},"Churn reduction",2,17.5,{"organization":305,"vendor":309,"regulator":305,"independent":305},[313,314],{"evidenceId":262,"organization":234,"value":247,"qualifier":249,"claimant":250,"grade":261,"pooled":256},{"evidenceId":304,"organization":286,"value":297,"qualifier":249,"claimant":250,"grade":261,"pooled":256},{"kpi":47,"label":316,"unit":248,"aggregate":256,"higherIsBetter":256,"n":317,"nUpTo":305,"median":254,"min":254,"max":254,"byClaimant":318,"vendorOnly":256,"points":319},"Conversion uplift",1,{"organization":305,"vendor":317,"regulator":305,"independent":305},[320],{"evidenceId":262,"organization":234,"value":254,"qualifier":249,"claimant":250,"grade":261,"pooled":256},{"low":322,"high":323},900000,8063999.999999999,[325,349,365,390,408],{"slug":191,"title":326,"shortTitle":327,"definition":328,"status":9,"industries":329,"functions":330,"patterns":331,"audience":335,"autonomy":35,"adoptionStage":336,"segment":337,"evidenceCount":338,"publicEvidenceCount":338,"organizations":339,"bestGrade":227,"headline":347,"lastVerified":226,"indexable":256},"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],[22,21,20],[26,27,332,333,334],"rag-knowledge-assistant","voice-agent","agentic-workflow","customer-facing","early-adopters","front-office",9,[340,341,342,343,344,234,345,206,346],"Reliance Jio","Mobily","Orange France","Singtel","T-Mobile","Verizon","Vodafone",{"kpi":47,"label":316,"unit":248,"n":317,"nUpTo":305,"kind":348,"value":254,"qualifier":249,"claimant":250,"organization":234,"vendorReported":256},"reported",{"slug":192,"title":350,"shortTitle":351,"definition":352,"status":9,"industries":353,"functions":354,"patterns":356,"audience":335,"autonomy":35,"adoptionStage":336,"segment":337,"evidenceCount":357,"publicEvidenceCount":357,"organizations":358,"bestGrade":227,"headline":360,"lastVerified":196,"indexable":256},"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.",[18],[21,355],"case-management",[27,332,334,333],4,[359,341,345,346],"BT Group",{"kpi":361,"label":362,"unit":248,"n":317,"nUpTo":305,"kind":348,"value":363,"qualifier":249,"claimant":364,"organization":346,"vendorReported":207},"first-contact-resolution","First contact resolution",60,"organization",{"slug":193,"title":366,"shortTitle":367,"definition":368,"status":9,"industries":369,"functions":375,"patterns":376,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":378,"publicEvidenceCount":379,"organizations":380,"bestGrade":227,"headline":388,"lastVerified":196,"indexable":256},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[370,371,372,373,374],"cross-industry","travel-and-hospitality","media-and-entertainment","retail-and-ecommerce","banking",[20,22],[26,25,377],"content-generation",8,7,[381,382,383,384,385,386,387],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":47,"label":316,"unit":248,"n":317,"nUpTo":305,"kind":348,"value":389,"qualifier":249,"claimant":250,"organization":382,"vendorReported":256},30,{"slug":194,"title":391,"shortTitle":392,"definition":393,"status":9,"industries":394,"functions":396,"patterns":398,"audience":34,"autonomy":400,"adoptionStage":401,"segment":402,"evidenceCount":403,"publicEvidenceCount":403,"organizations":404,"bestGrade":227,"headline":263,"lastVerified":226,"indexable":256},"AI for insurance renewal processing and customer retention","Renewal and retention","AI that prepares and runs the renewal cycle: it digitizes renewal submissions and changes in risk for underwriters, flags policies at risk of lapsing or leaving, prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[395],"insurance",[397,21,22],"underwriting",[25,399,27,26],"document-processing","copilot","emerging","distribution",3,[405,406,407],"Hiscox","Nsure.com","Zurich Insurance Group",{"slug":195,"title":409,"shortTitle":410,"definition":411,"status":9,"industries":412,"functions":415,"patterns":416,"audience":34,"autonomy":400,"adoptionStage":36,"evidenceCount":420,"publicEvidenceCount":420,"organizations":421,"bestGrade":227,"headline":427,"lastVerified":196,"indexable":256},"AI for voice of the customer and feedback analysis","Customer feedback analysis","AI that reads every piece of free text customer feedback, such as survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and turns it into themes, sentiment, drivers and suggested actions that a named owner can act on, so the organization hears all of its customers instead of a sample.",[370,373,413,414],"government","manufacturing",[21,20,23],[417,418,419],"classification-and-routing","summarization","speech-analytics",5,[422,423,424,425,426],"U.S. Department of Housing and Urban Development","Majid Al Futtaim Retail","Mattel","SBF Group","U.S. Social Security Administration",{"kpi":428,"label":429,"unit":248,"n":317,"nUpTo":305,"kind":348,"value":430,"qualifier":249,"claimant":250,"organization":425,"vendorReported":256},"accuracy","Accuracy",84,{"indexable":256,"reasons":432},[],[434,439,444,452,460,466,473,480,488,495,501,507,514,520,526,531,538,544,550,555,560,566,572,577,582,588,594,599,605,611,617,623,629,634],{"id":155,"label":435,"issuer":168,"region":163,"url":436,"description":437,"useCases":438,"indexable":256},"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":156,"label":440,"issuer":168,"region":163,"url":441,"description":442,"useCases":443,"indexable":256},"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":445,"label":446,"issuer":447,"region":448,"url":449,"description":450,"useCases":451,"indexable":256},"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":453,"label":454,"issuer":455,"region":456,"url":457,"description":458,"useCases":459,"indexable":256},"nist-ai-rmf","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":461,"label":462,"issuer":168,"region":163,"url":463,"description":464,"useCases":465,"indexable":256},"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":467,"label":468,"issuer":469,"region":163,"url":470,"description":471,"useCases":472,"indexable":256},"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":474,"label":475,"issuer":476,"region":163,"url":477,"description":478,"useCases":479,"indexable":256},"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":481,"label":482,"issuer":483,"region":484,"url":485,"description":486,"useCases":487,"indexable":256},"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":489,"label":490,"issuer":491,"region":484,"url":492,"description":493,"useCases":494,"indexable":256},"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":496,"label":497,"issuer":498,"region":448,"url":499,"description":500,"useCases":247,"indexable":256},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":502,"label":503,"issuer":504,"region":456,"url":505,"description":506,"useCases":247,"indexable":256},"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":508,"label":509,"issuer":510,"region":163,"url":511,"description":512,"useCases":513,"indexable":256},"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":515,"label":516,"issuer":517,"region":448,"url":518,"description":519,"useCases":297,"indexable":256},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":521,"label":522,"issuer":168,"region":163,"url":523,"description":524,"useCases":525,"indexable":256},"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":527,"label":528,"issuer":168,"region":163,"url":529,"description":530,"useCases":525,"indexable":256},"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":532,"label":533,"issuer":534,"region":456,"url":535,"description":536,"useCases":537,"indexable":256},"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":539,"label":540,"issuer":168,"region":163,"url":541,"description":542,"useCases":543,"indexable":256},"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":545,"label":546,"issuer":547,"region":456,"url":548,"description":549,"useCases":543,"indexable":256},"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":157,"label":551,"issuer":552,"region":448,"url":553,"description":554,"useCases":543,"indexable":256},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":158,"label":556,"issuer":168,"region":163,"url":557,"description":558,"useCases":559,"indexable":256},"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":561,"label":562,"issuer":563,"region":456,"url":564,"description":565,"useCases":559,"indexable":256},"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":567,"label":568,"issuer":483,"region":484,"url":569,"description":570,"useCases":571,"indexable":256},"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":573,"label":574,"issuer":168,"region":163,"url":575,"description":576,"useCases":571,"indexable":256},"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":578,"label":579,"issuer":168,"region":163,"url":580,"description":581,"useCases":571,"indexable":256},"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":583,"label":584,"issuer":585,"region":163,"url":586,"description":587,"useCases":338,"indexable":256},"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":589,"label":590,"issuer":591,"region":456,"url":592,"description":593,"useCases":378,"indexable":256},"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":595,"label":596,"issuer":168,"region":163,"url":597,"description":598,"useCases":378,"indexable":256},"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":600,"label":601,"issuer":168,"region":163,"url":602,"description":603,"useCases":604,"indexable":256},"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":606,"label":607,"issuer":608,"region":288,"url":609,"description":610,"useCases":420,"indexable":256},"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":612,"label":613,"issuer":614,"region":163,"url":615,"description":616,"useCases":357,"indexable":256},"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":618,"label":619,"issuer":620,"region":163,"url":621,"description":622,"useCases":357,"indexable":256},"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":624,"label":625,"issuer":626,"region":484,"url":627,"description":628,"useCases":403,"indexable":256},"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":630,"label":631,"issuer":168,"region":163,"url":632,"description":633,"useCases":403,"indexable":256},"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":635,"label":636,"issuer":637,"region":456,"url":638,"description":639,"useCases":403,"indexable":256},"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.",1790598302053]