[{"data":1,"prerenderedAt":634},["ShallowReactive",2],{"uc-dynamic-paywall-and-subscription-propensity":3,"uc-regulations":411},{"useCase":4,"evidence":184,"blitsAiDeployments":296,"benchmarks":297,"indicative":313,"related":316,"indexability":409,"includeUnpublished":190},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":25,"audience":26,"autonomy":27,"adoptionStage":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":34,"indicativeValue":38,"macroEstimates":73,"feasibility":79,"implementation":93,"risk":135,"blitsAi":161,"faq":163,"related":176,"datePublished":179,"dateModified":179,"lastVerified":179,"changelog":180,"slug":183},"AI dynamic paywall and subscription propensity scoring","Dynamic paywall","AI dynamic paywall and propensity to subscribe","A dynamic paywall scores each reader's chance to subscribe. Reuters decides per visitor whether to show it; FT Strategies reports 92% higher conversion at the FT.","published","An AI dynamic paywall scores how likely each reader is to subscribe and uses the score to choose whether to show a paywall, which offer and which message. News publishers use it to grow subscriptions while keeping the advertising revenue of readers who are unlikely to pay, where a classic paywall applies one fixed rule to everyone.",[12,13,14,15],"propensity paywall","smart paywall","AI paywall","propensity to subscribe model",[17],"media-and-entertainment",[19,20,21],"marketing","sales","analytics-and-reporting",[23,24],"prediction-and-scoring","recommendation-and-personalization",[],"customer-facing","autonomous","early-adopters","A classic paywall applies the same rule to every visitor. FT Strategies gives the example of a\nstandard paywall that appears after three articles within a 30 day period. The description of a\nWall Street Journal session makes the same point: whether a prospective subscriber has visited a\npublisher multiple times or is a first time visitor, the experience is the same.\n\nA reader who leaves at the wall also stops producing ad impressions, so a publisher that depends\non both revenue lines has to decide, visitor by visitor, whether a subscription or an ad\nimpression is worth more. In 2018 Digiday reported that NZZ got 60 percent of its revenue from\nsubscriptions and less than 40 percent from advertising, and in 2026 Reuters said that it is\nnot sacrificing ad revenue to grow subscriptions. A dynamic paywall lets a publisher make that\ndecision per visitor instead of per site.",[],"1. **Collect behavior signals.** The publisher records, for registered and anonymous readers,\n   what they read, how often they come back, on which device and at what time of day, and which\n   newsletters they receive. In 2018 Digiday reported that NZZ had scored registered readers on\n   about 30 attributes, such as time spent on articles, frequency and newsletters received.\n2. **Score the propensity to subscribe.** A model estimates how likely each reader is to\n   subscribe. Reuters says its system estimates how much ad revenue a visitor could\n   generate, then decides whether to show a paywall based on how likely that visitor is to convert.\n3. **Choose the experience.** The score and the content decide whether the paywall appears now,\n   how many free articles the reader still gets, which package is promoted and what the message\n   says. In 2018 NZZ served the payment prompt to readers who reached the top 20 percent\n   of its propensity score.\n4. **Adjust to the news cycle.** Reuters says the paywall tightens when the news cycle is busy\n   and content converts well, and loosens on a slow news day.\n5. **Learn from outcomes.** Every decision and its result, subscribed or not, goes back into\n   the model, and the publisher runs A\u002FB tests against a control group to check that revenue\n   moves in the right direction.",[33],"revenue-growth",[35,36,37],"conversion-rate-uplift","revenue-uplift","interactions-handled",{"referenceOrg":39,"inputs":40,"formula":68,"currency":69,"period":70,"resultLabel":71,"caveat":72},"A news publisher with 2 million registered readers",[41,47,54,61],{"key":42,"label":43,"low":44,"high":44,"unit":45,"note":46},"registeredReaders","Registered readers who see a paywall decision each year",2000000,"readers","The reference publisher.",{"key":48,"label":49,"low":50,"high":51,"unit":52,"note":53},"baseConversion","Share of those readers who subscribe each year with a fixed paywall",0.004,0.01,"fraction of readers","Editorial assumption. Replace with the conversion rate of your own registered readers.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"relativeUplift","Relative increase in subscriptions from the dynamic paywall",0.1,0.4,"fraction of base subscriptions","Conservative against the benchmarks on this page, where NZZ reported in 2018 that its conversion rate rose fivefold over three years and FT Strategies, the consulting arm of the Financial Times, reports a 92% increase in conversion rate at the Financial Times. Both are the organizations' own figures without a stated baseline, and much of the NZZ rise predates its propensity model.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"revenuePerSubscriber","First year revenue per added subscriber",48,200,"USD per subscriber","The low value is Reuters' flat price of USD 4 a month, reported by Digiday. The high value is an editorial assumption for a premium news subscription.","registeredReaders * baseConversion * relativeUplift * revenuePerSubscriber","USD","per year","Added first year subscription revenue","Gross first year subscription revenue only. It leaves out churn in later years, any loss of advertising revenue from readers who now see a wall, discounts, the cost of the data and model work and the effect on the readers who would have subscribed anyway.",[74],{"statement":75,"sourceTitle":76,"sourceUrl":77,"year":78},"FT Strategies reports that at the INMA Subscriptions Summit it was highlighted that 22% of 264 news brands declared using a hybrid, dynamic or smart paywall, and says that proportion has increased 4 times since 2020.","How dynamic paywalls are transforming reader revenue","https:\u002F\u002Fwww.ftstrategies.com\u002Fen-gb\u002Finsights\u002Fhow-dynamic-paywalls-are-transforming-reader-revenue",2025,{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"medium","The build needs a reader identity and event stream that can be queried within a page load, a paywall layer in the content management system that can change the wall per request, a subscription system for packages and prices, and an experimentation setup with a control group. A rules based version, which FT Strategies describes as a practical starting point when data is limited, can run before any model is trained.",[83,84,85,86],"Reader level behavior data: pages read, frequency, recency, device, referrer and newsletters","Subscription and cancellation history linked to the same reader identity","Consent records for the tracking the model relies on","Ad revenue per reader or per page, when the publisher wants to weigh ads against subscriptions",[88,89,90,91,92],"Content management system and paywall layer that can vary the wall per request","Subscription and billing platform for packages, prices and offers","Customer data platform or event pipeline for reader signals","Experimentation tool for control groups","Consent management platform",{"steps":94,"guardrails":110,"humanInTheLoop":116,"kpisToInstrument":117,"failureModes":122},[95,98,101,104,107],{"title":96,"detail":97},"Start with rules and a measurement frame","Define the segments and wall rules you can already act on, and set up a holdout group that always gets the current paywall so that every later change has a baseline.",{"title":99,"detail":100},"Train the propensity model on your own readers","Use reader behavior before the first subscription as the features and subscription within a fixed window as the label. Check that the score ranks readers better than recency alone.",{"title":102,"detail":103},"Decide what is allowed to vary","Choose whether the model may change the timing of the wall, the message, the package or all three. FT Strategies lists the access model, the messaging and the packages and prices shown to readers as things a dynamic paywall can adjust, and leaves dynamic pricing out of its article because it needs a completely different set of steps. Get a legal review before any price varies per reader.",{"title":105,"detail":106},"Weigh subscriptions against ads","Where advertising matters, estimate the ad revenue per reader and feed it into the wall decision next to the likelihood to subscribe. Reuters says its system estimates ad revenue per visitor and then decides on the paywall based on how likely that visitor is to convert.",{"title":108,"detail":109},"Review the model on a schedule","Retrain on recent conversions, compare against the holdout every month and retire rules that no longer win in tests.",[111,112,113,114,115],"A holdout group that keeps the existing paywall, so uplift is measured and not assumed","Tracking used for scoring limited to what the reader consented to","No individual pricing without legal review and a notice to the reader","A ceiling on how fast the wall can tighten, so a busy news day cannot lock out loyal readers","Free access for content the publisher treats as public interest, such as emergency information","The commercial and editorial leads set which content is always free, which packages exist and how much advertising revenue the publisher is willing to trade for subscriptions. Analysts review the model against the holdout each month. The model decides per visit, within those limits.",[118,119,120,121],"Conversion rate from reader to subscriber, treatment against holdout","Subscriber lifetime value and churn by acquisition path","Ad revenue per reader for readers who see a wall and those who do not","Share of paywall decisions served within the page load time",[123,126,129,132],{"title":124,"detail":125},"Optimizing for the quick conversion","A model rewards offers that convert and cancel early. Measure lifetime value and cancellation, not only the first subscription.",{"title":127,"detail":128},"Ad revenue falls unnoticed","More walls shrink the pool of anonymous readers. Track ad revenue per reader in the same dashboard as conversion.",{"title":130,"detail":131},"Training on a biased sample","A model trained only on readers who were already shown a wall learns that wall. Keep a small share of readers on random walls so the model sees alternatives.",{"title":133,"detail":134},"Consent drift","New signals are added to the model without checking what the reader agreed to. Review features against the consent record at every retraining.",{"euAiAct":136,"regulations":139,"guidance":143,"controls":155,"incidents":160},{"tier":137,"basis":138},"minimal","Scoring readers for a subscription offer is not an Annex III use and does not interact with people in a way that triggers the transparency duties of Article 50. It would move toward a more regulated setting if the same score decided access to an essential service. The relevant law is data protection: profiling for marketing needs a lawful basis and respects the right to object.",[140,141,142],"gdpr","uk-gdpr","eu-ai-act",[144,150],{"title":145,"issuer":146,"region":147,"url":148,"note":149},"Regulation (EU) 2016\u002F679, Articles 21 and 22 on profiling","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","Gives the data subject a right to object to profiling for direct marketing and limits decisions based solely on automated processing that significantly affect them.",{"title":151,"issuer":152,"region":147,"url":153,"note":154},"Guidance on the use of storage and access technologies (PECR)","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fdirect-marketing-and-privacy-and-electronic-communications\u002Fguidance-on-the-use-of-storage-and-access-technologies\u002F","Explains when cookies and similar technologies need consent under the UK rules that implement the ePrivacy Directive, which applies to the tracking signals a reader level model relies on.",[156,157,158,159],"Consent and lawful basis recorded for each signal used in the score","Documented model features, owner and monthly review against the holdout","A way for the reader to object to personalization and see a standard paywall","Legal review before any individual price or discount is set by the model",[],{"howToBuild":162},"The scoring model and the wall itself stay in the publisher's own stack, next to the content\nmanagement and subscription systems. On Blits.ai the related build is the conversational layer\naround the subscription decision: an **AI agent** on the website, in the app or on **WhatsApp**\nthat answers questions about packages and prices from a **knowledge base**, helps a reader\nfinish checkout and hands cancellations to a person through **human handover**. A **custom\nfunction** can read the reader's propensity band from the publisher's API so the agent knows\nwhich offer the paywall has already shown.\n\n**SQL knowledge bases** let analysts ask questions in plain language over the subscription and\nconversion tables, and **analytics** show which questions precede a subscription or a\ncancellation. **Guardrails** and **PII masking** keep reader data out of prompts, and **test\nsuites** check that the agent quotes the right prices after each change. The platform is model\nagnostic and supports EU and UAE data residency.",[164,167,170,173],{"question":165,"answer":166},"What does a dynamic paywall change compared with a meter?","A meter applies a fixed rule such as three free articles in 30 days. A dynamic paywall changes when the wall appears, what content it gates, which message it shows and which package it promotes, depending on the reader. In 2018 NZZ served its payment prompt to readers who reached the top 20 percent of a propensity to subscribe score.",{"question":168,"answer":169},"How much do conversion rates rise with an AI paywall?","Published figures come from the organizations themselves. FT Strategies, the consulting arm of the Financial Times, which also sells paywall consulting, reports a 92% increase in conversion rate and a 78% uplift in subscriber lifetime value at the Financial Times, without a baseline. NZZ said in 2018 that conversion rose fivefold over three years, but Digiday dates its propensity scoring from 2018, so much of that rise came before the model. Reuters reports no figure and says its advertising business is doing better than before the paywall.",{"question":171,"answer":172},"Does a dynamic paywall reduce advertising revenue?","It can, which is why Reuters builds the expected ad revenue of each visitor into the decision. Reuters says subscribers still see ads, visit often and are authenticated, and that its advertising business is doing better than before the paywall launched.",{"question":174,"answer":175},"Is this the same as dynamic pricing?","FT Strategies treats them as separate subjects. It counts the access model, the messaging and the packages and prices shown to readers as things a dynamic paywall can adjust, and leaves dynamic pricing out of its article because it requires a completely different set of steps.",[177,178],"content-recommendation-and-personalization","personalized-marketing-at-scale","2026-10-13",[181],{"date":179,"note":182},"First published","dynamic-paywall-and-subscription-propensity",[185,219,248,270],{"title":186,"useCases":187,"organization":188,"vendors":193,"summary":197,"stage":198,"year":78,"channels":199,"languages":200,"metrics":202,"outcomeDisclosed":209,"sources":210,"verification":214,"grade":216,"id":217,"organizationSlug":218},"Financial Times: AI paywall built with Zuora",[183],{"name":189,"anonymized":190,"country":191,"region":147,"industry":17,"wikidata":192},"Financial Times",false,"GB","Q183399",[194],{"name":195,"role":196},"Zuora","platform","The Financial Times launched a dynamic paywall about a year before November 2025, with Zuora as its technology partner. FT Strategies, the specialist consultancy from the Financial Times, which sells paywall consulting to news publishers, says the AI paywall has become a key driver of subscription growth, with a 92% increase in conversion rate, a 118% increase in progression through the subscription funnel and a 78% uplift in subscriber lifetime value. It also says the paywall reduced manual overhead and freed engineering capacity.","production",[],[201],"en",[203],{"kpi":35,"value":204,"unit":205,"qualifier":206,"claimant":207,"quote":208,"sourceUrl":77},92,"percent","exact","organization","The AI paywall has become a key driver of subscription growth, with a 92% increase in conversion rate, a 118% increase in progression through the subscription funnel and a 78% uplift in subscriber lifetime value.",true,[211],{"url":77,"title":76,"publisher":212,"date":213},"FT Strategies","2025-11-20",{"level":215,"checkedAt":179},"source-verified","B","financial-times-ai-paywall-zuora",null,{"title":220,"useCases":221,"organization":222,"vendors":227,"summary":230,"stage":198,"year":231,"channels":232,"languages":233,"metrics":234,"outcomeDisclosed":209,"sources":242,"verification":246,"grade":216,"id":247,"organizationSlug":218},"The Wall Street Journal: dynamic paywall driven by a propensity to subscribe model",[183],{"name":223,"anonymized":190,"country":224,"region":225,"industry":17,"wikidata":226},"The Wall Street Journal","US","north-america","Q164746",[228],{"name":223,"role":229},"in-house","The Wall Street Journal describes a dynamic paywall in which machine learning and a proprietary algorithm predict each visitor's likelihood to subscribe, moving away from giving everyone the same experience. That likelihood sets the paywall experience the visitor gets, in real time, up to 15 million times a week. The listed objectives include the capacity to deal with 15 million visitors a week and the ability to increase subscription sales without impacting advertising capacity. The session description gives no results; the only outcome like statement is in a speaker biography, which credits machine learning models with the Journal's highest levels of membership ever.",2018,[],[201],[235],{"kpi":37,"value":236,"unit":237,"qualifier":238,"period":239,"claimant":207,"quote":240,"sourceUrl":241},15000000,"count","up-to","per week","This takes place in real time, up to 15M times per week.","https:\u002F\u002Fconferences.oreilly.com\u002Fartificial-intelligence\u002Fai-eu-2018\u002Fpublic\u002Fschedule\u002Fdetail\u002F69832.html",[243],{"url":241,"title":244,"publisher":245},"The WSJ dynamic paywall","O'Reilly Artificial Intelligence Conference",{"level":215,"checkedAt":179},"wsj-dynamic-paywall-propensity-model",{"title":249,"useCases":250,"organization":251,"vendors":254,"summary":256,"stage":198,"year":257,"channels":258,"languages":259,"metrics":260,"outcomeDisclosed":190,"sources":261,"verification":267,"grade":268,"id":269,"organizationSlug":218},"Reuters: dynamic paywall decides per visitor whether to show the subscription wall",[183],{"name":252,"anonymized":190,"country":191,"region":147,"industry":17,"wikidata":253},"Reuters","Q130879",[255],{"name":252,"role":229},"Reuters launched a dynamic paywall in 2026 on top of its subscription offering. Phil Andraos, general manager for digital, told the Digiday Publishing Summit that a machine learning system uses user behavior and content performance to estimate how much ad revenue a visitor could generate, then decides whether to show that visitor a paywall based on how likely they are to convert. The paywall tightens when the news cycle is busy and loosens when it is slow. Reuters states that its advertising business is doing better than before the paywall and gives no subscriber or conversion figures.",2026,[],[201],[],[262],{"url":263,"title":264,"publisher":265,"date":266},"https:\u002F\u002Fdigiday.com\u002Fmedia\u002Fhow-reuters-dynamic-paywall-is-boosting-subscriptions-and-ads\u002F","How Reuters' dynamic paywall is boosting subscriptions and ads","Digiday","2026-09-16",{"level":215,"checkedAt":179},"C","reuters-dynamic-paywall",{"title":271,"useCases":272,"organization":273,"vendors":277,"summary":279,"stage":198,"year":231,"channels":280,"languages":281,"metrics":283,"outcomeDisclosed":209,"sources":290,"verification":294,"grade":268,"id":295,"organizationSlug":218},"Neue Zürcher Zeitung: propensity to subscribe scoring and a personalized payment prompt",[183],{"name":274,"anonymized":190,"country":275,"region":147,"industry":17,"wikidata":276},"Neue Zürcher Zeitung","CH","Q658945",[278],{"name":274,"role":229},"NZZ, the Swiss German language newspaper, requires readers to register and eventually pay. It scores registered users on about 30 attributes, such as time spent on articles, frequency and newsletters received, to rate their propensity to subscribe, and shows the payment prompt to readers in the top 20 percent of that score. Between 100 and 150 rules based on reading history, device and time of day change the message, text, placement and color of the prompt. Managing director Steven Neubauer says conversion rose fivefold in three years. The report dates from 2018, so it shows an early production system.",[],[282],"de",[284],{"kpi":35,"value":285,"unit":286,"qualifier":206,"period":287,"claimant":207,"quote":288,"sourceUrl":289},5,"multiplier","over three years, up to June 2018","The 200-year old German-language publisher said it has increased conversion rate by fivefold in the last three years by using this system, with 2.5 percent of people who view a payment message prompted to subscribe.","https:\u002F\u002Fdigiday.com\u002Fmedia\u002Fswiss-news-publisher-nzz-built-flexible-paywall-using-machine-learning\u002F",[291],{"url":289,"title":292,"publisher":265,"date":293},"How Swiss news publisher NZZ built a flexible paywall using machine learning","2018-06-19",{"level":215,"checkedAt":179},"nzz-propensity-paywall",0,[298,304,308],{"kpi":35,"label":299,"unit":205,"aggregate":209,"higherIsBetter":209,"n":300,"nUpTo":296,"median":204,"min":204,"max":204,"byClaimant":301,"vendorOnly":190,"points":302},"Conversion uplift",1,{"organization":300,"vendor":296,"regulator":296,"independent":296},[303],{"evidenceId":217,"organization":189,"value":204,"qualifier":206,"claimant":207,"grade":216,"pooled":209},{"kpi":35,"label":299,"unit":286,"aggregate":209,"higherIsBetter":209,"n":300,"nUpTo":296,"median":285,"min":285,"max":285,"byClaimant":305,"vendorOnly":190,"points":306},{"organization":300,"vendor":296,"regulator":296,"independent":296},[307],{"evidenceId":295,"organization":274,"value":285,"qualifier":206,"claimant":207,"grade":268,"pooled":209},{"kpi":37,"label":309,"unit":237,"aggregate":190,"higherIsBetter":209,"n":296,"nUpTo":300,"median":218,"min":218,"max":218,"byClaimant":310,"vendorOnly":190,"points":311},"Interactions handled",{"organization":296,"vendor":296,"regulator":296,"independent":296},[312],{"evidenceId":247,"organization":223,"value":236,"qualifier":238,"claimant":207,"grade":216,"pooled":190},{"low":314,"high":315},38400,1600000,[317,334,364,387],{"slug":177,"title":318,"shortTitle":319,"definition":320,"status":9,"industries":321,"functions":322,"patterns":323,"audience":324,"autonomy":27,"adoptionStage":325,"segment":326,"evidenceCount":285,"publicEvidenceCount":285,"organizations":327,"bestGrade":216,"headline":218,"lastVerified":333,"indexable":209},"AI recommendation and personalization engine for streaming and media","Content recommendation and personalization","AI content recommendation and personalization decides what to show each viewer or listener next on a home page, in search or in a personalized playlist. It learns from that person's own viewing or listening history, ratings and context, updates continuously as new content is added and behavior changes, and ranks only the catalog's own content, not marketing offers or campaigns.",[17],[19,21],[24,23],"back-office","mainstream","content discovery",[328,329,330,331,332],"Globo","Golden State Warriors","La Liga","Netflix, Inc.","Spotify","2026-09-28",{"slug":178,"title":335,"shortTitle":336,"definition":337,"status":9,"industries":338,"functions":343,"patterns":344,"audience":324,"autonomy":346,"adoptionStage":325,"evidenceCount":347,"publicEvidenceCount":348,"organizations":349,"bestGrade":216,"headline":359,"lastVerified":363,"indexable":209},"AI marketing personalization at scale","Marketing personalization at scale","AI marketing personalization at scale is AI that decides, for each customer, which product, offer, message or content to show next, for a marketing team running many campaigns. It works across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the team's engine across many campaigns and channels, not an agent that converses with the customer.",[339,340,17,341,342],"cross-industry","travel-and-hospitality","retail-and-ecommerce","banking",[19,20],[24,23,345],"content-generation","supervised-agent",10,9,[350,351,352,353,354,355,356,357,358],"Amazon","Catchtable","Commonwealth Bank of Australia","Natura","Radisson Hotel Group","Square Enix","Swarovski","Techcombank","Virgin Voyages",{"kpi":35,"label":299,"unit":205,"n":300,"nUpTo":296,"kind":360,"value":361,"qualifier":206,"claimant":362,"organization":351,"vendorReported":209},"reported",30,"vendor","2026-09-27",{"slug":365,"title":366,"shortTitle":367,"definition":368,"status":9,"industries":369,"functions":371,"patterns":373,"audience":324,"autonomy":346,"adoptionStage":325,"segment":375,"evidenceCount":285,"publicEvidenceCount":285,"organizations":376,"bestGrade":216,"headline":382,"lastVerified":363,"indexable":209},"churn-prediction-and-retention-offers","AI for telecom churn prediction and retention offers","Churn prediction and retention","AI churn prediction and retention is AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals. It 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.",[370],"telecommunications",[19,372,20,21],"customer-service",[23,24,374],"conversational-agent","middle-office",[377,378,379,380,381],"BT Business","Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":383,"label":384,"unit":205,"n":385,"nUpTo":296,"kind":360,"value":386,"qualifier":206,"claimant":362,"organization":379,"vendorReported":209},"churn-reduction","Churn reduction",2,20,{"slug":388,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":394,"patterns":395,"audience":396,"autonomy":397,"adoptionStage":28,"segment":398,"evidenceCount":285,"publicEvidenceCount":285,"organizations":399,"bestGrade":216,"headline":405,"lastVerified":363,"indexable":209},"next-best-action-for-advisors","AI next best action prompts for wealth advisors","Advisor next best action","AI next best action for advisors is an engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts. Prompts include idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[393,342],"wealth-and-asset-management",[20,19,21],[24,23,345],"employee-facing","assist","front-office",[400,401,402,403,404],"CIMB Niaga","Citi","JPMorgan Chase","Morgan Stanley","UBS",{"kpi":406,"label":407,"unit":205,"n":300,"nUpTo":296,"kind":360,"value":408,"qualifier":206,"claimant":207,"organization":404,"vendorReported":190},"employee-adoption","Employee adoption",80,{"indexable":209,"reasons":410},[],[412,417,421,429,436,441,447,454,462,469,476,482,488,495,502,509,515,521,528,533,538,545,551,558,563,568,573,578,585,591,598,604,610,616,623,628],{"id":142,"label":413,"issuer":146,"region":147,"url":414,"description":415,"useCases":416,"indexable":209},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":140,"label":418,"issuer":146,"region":147,"url":148,"description":419,"useCases":420,"indexable":209},"GDPR","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":422,"label":423,"issuer":424,"region":425,"url":426,"description":427,"useCases":428,"indexable":209},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":430,"label":431,"issuer":432,"region":225,"url":433,"description":434,"useCases":435,"indexable":209},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":141,"label":437,"issuer":152,"region":147,"url":438,"description":439,"useCases":440,"indexable":209},"UK GDPR","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":442,"label":443,"issuer":146,"region":147,"url":444,"description":445,"useCases":446,"indexable":209},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":448,"label":449,"issuer":450,"region":147,"url":451,"description":452,"useCases":453,"indexable":209},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":455,"label":456,"issuer":457,"region":458,"url":459,"description":460,"useCases":461,"indexable":209},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":463,"label":464,"issuer":465,"region":458,"url":466,"description":467,"useCases":468,"indexable":209},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":470,"label":471,"issuer":472,"region":225,"url":473,"description":474,"useCases":475,"indexable":209},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":477,"label":478,"issuer":146,"region":147,"url":479,"description":480,"useCases":481,"indexable":209},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":483,"label":484,"issuer":485,"region":425,"url":486,"description":487,"useCases":481,"indexable":209},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":489,"label":490,"issuer":491,"region":147,"url":492,"description":493,"useCases":494,"indexable":209},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":496,"label":497,"issuer":498,"region":225,"url":499,"description":500,"useCases":501,"indexable":209},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":503,"label":504,"issuer":505,"region":425,"url":506,"description":507,"useCases":508,"indexable":209},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":510,"label":511,"issuer":146,"region":147,"url":512,"description":513,"useCases":514,"indexable":209},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":516,"label":517,"issuer":518,"region":225,"url":519,"description":520,"useCases":514,"indexable":209},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":522,"label":523,"issuer":524,"region":225,"url":525,"description":526,"useCases":527,"indexable":209},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":529,"label":530,"issuer":146,"region":147,"url":531,"description":532,"useCases":527,"indexable":209},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":534,"label":535,"issuer":146,"region":147,"url":536,"description":537,"useCases":527,"indexable":209},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":539,"label":540,"issuer":541,"region":225,"url":542,"description":543,"useCases":544,"indexable":209},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":546,"label":547,"issuer":548,"region":425,"url":549,"description":550,"useCases":544,"indexable":209},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":552,"label":553,"issuer":554,"region":147,"url":555,"description":556,"useCases":557,"indexable":209},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":559,"label":560,"issuer":146,"region":147,"url":561,"description":562,"useCases":557,"indexable":209},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":564,"label":565,"issuer":146,"region":147,"url":566,"description":567,"useCases":557,"indexable":209},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":569,"label":570,"issuer":457,"region":458,"url":571,"description":572,"useCases":347,"indexable":209},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":574,"label":575,"issuer":146,"region":147,"url":576,"description":577,"useCases":347,"indexable":209},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":579,"label":580,"issuer":581,"region":225,"url":582,"description":583,"useCases":584,"indexable":209},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":586,"label":587,"issuer":146,"region":147,"url":588,"description":589,"useCases":590,"indexable":209},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":592,"label":593,"issuer":594,"region":595,"url":596,"description":597,"useCases":285,"indexable":209},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":599,"label":600,"issuer":146,"region":147,"url":601,"description":602,"useCases":603,"indexable":209},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":605,"label":606,"issuer":607,"region":147,"url":608,"description":609,"useCases":603,"indexable":209},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":611,"label":612,"issuer":613,"region":147,"url":614,"description":615,"useCases":603,"indexable":209},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":617,"label":618,"issuer":619,"region":458,"url":620,"description":621,"useCases":622,"indexable":209},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":624,"label":625,"issuer":146,"region":147,"url":626,"description":627,"useCases":622,"indexable":209},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":629,"label":630,"issuer":631,"region":225,"url":632,"description":633,"useCases":622,"indexable":209},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904356525]