[{"data":1,"prerenderedAt":630},["ShallowReactive",2],{"uc-proactive-outbound-engagement-agent":3,"uc-regulations":425},{"useCase":4,"evidence":204,"blitsAiDeployments":300,"benchmarks":301,"indicative":308,"related":311,"indexability":423,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":28,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":45,"valueDrivers":46,"kpis":50,"indicativeValue":56,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":149,"blitsAi":180,"faq":182,"related":192,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","AI agents for proactive bank customer outreach","AI agents that contact bank customers first about card activation, fee alerts and retention. Bank of America reports over 1.7 billion proactive Erica insights.","published","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[12,13,14,15],"outbound banking agent","card activation outreach","dormant account reactivation","event triggered customer outreach",[17,18],"banking","payments",[20,21,22],"marketing","sales","customer-service",[24,25,26,27],"conversational-agent","voice-agent","agentic-workflow","recommendation-and-personalization",[29,30,31,32,33],"whatsapp","sms","voice","mobile-app","email","customer-facing","supervised-agent","emerging","front-office","Banks pay to acquire customers and products that then sit unused. A new card that never leaves the\ndrawer earns nothing, a savings account opened for a promotion goes dormant, and a customer who\nkeeps paying overdraft fees has a reason to look at another bank. The bank usually knows the\nmoment to act (a card not used 30 days after delivery, a balance heading below zero, a fixed rate\nending), but the tools it has are weak: push notifications and emails that are easy to ignore,\nor human outbound calls that are costly to scale beyond high value products.\n\nWhat is missing is a way to have a short, useful two way conversation at scale: answer the\ncustomer's question, complete the action (activate, set up a transfer, accept an offer they\nalready qualify for) and leave them alone when they say no. Because some of these conversations\ntouch credit and fees, they are regulated conversations, not marketing copy.",[40],{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":44},"Forrester's survey of banking customers, as reported by Mi3 in 2025, found that the thing customers most want (60 percent) is to be alerted when there is not enough money in their account to cover an upcoming expense.","Westpac blows app rivals away as Forrester rates Australia among world's best – but Big Four still missing key customer aspirations","https://www.mi-3.com.au/01-10-2025/big-four-deliver-ai-nudges-and-cautious-cleverness-their-banking-apps-forrester",2025,"1. **The bank decides who and why.** Event triggers and campaign lists come from the bank's own\n   systems (core banking, the decision engine, CRM), including eligibility for any offer. The\n   agent does not choose targets.\n2. **Check consent and contact rules.** Before any message or call the agent checks marketing\n   consent where needed, frequency caps, quiet hours and the customer's preferred channel.\n3. **Open with the reason.** The agent says it is an AI assistant, names the bank and states the\n   specific reason for contact (\"your new card has not been activated\").\n4. **Converse and act.** It answers questions from the customer's own account data and approved\n   product content, and completes the action through allow listed APIs after the right\n   authentication: activate the card, set up a transfer to avoid a fee, book a call, accept a\n   pre approved offer.\n5. **Respect no.** An opt out or a \"not now\" is recorded at once and suppresses further contact\n   on that topic.\n6. **Hand over.** Complaints, hardship, vulnerability and complex product questions go to a\n   human with the conversation attached.\n7. **Write back the outcome.** Results flow to CRM so the decision engine learns and the next\n   campaign does not repeat the contact.",[47,48,49],"revenue-growth","customer-experience","cost-to-serve",[51,52,53,54,55],"conversion-rate-uplift","churn-reduction","revenue-uplift","interactions-handled","customer-satisfaction",{"referenceOrg":57,"inputs":58,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A card issuer that issues 100,000 new cards a year",[59,65,72,79],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"newCards","New cards issued per year",100000,"cards per year","The reference issuer.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"inactiveShare","Share of new cards not used within 90 days",0.1,0.2,"fraction of new cards","Editorial assumption, replace with your own activation data.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"activationLift","Share of inactive cards activated because of the outreach",0.05,0.15,"fraction of inactive cards","Editorial assumption; measure against a control group that receives only the usual push and email.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"revenuePerActiveCard","Annual revenue from an active card",50,120,"USD per card per year","Editorial assumption, replace with your own figure.","newCards * inactiveShare * activationLift * revenuePerActiveCard","USD","per year","Annual revenue from cards activated by outreach","Covers card activation only. It leaves out fee avoidance, reactivation of dormant accounts, accepted offers and retention, the cost of messages, calls and the AI, and any complaints from unwanted contact.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","The conversation is straightforward. The hard parts are the triggers and eligibility data, the consent and contact rules per channel and market, and authenticating a customer on a conversation the bank started.",[96,97,98,99],"Event triggers and campaign lists with the reason for contact","Consent, channel preference and suppression data per customer","Eligibility decisions for any offer, made upstream by the bank's decision engine","Approved product and fee content for questions",[101,102,103,104,105],"CRM or customer engagement platform (lists, outcomes, suppression)","Messaging and telephony channels with opt out handling","Core banking and card platform (activation, transfers, limits)","Identity and step up authentication","Contact centre for handover and callbacks",{"steps":107,"guardrails":123,"humanInTheLoop":129,"kpisToInstrument":130,"failureModes":136},[108,111,114,117,120],{"title":109,"detail":110},"Start with service triggers, not sales","Card activation, low balance and payment reminders help the customer and build trust in the channel. Add offers only after service outreach performs well.",{"title":112,"detail":113},"Keep the campaign brain outside the agent","Targeting, eligibility and offer terms come from the bank's systems and are passed to the agent as facts. The agent explains and executes; it does not decide who qualifies.",{"title":115,"detail":116},"Build consent and frequency into the trigger","Check consent, caps and quiet hours before the first message, per channel and market, and log the check with the contact.",{"title":118,"detail":119},"Authenticate on the way in","When the bank starts the contact, the customer must still prove who they are before any action, ideally in the app. Never ask for secrets in an outbound message.",{"title":121,"detail":122},"Test against a control group","Hold out a random share of each trigger and compare activation, fees and churn, so the results are yours and not the vendor's.",[124,125,126,127,128],"The agent only offers products the bank's decision engine has already approved for that customer","AI disclosure and the reason for contact in the first message or sentence","Opt outs honoured immediately across channels","No requests for passcodes or card details in outbound contact","Frequency caps and quiet hours enforced before sending","Marketing and product owners approve every trigger, script and offer before launch. Humans take over for complaints, hardship, vulnerable customers and complex product questions, and a sample of conversations is reviewed each week for tone, accuracy and fair treatment.",[131,132,133,134,135],"Activation, reactivation and acceptance rates against a control group","Opt out and complaint rate per trigger","Fees avoided for customers who acted on a low balance alert","Handover rate and reasons","Churn of contacted customers versus control",[137,140,143,146],{"title":138,"detail":139},"Outreach that feels like spam","Too many contacts or vague reasons drive opt outs and complaints. Cap frequency, lead with the reason and stop when the customer says no.",{"title":141,"detail":142},"The agent starts deciding eligibility","Free form offers creep into the conversation. Pass eligibility in as data and block offers that were not approved upstream.",{"title":144,"detail":145},"Scam lookalike messages","Outbound bank messages are what scammers imitate. Never ask for secrets, and point customers to the app to act.",{"title":147,"detail":148},"No measurable effect","Without a control group every activation looks like a win. Hold out a share of each trigger from day one.",{"euAiAct":150,"regulations":153,"guidance":160,"controls":173,"incidents":179},{"tier":151,"basis":152},"limited","A customer facing agent must disclose that it is AI (Article 50(1)). It stays out of Annex III as long as eligibility for credit offers is decided upstream by the bank's own, separately governed credit processes; if the agent itself assessed creditworthiness it would be high risk under point 5(b).",[154,155,156,157,158,159],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","dora","us-tcpa",[161,167],{"title":162,"issuer":163,"region":164,"url":165,"note":166},"Guide to Privacy and Electronic Communications Regulations","Information Commissioner's Office","europe","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/guide-to-pecr/","UK rules on marketing by phone, text and email, including consent and opt out. The ICO says the guide is under review after the Data (Use and Access) Act 2025.",{"title":168,"issuer":169,"region":170,"url":171,"note":172},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","north-america","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","AI generated voices count as \"artificial or prerecorded voice\" under the TCPA, so US outbound AI calls need the consent the TCPA requires.",[174,175,176,177,178],"Consent, frequency and quiet hour checks logged per contact","Approved scripts and offer terms per trigger with an accountable owner","Audit trail of every contact, answer and action taken","Suppression lists shared across channels","Monitoring of complaints and outcomes for vulnerable customers",[],{"howToBuild":181},"On Blits.ai each trigger is an **agentic workflow** started by the bank's systems through an API\ntoken or on a schedule, with the customer, the reason and any pre approved offer passed in as\ndata. Blits.ai can send the first message itself on the **email channel**, which works inbound\nand outbound. For outreach in the bank's own app, the app starts the contact and hands the\nconversation to the **AI agent** through the **REST or WebSocket API channel**. For WhatsApp, SMS\nand voice, the bank's own messaging or dialler platform sends the first message or places the\ncall, and the agent answers the replies on the **WhatsApp**, **SMS** and **voice telephony**\nchannels. In every case the agent answers from a **knowledge base** of approved product content\nand acts only through **custom functions** that call the bank's APIs, behind an\n**authentication** block.\n\nA **flow** fixes the opening (AI disclosure, reason for contact, opt out) and the consent checks,\nwhile the **GDPR toolkit** handles consent gating and data removal. **Human handover** routes\ncomplaints and hardship to the contact centre, **guardrails** stop the agent from inventing\noffers or asking for secrets, and **test suites** replay each trigger's conversations before\nlaunch. The workflow **run history with analytics** shows outcomes per trigger for comparison with\nthe control group.",[183,186,189],{"question":184,"answer":185},"Is this the same as a marketing campaign tool?","No. The bank's campaign and decision systems still choose who to contact and what they qualify for. The agent is the caller: it explains, answers questions, completes the action and records the outcome.",{"question":187,"answer":188},"Do banks already reach out proactively with AI?","Yes, but mostly as proactive alerts and insights in the app and by message, from an assistant the customer can then talk to. Bank of America says clients have received and interacted with more than 1.7 billion proactive, personalized insights from Erica, and Capital One says its Eno assistant looks out for charges that might surprise the customer and alerts them by text, email and app. We found few published results for AI agents that hold two way outbound conversations or make outbound calls in banking, which is why this page treats the pattern as emerging.",{"question":190,"answer":191},"What consent do outbound AI calls need?","It depends on the market and on whether the call is service or marketing. In the US the FCC has ruled that AI generated voices fall under the TCPA's rules for artificial voices; in the UK PECR governs marketing calls, texts and emails. Build the check into every trigger.",[193,194,195,196,197,198],"outbound-reminder-and-confirmation-agent","offers-and-rewards-agent","financial-wellbeing-coach","fraud-alert-confirmation","collections-and-hardship-agent","personalized-marketing-at-scale","2026-09-27",[201],{"date":199,"note":202},"First published","proactive-outbound-engagement-agent",[205,232,271],{"title":206,"useCases":207,"organization":208,"vendors":212,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":221,"outcomeDisclosed":210,"sources":222,"verification":226,"grade":229,"id":230,"organizationSlug":231},"Capital One: Eno assistant alerts on unexpected card charges",[196,203],{"name":209,"anonymized":210,"country":211,"region":170,"industry":17},"Capital One",false,"US",[213],{"name":209,"role":214},"in-house","Eno is Capital One's virtual assistant. Capital One says it helps protect card accounts by looking out for charges that might surprise the customer, and sends insights when it spots free trials and recurring charges, through text, email and app alerts. Capital One does not publish outcome figures for Eno on this page.","production",2026,[30,33,32],[220],"en",[],[223],{"url":224,"title":225,"publisher":209},"https://www.capitalone.com/digital/tools/eno/","Eno, your Capital One assistant",{"level":227,"checkedAt":228},"source-verified","2026-09-26","B","capital-one-eno-assistant",null,{"title":233,"useCases":234,"organization":237,"vendors":239,"summary":241,"stage":242,"year":44,"channels":243,"languages":244,"metrics":245,"outcomeDisclosed":260,"sources":261,"verification":268,"grade":229,"id":269,"organizationSlug":270},"Bank of America: Erica, a virtual financial assistant with proactive insights",[195,194,235,203,236],"first-line-contact-centre-agent","branch-and-appointment-booking-agent",{"name":238,"anonymized":210,"country":211,"region":170,"industry":17},"Bank of America",[240],{"name":238,"role":214},"Erica, launched in 2018, is Bank of America's virtual financial assistant in its Mobile Banking app. Beyond answering questions it delivers proactive, personalized insights: BankAmeriDeals cash back deals based on the client's spending, where balances are trending over the next seven days and eligibility for the Preferred Rewards program. It also gives guidance on investment topics for Merrill clients and hands off to people by scheduling appointments. The bank reports that clients have received and interacted with more than 1.7 billion of these insights, and that most users find the information they need, which it links to lower call centre volume. Bank of America says Erica selects answers from a predefined set and does not use generative AI or large language models.","scaled",[32],[220],[246,255],{"kpi":247,"value":248,"unit":249,"qualifier":250,"period":251,"claimant":252,"quote":253,"sourceUrl":254},"users-served",50000000,"count","approximately","since launch in 2018, as of August 2025","organization","assisting nearly 50 million users since launch, surpassing 3 billion client interactions, and now averaging more than 58 million interactions per month","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/08/a-decade-of-ai-innovation--bofa-s-virtual-assistant-erica-surpas.html",{"kpi":54,"value":256,"unit":249,"qualifier":257,"period":258,"claimant":252,"quote":259,"sourceUrl":254},3000000000,"at-least","client interactions since launch in 2018, as of August 2025","surpassing 3 billion client interactions",true,[262,265],{"url":254,"title":263,"publisher":238,"date":264},"A Decade of AI Innovation: BofA's Virtual Assistant Erica Surpasses 3 Billion Client Interactions","2025-08-20",{"url":266,"title":267,"publisher":238},"https://info.bankofamerica.com/en/digital-banking/erica","Erica: Virtual Financial Assistant",{"level":227,"checkedAt":199},"bank-of-america-erica-virtual-assistant","bank-of-america",{"title":272,"useCases":273,"organization":275,"vendors":279,"summary":283,"stage":242,"year":284,"channels":285,"languages":287,"metrics":288,"outcomeDisclosed":210,"sources":289,"verification":297,"grade":229,"id":298,"organizationSlug":299},"Commonwealth Bank: Customer Engagement Engine for next best conversations",[194,195,274,203,198],"loan-restructuring-recommendations",{"name":276,"anonymized":210,"country":277,"region":278,"industry":17},"Commonwealth Bank of Australia","AU","asia-pacific",[280],{"name":281,"role":282},"Pegasystems","platform","Commonwealth Bank's Customer Engagement Engine (CEE), built on Pega Customer Decision Hub, suggests in real time the next best conversation to have with each customer, whether in the branch, on the phone, online or on a mobile device. Beyond suggesting conversations, the bank uses it to match customers to government benefits and rebates they may be missing (Benefits finder) and to reach customers hit by natural disasters with same day support such as a loan deferral. The same decisions feed digital channels and prompts for branch and contact centre staff.",2022,[32,286],"agent-desktop",[220],[],[290,294],{"url":291,"title":292,"publisher":276,"date":293},"https://www.commbank.com.au/articles/newsroom/2022/06/CBA-artificial-intelligence-usages.html","How artificial intelligence is changing the face of banking","2022-06-24",{"url":295,"title":296,"publisher":281},"https://www.pega.com/customers/cba-marketing","Delivering next best conversations with Pega",{"level":227,"checkedAt":199},"commonwealth-bank-customer-engagement-engine","commonwealth-bank-of-australia",0,[302],{"kpi":54,"label":303,"unit":249,"aggregate":210,"higherIsBetter":260,"n":304,"nUpTo":300,"median":256,"min":256,"max":256,"byClaimant":305,"vendorOnly":210,"points":306},"Interactions handled",1,{"organization":304,"vendor":300,"regulator":300,"independent":300},[307],{"evidenceId":269,"organization":238,"value":256,"qualifier":257,"claimant":252,"grade":229,"pooled":260},{"low":309,"high":310},25000,360000,[312,332,345,358,377,400],{"slug":193,"title":313,"shortTitle":314,"definition":315,"status":9,"industries":316,"functions":320,"patterns":322,"audience":34,"autonomy":35,"adoptionStage":324,"evidenceCount":325,"publicEvidenceCount":326,"organizations":327,"bestGrade":229,"headline":231,"lastVerified":228,"indexable":260},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[317,318,319],"cross-industry","healthcare","government",[22,321],"operations",[25,24,26,323],"prediction-and-scoring","early-adopters",5,4,[328,329,330,331],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health",{"slug":194,"title":333,"shortTitle":334,"definition":335,"status":9,"industries":336,"functions":337,"patterns":338,"audience":34,"autonomy":339,"adoptionStage":340,"segment":37,"evidenceCount":341,"publicEvidenceCount":342,"organizations":343,"bestGrade":229,"headline":231,"lastVerified":199,"indexable":260},"AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[17,18],[20,21,22],[27,323,24],"autonomous","mainstream",8,3,[238,276,344],"DBS Bank",{"slug":195,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":350,"patterns":351,"audience":34,"autonomy":35,"adoptionStage":324,"segment":37,"evidenceCount":341,"publicEvidenceCount":352,"organizations":353,"bestGrade":229,"headline":231,"lastVerified":199,"indexable":260},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[17],[22,20],[24,27,323,26],6,[238,276,354,355,356,357],"Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":196,"title":359,"shortTitle":360,"definition":361,"status":9,"industries":362,"functions":363,"patterns":365,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":325,"publicEvidenceCount":325,"organizations":366,"bestGrade":229,"headline":369,"lastVerified":199,"indexable":260},"AI agent for fraud alert confirmation with cardholders","Fraud alert confirmation","A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.",[17,18],[364,22],"fraud-prevention",[24,25,26],[209,276,367,368,357],"Macquarie Bank","Revolut",{"kpi":370,"label":371,"unit":372,"n":373,"nUpTo":300,"kind":374,"value":375,"qualifier":376,"claimant":252,"organization":276,"vendorReported":210},"fraud-loss-reduction","Fraud loss reduction","percent",2,"reported",76,"exact",{"slug":197,"title":378,"shortTitle":379,"definition":380,"status":9,"industries":381,"functions":386,"patterns":388,"audience":34,"autonomy":35,"adoptionStage":324,"segment":390,"evidenceCount":342,"publicEvidenceCount":373,"organizations":391,"bestGrade":394,"headline":395,"lastVerified":199,"indexable":260},"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.",[317,17,18,382,383,384,385],"telecommunications","energy-and-utilities","automotive","professional-services",[387,22],"collections-and-recovery",[25,24,26,389],"classification-and-routing","lending",[392,393],"Day Knight & Associates","SameDay Auto Finance","C",{"kpi":396,"label":397,"unit":372,"n":373,"nUpTo":300,"kind":374,"value":398,"qualifier":376,"claimant":399,"organization":393,"vendorReported":260},"cost-reduction","Cost reduction",75,"vendor",{"slug":198,"title":401,"shortTitle":402,"definition":403,"status":9,"industries":404,"functions":408,"patterns":409,"audience":411,"autonomy":35,"adoptionStage":340,"evidenceCount":341,"publicEvidenceCount":412,"organizations":413,"bestGrade":229,"headline":420,"lastVerified":199,"indexable":260},"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.",[317,405,406,407,17],"travel-and-hospitality","media-and-entertainment","retail-and-ecommerce",[20,21],[27,323,410],"content-generation","back-office",7,[414,415,276,416,417,418,419],"Amazon","Catchtable","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":51,"label":421,"unit":372,"n":304,"nUpTo":300,"kind":374,"value":422,"qualifier":376,"claimant":399,"organization":415,"vendorReported":260},"Conversion uplift",30,{"indexable":260,"reasons":424},[],[426,432,437,445,452,457,462,468,475,482,489,495,502,509,515,520,527,533,539,545,551,555,561,566,571,578,584,589,594,601,607,613,619,624],{"id":154,"label":427,"issuer":428,"region":164,"url":429,"description":430,"useCases":431,"indexable":260},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":155,"label":433,"issuer":428,"region":164,"url":434,"description":435,"useCases":436,"indexable":260},"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":438,"label":439,"issuer":440,"region":441,"url":442,"description":443,"useCases":444,"indexable":260},"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":446,"label":447,"issuer":448,"region":170,"url":449,"description":450,"useCases":451,"indexable":260},"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":158,"label":453,"issuer":428,"region":164,"url":454,"description":455,"useCases":456,"indexable":260},"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":156,"label":458,"issuer":163,"region":164,"url":459,"description":460,"useCases":461,"indexable":260},"UK GDPR","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":157,"label":463,"issuer":464,"region":164,"url":465,"description":466,"useCases":467,"indexable":260},"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":469,"label":470,"issuer":471,"region":278,"url":472,"description":473,"useCases":474,"indexable":260},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":476,"label":477,"issuer":478,"region":278,"url":479,"description":480,"useCases":481,"indexable":260},"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":483,"label":484,"issuer":485,"region":441,"url":486,"description":487,"useCases":488,"indexable":260},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":490,"label":491,"issuer":492,"region":170,"url":493,"description":494,"useCases":488,"indexable":260},"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":496,"label":497,"issuer":498,"region":164,"url":499,"description":500,"useCases":501,"indexable":260},"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":503,"label":504,"issuer":505,"region":441,"url":506,"description":507,"useCases":508,"indexable":260},"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":510,"label":511,"issuer":428,"region":164,"url":512,"description":513,"useCases":514,"indexable":260},"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":516,"label":517,"issuer":428,"region":164,"url":518,"description":519,"useCases":514,"indexable":260},"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":521,"label":522,"issuer":523,"region":170,"url":524,"description":525,"useCases":526,"indexable":260},"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":528,"label":529,"issuer":428,"region":164,"url":530,"description":531,"useCases":532,"indexable":260},"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":534,"label":535,"issuer":536,"region":170,"url":537,"description":538,"useCases":532,"indexable":260},"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":540,"label":541,"issuer":542,"region":441,"url":543,"description":544,"useCases":532,"indexable":260},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":546,"label":547,"issuer":428,"region":164,"url":548,"description":549,"useCases":550,"indexable":260},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":159,"label":552,"issuer":169,"region":170,"url":553,"description":554,"useCases":550,"indexable":260},"Telephone Consumer Protection Act","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":556,"label":557,"issuer":471,"region":278,"url":558,"description":559,"useCases":560,"indexable":260},"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":562,"label":563,"issuer":428,"region":164,"url":564,"description":565,"useCases":560,"indexable":260},"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":567,"label":568,"issuer":428,"region":164,"url":569,"description":570,"useCases":560,"indexable":260},"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":572,"label":573,"issuer":574,"region":164,"url":575,"description":576,"useCases":577,"indexable":260},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":579,"label":580,"issuer":581,"region":170,"url":582,"description":583,"useCases":341,"indexable":260},"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":585,"label":586,"issuer":428,"region":164,"url":587,"description":588,"useCases":341,"indexable":260},"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":590,"label":591,"issuer":428,"region":164,"url":592,"description":593,"useCases":352,"indexable":260},"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":595,"label":596,"issuer":597,"region":598,"url":599,"description":600,"useCases":325,"indexable":260},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":602,"label":603,"issuer":604,"region":164,"url":605,"description":606,"useCases":326,"indexable":260},"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":608,"label":609,"issuer":610,"region":164,"url":611,"description":612,"useCases":326,"indexable":260},"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":614,"label":615,"issuer":616,"region":278,"url":617,"description":618,"useCases":342,"indexable":260},"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":620,"label":621,"issuer":428,"region":164,"url":622,"description":623,"useCases":342,"indexable":260},"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":625,"label":626,"issuer":627,"region":170,"url":628,"description":629,"useCases":342,"indexable":260},"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.",1790598295869]