[{"data":1,"prerenderedAt":700},["ShallowReactive",2],{"uc-financial-wellbeing-coach":3,"uc-regulations":492},{"useCase":4,"evidence":193,"blitsAiDeployments":388,"benchmarks":389,"indicative":406,"related":409,"indexability":490,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":50,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":141,"blitsAi":170,"faq":172,"related":182,"datePublished":188,"dateModified":188,"lastVerified":188,"changelog":189,"slug":192},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","AI financial wellbeing coach for banking apps","An in app AI coach that explains spending, forecasts bills and helps customers save. See how Bank of America's Erica and RBC's NOMI do it, with the risks.","published","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.",[12,13,14,15],"AI money coach","personal finance assistant","financial health assistant","proactive insights",[17],"banking",[19,20],"customer-service","marketing",[22,23,24,25],"conversational-agent","recommendation-and-personalization","prediction-and-scoring","agentic-workflow",[27],"mobile-app","customer-facing","supervised-agent","early-adopters","front-office","Banks see a detailed picture of a customer's financial life in their transaction data. A customer\nwho is surprised by a direct debit, or who does not know how much they can safely save, is often\nleft to work it out from a list of transactions.\n\nForrester's 2025 review of the mobile apps of the four biggest Australian banks found that most\nstill fall short in helping customers improve their overall financial health, while leading banks\nincreasingly use AI powered insights to help customers stay on top of their finances. The\nopportunity is a coach that does the analysis for the customer, speaks up at the right moment and\ncan act on a simple instruction, without drifting into selling or unlicensed advice.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"Forrester's 2025 review of Australian mobile banking apps found that most banks still fall short in helping customers improve their overall financial health.","Conversational AI And Anticipatory Insights, What's New In Australian Mobile Banking In 2025","https://www.forrester.com/blogs/conversational-ai-and-anticipatory-insights-whats-new-in-australian-mobile-banking-in-2025/",2025,"1. **Understand the customer's money.** Models categorise transactions, detect income, bills and\n   subscriptions, and forecast the balance over the coming days.\n2. **Speak up at the right moment.** Proactive insights flag a bill that will not be covered, a\n   subscription price rise or a month of unusual spending, with an action attached.\n3. **Answer in conversation.** The customer asks \"can I afford this trip\" or \"where did my money\n   go\" and gets an answer grounded in their own data and the bank's approved guidance content.\n4. **Act within limits.** On instruction it sets up a savings goal, a transfer into a savings pot\n   or a budget, using the same authenticated APIs as the app, and confirms before moving money.\n5. **Know the boundary.** Questions that need regulated advice (investments, pensions, debt\n   solutions) or show signs of financial difficulty go to a human or to the right service.",[41,42,43],"customer-experience","revenue-growth","inclusion-and-access",[45,46,47,48,49],"users-served","interactions-handled","customer-satisfaction","nps-change","churn-reduction",{"referenceOrg":51,"inputs":52,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A retail bank with 1 million digitally active customers",[53,58,65,72],{"key":54,"label":55,"low":56,"high":56,"unit":54,"note":57},"customers","Digitally active customers",1000000,"The reference bank.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"engagedShare","Share of customers who use the coach regularly",0.1,0.25,"fraction of customers","Editorial assumption. For scale, RBC reports more than 900,000 clients used NOMI Forecast in its first 19 months.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"churnPoints","Reduction in annual attrition among engaged users",0.005,0.01,"fraction of engaged customers per year","Editorial assumption; no deployment on this page discloses a retention effect. Measure it with a control group.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"valuePerCustomer","Annual revenue of a retained main bank customer",100,200,"USD per customer per year","Editorial assumption, replace with your own customer economics.","customers * engagedShare * churnPoints * valuePerCustomer","USD","per year","Revenue retained through lower attrition","Retention value only, and the most uncertain input is the attrition effect. It leaves out fees customers avoid (a benefit to them, not the bank), deposit growth from savings features, contact centre calls avoided and the cost of building and running the coach.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Categorisation and forecasting models are well understood; the difficulty is quality (a wrong forecast destroys trust), the boundary with regulated advice, and making insights useful rather than noisy.",[89,90,91,92],"Transaction history with reliable merchant and category enrichment","Scheduled payments, direct debits and income patterns","Approved guidance content on budgeting, saving and financial difficulty","Customer consent and preferences for proactive messages",[94,95,96,97],"Core banking and payments data (read) and savings and transfer APIs (write, with confirmation)","Transaction enrichment and categorisation service","Notification and in app messaging","Referral routes to advice, debt support and the contact centre",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Start with forecasting and alerts","A reliable view of upcoming bills and the projected balance is useful on its own and needs no conversation. RBC's NOMI Forecast (a seven day view of upcoming payments) and Erica's alerts on where balances are trending over the next seven days at Bank of America are examples.",{"title":104,"detail":105},"Add conversation over the customer's own data","Let customers ask about their spending and plans, with answers computed from their data by deterministic functions and explained by the model, never estimated by it.",{"title":107,"detail":108},"Draw the advice line in writing","With compliance, list what the coach may say (facts, general guidance, the bank's own product features) and what triggers a referral (investment, pension or debt advice, signs of financial difficulty).",{"title":110,"detail":111},"Let it act with confirmation","Add savings goals and transfers between the customer's own accounts, each confirmed by the customer, before anything more autonomous.",{"title":113,"detail":114},"Measure outcomes, not clicks","Track fees avoided, savings built and financial difficulty referrals against a control group, not just insight views.",[116,117,118,119,120],"Numbers come from deterministic calculations on the customer's data, never from the model's own arithmetic","A written boundary between guidance and regulated advice, with automatic referral when it is crossed","No sales messages disguised as coaching; product offers are labelled and follow marketing consent","Signs of financial difficulty trigger support routes, not product offers","Any money movement is confirmed by the customer and limited to their own accounts","Customers approve every action. Humans take over for regulated advice and financial difficulty. A conduct and quality team reviews samples of insights and conversations for accuracy, tone and any drift towards selling.",[123,124,125,126,127],"Regular users and repeat use of insights","Forecast accuracy on upcoming balances","Fees avoided and savings built by users versus a control group","Referrals to advice and financial difficulty support","Complaints and satisfaction about the coach",[129,132,135,138],{"title":130,"detail":131},"The coach becomes a sales channel","Insights turn into product pushes and customers stop trusting them. Separate coaching from offers and review the mix.",{"title":133,"detail":134},"Wrong numbers","A misclassified income or a missed direct debit gives a wrong forecast. Compute with deterministic functions, show the basis and let customers correct it.",{"title":136,"detail":137},"Advice by accident","The assistant recommends an investment or a debt product in a way that counts as regulated advice. Enforce the boundary with guardrails and tests.",{"title":139,"detail":140},"Alert fatigue","Too many nudges and customers mute them all. Cap frequency and measure action rates per insight type.",{"euAiAct":142,"regulations":145,"guidance":151,"controls":163,"incidents":169},{"tier":143,"basis":144},"context-dependent","The conversational assistant carries the Article 50 transparency duty: customers must be told they are interacting with an AI system. The system becomes high risk if it is used to evaluate the creditworthiness of natural persons or establish their credit score (Annex III point 5(b)). Article 5(1)(b) prohibits AI that exploits vulnerabilities due to a person's specific social or economic situation to materially distort their behaviour in a way that causes, or is reasonably likely to cause, significant harm.",[146,147,148,149,150],"eu-ai-act","gdpr","uk-consumer-duty","mas-ai-risk-management","iso-42001",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Advice Guidance Boundary Review","Financial Conduct Authority","europe","https://www.fca.org.uk/firms/advice-guidance-boundary-review","The joint HM Treasury and FCA review of the boundary between financial advice and other forms of support. Its targeted support rules, confirmed as final on 26 February 2026 and expected by the FCA to take effect from 6 April 2026, let firms that hold the new targeted support permission suggest options on pensions and retail investments to groups of customers with common characteristics, which bears on how far a coach may go.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Article 5: Prohibited AI practices","European Union","https://artificialintelligenceact.eu/article/5/","Bans manipulative techniques and the exploitation of vulnerabilities, including those due to a person's economic situation.",[164,165,166,167,168],"Inventory entry for the coach and its personalisation models with an accountable owner","Documented guidance and advice boundary signed off by compliance","Fairness monitoring of insights and referrals across customer segments","Accuracy monitoring of forecasts and categorisation","Consent management for proactive messages and use of data",[],{"howToBuild":171},"On Blits.ai the coach is an **AI agent** whose numbers come from **custom functions** that\ncall the bank's data and forecasting services, or from a **SQL knowledge base** the agent can\nquery, so the model explains figures rather than calculating them. Guidance content sits in a\n**knowledge base** with hybrid retrieval. Actions such as creating a savings goal run as\n**flows** or **agentic workflows** with a tool execution policy and **human in the loop**\nconfirmation above a threshold.\n\nThe coach lives in the **mobile app** through the REST or WebSocket API channel, with\nrich cards for balances, charts and insight panels and optional **voice**. **Guardrails**\nenforce the advice boundary and block sales language in coaching answers, **PII masking**\nprotects transaction data, **test suites** check the boundary cases on every change, and\n**analytics** track use and satisfaction. The platform is model agnostic, with EU and UAE\ndata residency.",[173,176,179],{"question":174,"answer":175},"Is a financial wellbeing assistant giving financial advice?","It should not be. Explaining a customer's own data, general guidance and the bank's own features is guidance; recommending a specific investment or debt product for a customer's circumstances can be regulated advice. Write the boundary down and test it.",{"question":177,"answer":178},"Which banks run AI financial coaches today?","Bank of America's Erica has delivered more than 1.7 billion proactive, personalized insights (from a predefined set of responses, without generative AI), and RBC's NOMI forecasts cash flow and helps clients put money aside with Find and Save. Starling added smart tools to its agentic assistant in August 2026 and says it will release new ones every week for the rest of 2026.",{"question":180,"answer":181},"How do you prove it helps customers?","Compare users with a control group on fees avoided, savings built and financial difficulty outcomes, not on clicks. Engagement alone can hide a coach that mostly sells.",[183,184,185,186,187],"proactive-outbound-engagement-agent","offers-and-rewards-agent","goal-based-financial-planning-assistant","collections-and-hardship-agent","account-and-card-servicing-agent","2026-09-27",[190],{"date":188,"note":191},"First published","financial-wellbeing-coach",[194,236,263,299,340,369],{"title":195,"useCases":196,"organization":197,"vendors":203,"summary":206,"stage":207,"year":208,"channels":209,"languages":210,"metrics":212,"outcomeDisclosed":221,"sources":222,"verification":230,"grade":233,"id":234,"organizationSlug":235},"Hyundai Card: AI agent system that produces the personalized 2025 annual card statement",[192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":202},"Hyundai Card",false,"KR","asia-pacific","payments",[204],{"name":198,"role":205},"in-house","Hyundai Card, a South Korean credit card issuer, has published an annual statement (연간명세서) in its app since 2021 that summarizes each member's yearly card spending. For the 2025 edition, released in January 2026, Hyundai Card applied an AI agent system it built itself, described as generative AI based on a large language model that runs a predesigned workflow. According to the company, the agent was used across the whole production run: analysing the payment data of 12.6 million members, generating a personalized message for each member and reviewing the results. The statement is a spending review with narrative, persona based insights and peer comparisons, not a legal account statement or tax document.","scaled",2026,[27],[211],"ko",[213],{"kpi":46,"value":214,"unit":215,"qualifier":216,"period":217,"claimant":218,"quote":219,"sourceUrl":220},12600000,"count","exact","members whose payment data the AI agent analysed for the 2025 annual statement, with a personalized message generated and reviewed per member","organization","이번 연간명세서 제작 과정에서는 1260만 회원의 결제 데이터 분석, 회원별 개인화 메시지 생성, 결과 검수까지 전 과정에 AI 에이전트가 사용됐다.","https://newsroom.hyundaicard.com/front/board/AI%EA%B0%80-%EB%93%A4%EB%A0%A4%EC%A3%BC%EB%8A%94-2025%EB%85%84-%EC%86%8C%EB%B9%84-%EC%9D%B4%EC%95%BC%EA%B8%B0-%EC%97%B0%EA%B0%84%EB%AA%85%EC%84%B8%EC%84%9C%EC%97%90%EC%84%9C-%EB%A7%8C%EB%82%98%EB%B3%B4%EC%84%B8%EC%9A%94",true,[223,226],{"url":220,"title":224,"publisher":198,"date":225},"AI가 들려주는 2025년 소비 이야기, 연간명세서에서 만나보세요 (Hyundai Card opens the 2025 annual statement)","2026-01-15",{"url":227,"title":228,"publisher":229,"date":225},"https://www.kbanker.co.kr/news/articleView.html?idxno=223593","현대카드, AI 에이전트 적용 '연간명세서 2025' 출시","대한금융신문 (Korea Financial Newspaper)",{"level":231,"checkedAt":232},"source-verified","2026-09-28","B","hyundai-card-ai-annual-statement",null,{"title":237,"useCases":238,"organization":239,"vendors":242,"summary":246,"stage":247,"year":208,"channels":248,"languages":249,"metrics":251,"outcomeDisclosed":199,"sources":252,"verification":261,"grade":233,"id":262,"organizationSlug":235},"Starling Bank: Starling Assistant and its smart tools for money management",[192],{"name":240,"anonymized":199,"country":241,"region":155,"industry":17},"Starling Bank","GB",[243],{"name":244,"role":245},"Google","model-provider","Starling Assistant is an agentic AI assistant in the Starling app that responds to text and voice, analyses spending patterns, creates savings Spaces and sets up transfers on the customer's behalf. In August 2026 Starling added \"smart tools\" to it and said new ones would follow every week for the rest of 2026 and at least monthly after that. The launch set includes a tax saver that sweeps a share of the transactions a small business picks into a Space, a Making Tax Digital guide, a spending quiz and a student budget planner. A rainy day saver, which works out with the customer how much they can realistically save and sets up transfers into a savings Space, was announced as a follow up tool. The assistant is built on Google's Gemini models. No outcome figures are disclosed.","production",[27],[250],"en",[],[253,257],{"url":254,"title":255,"publisher":240,"date":256},"https://www.starlingbank.com/news/starling-to-release-weekly-smart-tools","Starling to release weekly 'smart tools' that supercharge money management for millions of Brits","2026-08-20",{"url":258,"title":259,"publisher":260},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real-world gen AI use cases from the world's leading organizations","Google Cloud",{"level":231,"checkedAt":188},"starling-assistant-agentic-financial-assistant",{"title":264,"useCases":265,"organization":268,"vendors":272,"summary":274,"stage":207,"year":38,"channels":275,"languages":276,"metrics":277,"outcomeDisclosed":221,"sources":289,"verification":296,"grade":233,"id":297,"organizationSlug":298},"Bank of America: Erica, a virtual financial assistant with proactive insights",[192,184,266,183,267],"first-line-contact-centre-agent","branch-and-appointment-booking-agent",{"name":269,"anonymized":199,"country":270,"region":271,"industry":17},"Bank of America","US","north-america",[273],{"name":269,"role":205},"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.",[27],[250],[278,284],{"kpi":45,"value":279,"unit":215,"qualifier":280,"period":281,"claimant":218,"quote":282,"sourceUrl":283},50000000,"approximately","since launch in 2018, as of August 2025","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":46,"value":285,"unit":215,"qualifier":286,"period":287,"claimant":218,"quote":288,"sourceUrl":283},3000000000,"at-least","client interactions since launch in 2018, as of August 2025","surpassing 3 billion client interactions",[290,293],{"url":283,"title":291,"publisher":269,"date":292},"A Decade of AI Innovation: BofA's Virtual Assistant Erica Surpasses 3 Billion Client Interactions","2025-08-20",{"url":294,"title":295,"publisher":269},"https://info.bankofamerica.com/en/digital-banking/erica","Erica: Virtual Financial Assistant",{"level":231,"checkedAt":188},"bank-of-america-erica-virtual-assistant","bank-of-america",{"title":300,"useCases":301,"organization":302,"vendors":305,"summary":308,"stage":207,"year":309,"channels":310,"languages":311,"metrics":312,"outcomeDisclosed":221,"sources":329,"verification":338,"grade":233,"id":339,"organizationSlug":235},"RBC: NOMI personal insights, cash flow forecasts and saving",[192],{"name":303,"anonymized":199,"country":304,"region":271,"industry":17},"Royal Bank of Canada","CA",[306],{"name":307,"role":205},"Borealis AI","RBC's NOMI is a set of AI features in the RBC Mobile app and RBC Online Banking that give clients personalized insights about their money. NOMI Forecast, built with the bank's research centre Borealis AI, uses deep learning to show a seven day view of upcoming preauthorized payments and cash flow; NOMI Find and Save helps clients put money aside; NOMI Budgets tracks spending. RBC says clients using Find and Save have put aside more than CAD 3.6 billion.",2023,[27],[250],[313,318,322],{"kpi":45,"value":314,"unit":215,"qualifier":286,"period":315,"claimant":218,"quote":316,"sourceUrl":317},900000,"NOMI Forecast, September 2021 to April 2023","Since its launch in September 2021, more than 900,000 clients have used the feature.","https://www.newswire.ca/news-releases/rbc-wins-best-use-of-ai-for-customer-experience-for-nomi-forecast-882322906.html",{"kpi":46,"value":319,"unit":215,"qualifier":286,"period":320,"claimant":218,"quote":321,"sourceUrl":317},10000000,"NOMI Forecast, 2021 to April 2023","The addition of NOMI Forecast has led to more than 10 million client interactions since 2021.",{"kpi":323,"value":324,"unit":325,"currency":326,"qualifier":286,"period":327,"claimant":218,"quote":328,"sourceUrl":317},"customer-savings",3600000000,"currency","CAD","NOMI Find and Save, cumulative as of April 2023","Clients using NOMI Find & Save have put aside more than $3.6 billion in savings.",[330,334],{"url":317,"title":331,"publisher":332,"date":333},"RBC Wins Best Use of AI for Customer Experience for NOMI Forecast","RBC Royal Bank via Cision","2023-04-28",{"url":335,"title":336,"publisher":337},"https://www.rbcroyalbank.com/mobile/feature/nomi/index.html","NOMI","RBC Royal Bank",{"level":231,"checkedAt":188},"rbc-nomi-personal-insights",{"title":341,"useCases":342,"organization":345,"vendors":348,"summary":352,"stage":207,"year":353,"channels":354,"languages":356,"metrics":357,"outcomeDisclosed":199,"sources":358,"verification":366,"grade":233,"id":367,"organizationSlug":368},"Commonwealth Bank: Customer Engagement Engine for next best conversations",[184,192,343,183,344],"loan-restructuring-recommendations","personalized-marketing-at-scale",{"name":346,"anonymized":199,"country":347,"region":201,"industry":17},"Commonwealth Bank of Australia","AU",[349],{"name":350,"role":351},"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,[27,355],"agent-desktop",[250],[],[359,363],{"url":360,"title":361,"publisher":346,"date":362},"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":364,"title":365,"publisher":350},"https://www.pega.com/customers/cba-marketing","Delivering next best conversations with Pega",{"level":231,"checkedAt":188},"commonwealth-bank-customer-engagement-engine","commonwealth-bank-of-australia",{"title":370,"useCases":371,"organization":372,"vendors":374,"summary":375,"stage":247,"year":38,"channels":376,"languages":377,"metrics":378,"outcomeDisclosed":199,"sources":379,"verification":385,"grade":386,"id":387,"organizationSlug":235},"Westpac: AI nudges in the mobile banking app, as assessed by Forrester",[192],{"name":373,"anonymized":199,"country":347,"region":201,"industry":17},"Westpac",[],"Forrester's 2025 review of Australian mobile banking apps, as reported by Mi3, ranked Westpac first for the third year running and credited AI features that nudge customers toward better financial decisions. The same coverage says that the big four Australian banks still fall short of what customers most want, including timely alerts and personalised guidance. This is an analyst assessment of the app, not a result published by the bank.",[27],[250],[],[380],{"url":381,"title":382,"publisher":383,"date":384},"https://www.mi-3.com.au/01-10-2025/big-four-deliver-ai-nudges-and-cautious-cleverness-their-banking-apps-forrester","Westpac blows app rivals away as Forrester rates Australia among world's best – but Big Four still missing key customer aspirations","Mi3","2025-10-01",{"level":231,"checkedAt":188},"D","westpac-app-ai-nudges",0,[390,398],{"kpi":46,"label":391,"unit":215,"aggregate":199,"higherIsBetter":221,"n":392,"nUpTo":388,"median":214,"min":319,"max":285,"byClaimant":393,"vendorOnly":199,"points":394},"Interactions handled",3,{"organization":392,"vendor":388,"regulator":388,"independent":388},[395,396,397],{"evidenceId":297,"organization":269,"value":285,"qualifier":286,"claimant":218,"grade":233,"pooled":221},{"evidenceId":234,"organization":198,"value":214,"qualifier":216,"claimant":218,"grade":233,"pooled":221},{"evidenceId":339,"organization":303,"value":319,"qualifier":286,"claimant":218,"grade":233,"pooled":221},{"kpi":45,"label":399,"unit":215,"aggregate":199,"higherIsBetter":221,"n":400,"nUpTo":388,"median":401,"min":314,"max":279,"byClaimant":402,"vendorOnly":199,"points":403},"Users served",2,25450000,{"organization":400,"vendor":388,"regulator":388,"independent":388},[404,405],{"evidenceId":297,"organization":269,"value":279,"qualifier":280,"claimant":218,"grade":233,"pooled":221},{"evidenceId":339,"organization":303,"value":314,"qualifier":286,"claimant":218,"grade":233,"pooled":221},{"low":407,"high":408},50000,500000,[410,422,434,449,475],{"slug":183,"title":411,"shortTitle":412,"definition":413,"status":9,"industries":414,"functions":415,"patterns":417,"audience":28,"autonomy":29,"adoptionStage":419,"segment":31,"evidenceCount":392,"publicEvidenceCount":392,"organizations":420,"bestGrade":233,"headline":235,"lastVerified":188,"indexable":221},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","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.",[17,202],[20,416,19],"sales",[22,418,25,23],"voice-agent","emerging",[269,421,346],"Capital One",{"slug":184,"title":423,"shortTitle":424,"definition":425,"status":9,"industries":426,"functions":427,"patterns":428,"audience":28,"autonomy":429,"adoptionStage":430,"segment":31,"evidenceCount":431,"publicEvidenceCount":392,"organizations":432,"bestGrade":233,"headline":235,"lastVerified":188,"indexable":221},"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.",[17,202],[20,416,19],[23,24,22],"autonomous","mainstream",8,[269,346,433],"DBS Bank",{"slug":185,"title":435,"shortTitle":436,"definition":437,"status":9,"industries":438,"functions":440,"patterns":442,"audience":444,"autonomy":445,"adoptionStage":419,"segment":31,"evidenceCount":400,"publicEvidenceCount":400,"organizations":446,"bestGrade":233,"headline":235,"lastVerified":188,"indexable":221},"AI assistant for goal based financial planning","Goal based planning","An AI assistant that turns a client's goals into projections and what if scenarios using a rules based planning engine, explains the trade offs in plain language and prepares the plan for an advisor to validate, with every assumption disclosed and reproducible.",[439,17],"wealth-and-asset-management",[416,19,441],"product-and-pricing",[22,443,25],"content-generation","employee-facing","copilot",[447,448],"CIMB Niaga","Vanguard",{"slug":186,"title":450,"shortTitle":451,"definition":452,"status":9,"industries":453,"functions":459,"patterns":461,"audience":28,"autonomy":29,"adoptionStage":30,"segment":463,"evidenceCount":392,"publicEvidenceCount":400,"organizations":464,"bestGrade":467,"headline":468,"lastVerified":188,"indexable":221},"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.",[454,17,202,455,456,457,458],"cross-industry","telecommunications","energy-and-utilities","automotive","professional-services",[460,19],"collections-and-recovery",[418,22,25,462],"classification-and-routing","lending",[465,466],"Day Knight & Associates","SameDay Auto Finance","C",{"kpi":469,"label":470,"unit":471,"n":400,"nUpTo":388,"kind":472,"value":473,"qualifier":216,"claimant":474,"organization":466,"vendorReported":221},"cost-reduction","Cost reduction","percent","reported",75,"vendor",{"slug":187,"title":476,"shortTitle":477,"definition":478,"status":9,"industries":479,"functions":480,"patterns":482,"audience":28,"autonomy":29,"adoptionStage":430,"segment":31,"evidenceCount":484,"publicEvidenceCount":400,"organizations":485,"bestGrade":233,"headline":486,"lastVerified":188,"indexable":221},"AI agent for account and card servicing","Account and card servicing","An AI agent that resolves routine account and card requests end to end, such as balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[17,202],[19,481],"operations",[22,418,25,483],"rag-knowledge-assistant",4,[346,433],{"kpi":487,"label":488,"unit":471,"n":400,"nUpTo":388,"kind":472,"value":489,"qualifier":280,"claimant":218,"organization":433,"vendorReported":199},"containment-rate","Containment rate",90,{"indexable":221,"reasons":491},[],[493,498,503,510,517,523,530,535,541,548,555,561,568,575,581,586,593,599,605,611,617,623,629,634,639,646,652,657,663,671,677,683,689,694],{"id":146,"label":494,"issuer":160,"region":155,"url":495,"description":496,"useCases":497,"indexable":221},"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":147,"label":499,"issuer":160,"region":155,"url":500,"description":501,"useCases":502,"indexable":221},"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":150,"label":504,"issuer":505,"region":506,"url":507,"description":508,"useCases":509,"indexable":221},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":511,"label":512,"issuer":513,"region":271,"url":514,"description":515,"useCases":516,"indexable":221},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":518,"label":519,"issuer":160,"region":155,"url":520,"description":521,"useCases":522,"indexable":221},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":524,"label":525,"issuer":526,"region":155,"url":527,"description":528,"useCases":529,"indexable":221},"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":148,"label":531,"issuer":154,"region":155,"url":532,"description":533,"useCases":534,"indexable":221},"FCA Consumer Duty","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":149,"label":536,"issuer":537,"region":201,"url":538,"description":539,"useCases":540,"indexable":221},"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":542,"label":543,"issuer":544,"region":201,"url":545,"description":546,"useCases":547,"indexable":221},"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":549,"label":550,"issuer":551,"region":506,"url":552,"description":553,"useCases":554,"indexable":221},"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":556,"label":557,"issuer":558,"region":271,"url":559,"description":560,"useCases":554,"indexable":221},"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":562,"label":563,"issuer":564,"region":155,"url":565,"description":566,"useCases":567,"indexable":221},"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":569,"label":570,"issuer":571,"region":506,"url":572,"description":573,"useCases":574,"indexable":221},"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":576,"label":577,"issuer":160,"region":155,"url":578,"description":579,"useCases":580,"indexable":221},"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":582,"label":583,"issuer":160,"region":155,"url":584,"description":585,"useCases":580,"indexable":221},"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":587,"label":588,"issuer":589,"region":271,"url":590,"description":591,"useCases":592,"indexable":221},"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":594,"label":595,"issuer":160,"region":155,"url":596,"description":597,"useCases":598,"indexable":221},"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":600,"label":601,"issuer":602,"region":271,"url":603,"description":604,"useCases":598,"indexable":221},"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":606,"label":607,"issuer":608,"region":506,"url":609,"description":610,"useCases":598,"indexable":221},"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":612,"label":613,"issuer":160,"region":155,"url":614,"description":615,"useCases":616,"indexable":221},"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":618,"label":619,"issuer":620,"region":271,"url":621,"description":622,"useCases":616,"indexable":221},"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":624,"label":625,"issuer":537,"region":201,"url":626,"description":627,"useCases":628,"indexable":221},"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":630,"label":631,"issuer":160,"region":155,"url":632,"description":633,"useCases":628,"indexable":221},"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":635,"label":636,"issuer":160,"region":155,"url":637,"description":638,"useCases":628,"indexable":221},"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":640,"label":641,"issuer":642,"region":155,"url":643,"description":644,"useCases":645,"indexable":221},"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":647,"label":648,"issuer":649,"region":271,"url":650,"description":651,"useCases":431,"indexable":221},"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":653,"label":654,"issuer":160,"region":155,"url":655,"description":656,"useCases":431,"indexable":221},"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":658,"label":659,"issuer":160,"region":155,"url":660,"description":661,"useCases":662,"indexable":221},"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":664,"label":665,"issuer":666,"region":667,"url":668,"description":669,"useCases":670,"indexable":221},"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.",5,{"id":672,"label":673,"issuer":674,"region":155,"url":675,"description":676,"useCases":484,"indexable":221},"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":678,"label":679,"issuer":680,"region":155,"url":681,"description":682,"useCases":484,"indexable":221},"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":684,"label":685,"issuer":686,"region":201,"url":687,"description":688,"useCases":392,"indexable":221},"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":690,"label":691,"issuer":160,"region":155,"url":692,"description":693,"useCases":392,"indexable":221},"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":695,"label":696,"issuer":697,"region":271,"url":698,"description":699,"useCases":392,"indexable":221},"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.",1790598298982]