[{"data":1,"prerenderedAt":903},["ShallowReactive",2],{"uc-financial-services-digital-human-guide":3,"uc-regulations":686},{"useCase":4,"evidence":226,"blitsAiDeployments":472,"benchmarks":473,"indicative":488,"related":491,"indexability":684,"includeUnpublished":232},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":20,"functions":24,"patterns":27,"channels":32,"audience":36,"autonomy":37,"adoptionStage":38,"segment":39,"problem":40,"problemStats":41,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":55,"macroEstimates":95,"feasibility":96,"implementation":110,"risk":161,"blitsAi":197,"faq":199,"related":212,"datePublished":221,"dateModified":221,"lastVerified":221,"changelog":222,"slug":225},"AI digital human guide for banks and insurers","Digital human guide","Digital human for banks and insurers","A digital human is an AI avatar that answers bank and insurance questions by voice. ANZ reported more than 12,000 conversations with its avatar Jamie in 100 days.","published","A digital human guide is an AI agent with a lifelike face and voice that answers a bank's or insurer's product and service questions and passes complex cases to staff. It appears on a website, in an app, on a kiosk or on a screen in an advisor meeting, speaks with the customer in real time and answers from the institution's approved content.",[12,13,14,15,16,17,18,19],"banking avatar","insurance avatar","AI virtual employee for banks","digital human customer assistant","face to face AI assistant for financial services","AI bank teller","AI banker kiosk","branch digital human",[21,22,23],"banking","insurance","wealth-and-asset-management",[25,26],"customer-service","sales",[28,29,30,31],"digital-human","conversational-agent","rag-knowledge-assistant","voice-agent",[28,33,34,35],"web-chat","mobile-app","kiosk","customer-facing","supervised-agent","emerging","front-office","A bank or insurer website holds many pages on accounts, cards, loans and cover, and a visitor\noften wants a short answer to one question: how do I open an account, what does excess mean,\nwhat do I need for a mortgage. ANZ's most common question to Jamie in her first 100 days was how\nto open a bank account, asked nearly 1,200 times. A text chatbot can answer such questions, but\nsome customers avoid digital channels altogether. ANZ New Zealand said it started its Jamie trial to see whether a digital\nhuman would appeal to customers who are less comfortable with its other digital channels, and\nNatWest said early testing of its Cora digital human suggested that customers who had avoided\ndigital services might be more inclined to use one.\n\nANZ said that talking to the bank instead of pressing screens might be easier for certain groups\nof customers, and around 30% of its New Zealand customers do not use digital channels at all.\nInstitutions also treat the avatar as one more channel: Suncorp described its Ava avatar as\nanother way for customers to engage, next to chatbot, web chat and phone, and UBS tested whether\nclients accept avatars next to a human advisor in a wealth management meeting.\n\nThe risks are specific too. A lifelike face may make an answer more persuasive than the same\ntext on a screen, which raises the cost of a wrong answer, and a face that looks like a real\nemployee or executive raises its own disclosure questions. Most published deployments are\npilots with no disclosed outcome, which is the reason to start narrow and measure.",[],"1. **Greet and identify the topic.** The avatar appears on a website, in an app, on a kiosk or on\n   a screen in a meeting, greets the visitor and takes a question by voice or text. In the\n   published pilots it listens through the device microphone and, in some, watches through the\n   camera to react to the person. Reading a customer's emotions from the camera is high risk\n   under the EU AI Act, so treat the camera as an optional feature that needs its own assessment.\n2. **Answer from approved content.** The avatar answers from the institution's own knowledge base\n   of products, fees, policy wording and procedures, in the customer's language. ANZ limited\n   Jamie to 30 frequently searched help topics and no customer data; Ajman Bank's avatar answers\n   from the bank's knowledge base in Arabic and English.\n3. **Explain and guide.** It explains terms in plain language, shows where to find a form or\n   feature and walks through a journey such as a mortgage inquiry or a motor insurance quote.\n4. **Act only within limits.** Avatars differ in what they do. ANZ's Jamie answered general\n   questions only. UBS tested one in 2018 that could order a credit card for a client in a\n   meeting. Shinhan Bank's AI Banker at its branch digital desks opens deposit accounts, takes\n   loan applications and issues debit cards and certificates, with a video handover to staff.\n   Anything that needs an account lookup or a money movement needs\n   authentication and an allow list.\n5. **Hand over.** Complex, sensitive or unanswered questions go to a person with the\n   conversation so far, such as a client advisor, a contact center agent or an appointment.",[44,45,46,47],"customer-experience","inclusion-and-access","cost-to-serve","revenue-growth",[49,50,51,52,53,54],"interactions-handled","accuracy","containment-rate","customer-satisfaction","conversion-rate-uplift","contact-deflection",{"referenceOrg":56,"inputs":57,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A retail bank with 2 million help site and app sessions a year",[58,63,70,77,83],{"key":59,"label":60,"low":61,"high":61,"unit":59,"note":62},"sessions","Help site and app sessions per year",2000000,"The reference bank.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"engagement","Share of sessions that start a conversation with the avatar",0.01,0.03,"fraction of sessions","Editorial assumption, replace with your own. For scale, ANZ New Zealand reported more than 12,000 conversations in the first 100 days of Jamie.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"answered","Share of conversations the avatar answers without staff",0.4,0.6,"fraction of conversations","Editorial assumption, in line with ANZ New Zealand, which said Jamie could answer about 60% of customer queries in her first 100 days. ANZ counted all queries, including off topic ones, so that figure is not a resolution share.",{"key":78,"label":79,"low":80,"high":73,"unit":81,"note":82},"replaced","Share of answered conversations that would otherwise have reached a staff handled contact",0.2,"fraction of answered conversations","Editorial assumption, replace with your own. Many visitors would have left or found the answer on the page.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":89},"costPerContact","Cost of a staff handled contact",3,6,"USD per contact","Editorial assumption for a blended chat and phone contact. Replace with your own fully loaded cost.","sessions * engagement * answered * replaced * costPerContact","USD","per year","Staff handled contact cost avoided","Gross avoided contact cost only. It leaves out avatar production and hosting, content upkeep, the revenue effect of more engaged visitors and any lift in leads or quotes, which published pilots have not reported.",[],{"complexity":97,"complexityNote":98,"dataPrerequisites":99,"integrations":104},"medium","A general question avatar needs approved content in a knowledge base, a speech and avatar rendering service, a web or app embed, and a handover to chat or a branch. Anything that touches an account adds the bank's authentication and APIs, and a kiosk or in meeting screen adds hardware, microphones in a noisy room and accessibility checks.",[100,101,102,103],"Approved product, fee and policy content with an owner and a review date per document","The top help topics from site search and contact center reasons, to scope the first topics","A style guide for tone, name, appearance and what the avatar never says","Consent and recording rules for microphone and camera use",[105,106,107,108,109],"Website or app embed with microphone and optional camera permission","Knowledge base with retrieval over approved content","Contact center, chat or appointment booking for handover","Authentication and core system APIs, only for later transactional phases","Analytics for conversations, unanswered questions and satisfaction",{"steps":111,"guardrails":130,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":145},[112,115,118,121,124,127],{"title":113,"detail":114},"Choose a narrow first scope","Start with general questions only, as ANZ did with 30 help topics and no customer data. Pick the topics from site search and contact reasons and leave account actions for a later phase.",{"title":116,"detail":117},"Design the persona and the disclosure","Decide the name, appearance, languages and voice, and say on screen that the customer is talking to an AI. If the likeness is a real employee or executive, get that person's written consent and legal review before launch.",{"title":119,"detail":120},"Ground every answer","Load approved content into a knowledge base, make the avatar refuse when the content does not cover the question, and review the questions it could not answer each week to fill gaps.",{"title":122,"detail":123},"Design the handover","Define which topics and signals go to a person (complaints, vulnerability, advice that needs a licensed advisor, repeated misunderstanding) and pass the transcript to the advisor, contact center or booking.",{"title":125,"detail":126},"Test, then release to a share of visitors","Run a set of real customer questions in every language before launch, then show the avatar to a random share of visitors, as Suncorp did with desktop users, and compare conversion, satisfaction and contacts with a control group.",{"title":128,"detail":129},"Widen the channels and the actions","Add the app, kiosks and advisor meetings once the web pilot holds, and add transactions one at a time, each with its own authentication level and limits.",[131,132,133,134,135,136,137],"On screen disclosure that the customer is talking to an AI, at the start of every session","Answers only from approved content, with a refusal and a handover when the content does not cover the question","No financial or insurance advice beyond what the institution's licensed advisors have approved","No emotion recognition or other biometric inference from the camera feed unless it has passed a separate legal assessment","Microphone and camera input processed only for the conversation, with a clear consent prompt and short retention","Written consent and legal review for any avatar that uses the likeness or voice of a real person","PII masking before text reaches a model, and an allow list with step up authentication for any account action","Staff own advice, complaints, vulnerable customers and every action that moves money or changes a policy. A content owner reviews the avatar's unanswered and low rated conversations each week, and compliance approves every new topic and every new action before it goes live.",[140,141,142,143,144],"Conversations per week and the share that end with a staff handover","Share of questions answered from approved content, from a weekly sample check","Customer satisfaction after the conversation, and complaints that mention the avatar","Conversion, quote or appointment rate against a control group without the avatar","Cost per conversation including rendering, hosting and speech services",[146,149,152,155,158],{"title":147,"detail":148},"A confident face on a wrong answer","A lifelike avatar may make an incorrect answer more persuasive. Ground answers in approved content, refuse outside it and sample conversations every week.",{"title":150,"detail":151},"Novelty without outcomes","Interest in the avatar is high at launch and the pilot never measures conversion or contacts avoided. Set the control group and the KPIs before the launch.",{"title":153,"detail":154},"Uncanny or unwanted face","Some customers dislike talking to a lifelike face or have no microphone. Keep a text option and a human route, and test the persona with real customers, as Suncorp did with the name Ava.",{"title":156,"detail":157},"Vendor and platform risk","Several early avatars ran on one vendor's platform, and Soul Machines was reported in April 2026 to be in receivership. Keep the content, prompts and persona assets under your own control so you can move to another platform.",{"title":159,"detail":160},"Likeness and consent","An avatar modeled on a real employee or executive needs consent, a plan for when that person leaves, and a clear label that it is AI.",{"euAiAct":162,"regulations":165,"guidance":173,"controls":186,"incidents":192},{"tier":163,"basis":164},"limited","Article 50(1): people must be told they are interacting with an AI system unless that is obvious from the context. Camera based emotion recognition of customers, which some of the avatars described here do, is high risk under Annex III point 1(c) and needs the Article 50(3) notice, so leave it out of the design or put it through a separate assessment. An avatar that mimics a real employee or executive may also be deepfake content under Article 50(4), which requires disclosure that the content is artificially generated. General product and service answers are not an Annex III use; the tier would rise if the avatar assessed creditworthiness or priced life or health insurance.",[166,167,168,169,170,171,172],"eu-ai-act","gdpr","eu-accessibility-act","mifid-ii","eu-idd","uk-consumer-duty","cbuae-ai-guidance",[174,180],{"title":175,"issuer":176,"region":177,"url":178,"note":179},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","People must be informed that they are interacting with an AI system, and deepfake content must be disclosed as artificially generated.",{"title":181,"issuer":182,"region":183,"url":184,"note":185},"Chatbots in consumer finance","Consumer Financial Protection Bureau","north-america","https:\u002F\u002Fwww.consumerfinance.gov\u002Fdata-research\u002Fresearch-reports\u002Fchatbots-in-consumer-finance\u002F","Warns that chatbots which cannot resolve a problem and block access to a human can harm customers and lead to violations of consumer financial law; the same applies to an avatar front end.",[187,188,189,190,191],"AI disclosure at the start of every session and a visible label on the avatar","Inventory entry for the avatar with an accountable owner and a documented list of topics and actions","Consent record for any real person whose likeness or voice is used","Audit trail of conversations with a defined retention period and no biometric profiling of customers","Change control and regression tests for every new topic, language or action",[193],{"title":194,"url":195,"note":196},"Soul Machines placed in receivership","https:\u002F\u002Fwww.nbr.co.nz\u002Ftech\u002Fsoul-machines-owes-19-5m-in-receivership","NBR reported on 13 April 2026 that Soul Machines owes creditors more than $19.5 million (NZ$) in receivership. Soul Machines was the platform for ANZ's Jamie, NatWest's Cora and Suncorp's Ava, according to the deployment records. The report does not say what happened to those deployments.",{"howToBuild":198},"On Blits.ai the avatar is a **digital human** channel: a photorealistic 3D avatar streamed to the\nbrowser, with lip synced speech and an **emotion engine** that drives expression and voice. It\nis backed by an **AI agent** with a **knowledge base** of the institution's approved product,\nfee and policy content, retrieved with hybrid search, and by **voice** input with text as an\nalternative. Each branded persona runs as its own instance, and the same agent can also run in\n**web chat** and through the REST or WebSocket API in a mobile app.\n\n**Guardrails** check input and output against the institution's policies, **PII masking** runs\nat the gateway, and **human handover** passes the conversation to a live agent platform, and a\n**flow** or **custom function** can book an appointment. Regulated steps follow a **flow** with deterministic checks, and any account\naction is a **custom function** behind the institution's own authentication. **Test suites** of\nreal customer questions run after every content change, **analytics** show conversations and\nunanswered questions, and **model agnostic routing** lets the institution choose or switch the\nmodel. EU and UAE data residency is available for the hosting.",[200,203,206,209],{"question":201,"answer":202},"What is a digital human in banking and insurance?","It is an AI agent shown as a realistic person on screen, with a voice, facial expressions and the ability to answer questions in a conversation. ANZ New Zealand, Suncorp's AAMI and Ajman Bank have each run one to answer customer questions on a website or in an app.",{"question":204,"answer":205},"Does a digital human perform better than a text chatbot?","The published evidence does not show it yet. ANZ reported more than 12,000 conversations in the first 100 days of its pilot, and Ajman Bank's vendor reports more than 13,000 interactions in four months, but none of the pilots compares the avatar with a chatbot on resolution, satisfaction or conversion. Measure that against a control group before you scale.",{"question":207,"answer":208},"Does a digital human have to say it is AI?","In the EU, yes: Article 50 of the AI Act requires people to be told they are talking to an AI system unless that is obvious, and an avatar that imitates a real person may also need a deepfake disclosure. Show the label on screen from the first second.",{"question":210,"answer":211},"What can a digital human not do yet?","It depends on the avatar. ANZ's Jamie answered general questions only and did not ask for any customer details. UBS tested an avatar in 2018 that could order a credit card, and Shinhan Bank's AI Banker at branch digital desks handles account opening and loan applications, with a video handover to staff. Advice, credit decisions and payments belong with licensed staff or with systems that authenticate the customer.",[213,214,215,216,217,218,219,220],"first-line-contact-centre-agent","home-loan-assistant-and-prequalification","conversational-insurance-quote-and-buy","digital-onboarding-assistant","retail-store-and-kiosk-assistant","account-and-card-servicing-agent","atm-and-self-service-device-assistance","branch-and-appointment-booking-agent","2026-10-14",[223],{"date":221,"note":224},"First published","financial-services-digital-human-guide",[227,270,294,338,360,395,420,446],{"title":228,"useCases":229,"organization":230,"vendors":236,"summary":240,"stage":241,"year":242,"channels":243,"languages":244,"metrics":246,"outcomeDisclosed":255,"sources":256,"verification":265,"grade":267,"id":268,"organizationSlug":269},"ANZ New Zealand: Jamie, a digital human on the bank's help site",[225],{"name":231,"anonymized":232,"country":233,"region":234,"industry":21,"wikidata":235},"ANZ New Zealand",false,"NZ","asia-pacific","Q295862",[237],{"name":238,"role":239},"Soul Machines","platform","ANZ New Zealand put a digital assistant called Jamie, built with Soul Machines, on its help site in July 2018. Jamie has a human face and voice and was set up to answer general banking questions on the 30 most frequently searched topics, without asking for any customer details. ANZ ran it as a trial to see whether it would appeal to customers who are less comfortable with its other digital channels. After the first 100 days ANZ reported more than 12,000 conversations, said Jamie could answer about 60% of the queries put to her, and extended the pilot.","pilot",2018,[28,33],[245],"en",[247],{"kpi":49,"value":248,"unit":249,"qualifier":250,"period":251,"claimant":252,"quote":253,"sourceUrl":254},12000,"count","at-least","first 100 days of the pilot","organization","Jamie has already had more than 12,000 conversations with existing and potential customers.","https:\u002F\u002Fwww.exclusives.anz.com.au\u002Fcontent\u002Fdam\u002Fanzcomau\u002Fnews\u002FNew-Zealand\u002F2019\u002FJanuary\u002FANZ%20News%20Release%20-%20From-term-deposits-to-turtles.pdf",true,[257,260],{"url":254,"title":258,"publisher":231,"date":259},"ANZ news release: From term deposits to turtles, Jamie's first 100 days of your questions","2018-11-19",{"url":261,"title":262,"publisher":263,"date":264},"https:\u002F\u002Fexclusives.anz.com.au\u002Fbluenotes\u002F2018\u002F07\u002Fher\u002F","Her","ANZ bluenotes","2018-07-16",{"level":266,"checkedAt":221},"source-verified","B","anz-new-zealand-jamie-digital-assistant",null,{"title":271,"useCases":272,"organization":273,"vendors":277,"summary":282,"stage":241,"year":242,"channels":283,"languages":284,"metrics":285,"outcomeDisclosed":232,"sources":286,"verification":291,"grade":267,"id":292,"organizationSlug":293},"UBS: Companion avatars Fin and Daniel Kalt in wealth management client meetings",[225],{"name":274,"anonymized":232,"country":275,"region":177,"industry":23,"wikidata":276},"UBS","CH","Q193199",[278,280],{"name":279,"role":239},"FaceMe",{"name":281,"role":239},"IBM","In 2018 UBS ran a three month experiment called UBS Companion at a branch in Zurich. Client advisors in Wealth Management Switzerland brought two avatars into meetings with a set number of clients, shown on an LCD screen: Fin, a digital assistant that can handle simple banking transactions such as ordering a credit card, and Daniel Kalt, a digital version of the bank's Regional Chief Investment Officer Switzerland that supports market topics and provides CIO House View content on demand. UBS built them with IBM and FaceMe to test how clients accept digital assistants and how human like they should be. The page reports no results.",[28],[],[],[287],{"url":288,"title":289,"publisher":290},"https:\u002F\u002Fwww.ubs.com\u002Fmicrosites\u002Fnews-for-banks\u002Fen\u002Fglobal-trends\u002F2018\u002Fcan-one-be-available-for-clients-24-7.html","Can banks be available for clients 24\u002F7?","UBS News for banks",{"level":266,"checkedAt":221},"ubs-companion-fin-and-daniel-kalt-avatars","ubs",{"title":295,"useCases":296,"organization":297,"vendors":302,"summary":305,"stage":306,"year":307,"channels":308,"languages":309,"metrics":311,"outcomeDisclosed":255,"sources":327,"verification":335,"grade":336,"id":337,"organizationSlug":269},"Ajman Bank: bilingual AI avatar in the app, in branch kiosks and on HoloBox",[225],{"name":298,"anonymized":232,"country":299,"region":300,"industry":21,"wikidata":301},"Ajman Bank","AE","middle-east","Q140203277",[303],{"name":304,"role":239},"SESTEK","Ajman Bank, an Islamic bank in the UAE, unveiled a generative AI avatar at GITEX Global in October 2025 and runs it with SESTEK across its mobile app, in branch kiosks and HoloBox holographic displays. Customers speak to the avatar in Arabic or English, and it answers from the bank's own knowledge base using speech recognition, a localized Emirati text to speech voice and generative question answering. SESTEK reports more than 13,000 customer interactions in the first four months, with content generation accuracy of 94.45% in Arabic and 96.7% in English.","production",2026,[28,34,35],[310,245],"ar",[312,318,324],{"kpi":49,"value":313,"unit":249,"qualifier":250,"period":314,"claimant":315,"quote":316,"sourceUrl":317},13000,"first four months","vendor","Within the first four months, the avatar supported over 13,000 real customer interactions.","https:\u002F\u002Fwww.sestek.com\u002Fblog\u002Fai-avatars-in-banking-the-ajman-bank-story",{"kpi":50,"value":319,"unit":320,"qualifier":321,"period":322,"claimant":315,"quote":323,"sourceUrl":317},94.45,"percent","exact","first four months, Arabic content generation","It achieved a 94.45% content generation accuracy in Arabic and 96.7% in English, alongside a 4.3 customer satisfaction score.",{"kpi":50,"value":325,"unit":320,"qualifier":321,"period":326,"claimant":315,"quote":323,"sourceUrl":317},96.7,"first four months, English content generation",[328,330],{"url":317,"title":329,"publisher":304},"AI Avatars in Banking: The Ajman Bank Story",{"url":331,"title":332,"publisher":333,"date":334},"https:\u002F\u002Fmenastartupdigest.com\u002F?p=18046","Ajman Bank Redefines Islamic Digital Banking With AI Avatar Launch","MENA Startup Digest","2025-10-14",{"level":266,"checkedAt":221},"C","ajman-bank-sestek-ai-avatar",{"title":339,"useCases":340,"organization":341,"vendors":345,"summary":348,"stage":241,"year":349,"channels":350,"languages":351,"metrics":352,"outcomeDisclosed":232,"sources":353,"verification":358,"grade":336,"id":359,"organizationSlug":269},"Kuwait Finance House: Fahad conversational AI avatar at branch account service kiosks",[225],{"name":342,"anonymized":232,"country":343,"region":300,"industry":21,"wikidata":344},"Kuwait Finance House","KW","Q2336459",[346],{"name":347,"role":239},"NCR Atleos","Kuwait Finance House (KFH) and NCR Atleos announced in November 2025 a conversational AI avatar named Fahad, shown as a video avatar on the account service kiosks in KFH branches. The avatar answers frequently asked questions about KFH services and gives branch specific information, and from the second phase of the project it is meant to guide customers through transactions on the kiosk. It can hand the conversation to a live agent. KFH also plans AI speech analytics and automated quality management over these conversations. NCR Atleos describes the work as a field test of a product that is not yet commercially available. No usage or results have been published.",2025,[35,28],[],[],[354],{"url":355,"title":356,"publisher":347,"date":357},"https:\u002F\u002Finvestor.ncratleos.com\u002Fnews-events\u002Fpress-releases\u002Fdetail\u002F160\u002Fkuwait-finance-house-collaborates-with-ncr-atleos-to-revolutionize-in-branch-banking-experience-using-conversational-ai-avatar","Kuwait Finance House Collaborates with NCR Atleos to Revolutionize In-Branch Banking Experience Using Conversational AI Avatar","2025-11-04",{"level":266,"checkedAt":221},"kuwait-finance-house-fahad-ai-avatar-kiosks",{"title":361,"useCases":362,"organization":363,"vendors":367,"summary":370,"stage":371,"year":372,"channels":373,"languages":375,"metrics":377,"outcomeDisclosed":232,"sources":378,"verification":393,"grade":336,"id":394,"organizationSlug":269},"Shinhan Bank: AI Banker on digital desks and smart kiosks in branches",[225],{"name":364,"anonymized":232,"country":365,"region":234,"industry":21,"wikidata":366},"Shinhan Bank","KR","Q487230",[368],{"name":369,"role":239},"DeepBrain AI","Shinhan Bank, in South Korea, put an AI Banker, a conversational digital human, on the video consultation \"digital desks\" of its unmanned Digital Lounge branches from September 2021. At the desks it handles dozens of consultation tasks, such as opening deposit and savings accounts and taking credit loan applications, and hands the customer to a remote staff member over video when it cannot finish a task. In June 2024 Shinhan raised the task list from 56 to 64, added debit card, security card and certificate issuing, and extended the AI Banker to smart kiosks, where it guides customers who are not used to operating them. A July 2024 version uses five real branch employees as models, generative AI for the voice, and covers guidance on about 70 tasks at the kiosks. Shinhan said it had about 150 digital desks in branches nationwide in June 2024. No results such as share of tasks completed or customer satisfaction have been published.","scaled",2024,[35,28,374],"voice",[376],"ko",[],[379,384,389],{"url":380,"title":381,"publisher":382,"date":383},"https:\u002F\u002Fwww.smarttoday.co.kr\u002Fko-kr\u002Farticles\u002F51739","신한은행, AI 은행원 업무 늘렸다","Smart Today","2024-06-04",{"url":385,"title":386,"publisher":387,"date":388},"https:\u002F\u002Fwww.unicornfactory.co.kr\u002Farticle\u002F2024071210433527001","딥브레인AI, 신한은행 'AI 은행원' 고도화…\"실제 직원 모습으로 제작\"","Unicorn Factory (Money Today)","2024-07-12",{"url":390,"title":391,"publisher":392},"https:\u002F\u002Fwww.bizhankook.com\u002Fbk\u002Farticle\u002F23465","[현장] 'AI 은행원' 통해 적금 가입을 시도하자 직원이 나타났다","Bizhankook",{"level":266,"checkedAt":221},"shinhan-bank-ai-banker-digital-desk-and-smart-kiosk",{"title":396,"useCases":397,"organization":398,"vendors":401,"summary":403,"stage":241,"year":404,"channels":405,"languages":406,"metrics":407,"outcomeDisclosed":232,"sources":408,"verification":418,"grade":336,"id":419,"organizationSlug":269},"KB Kookmin Bank: AI Banker kiosk in branches",[225],{"name":399,"anonymized":232,"country":365,"region":234,"industry":21,"wikidata":400},"KB Kookmin Bank","Q496606",[402],{"name":369,"role":239},"KB Kookmin Bank, in South Korea, installed AI Banker kiosks in selected branches from early 2022, including its digital test bed branch in Yeouido, Seoul. The AI Banker is a digital human built with DeepBrain AI from the voice and video of two branch employees. It greets customers by voice, recommends savings products, explains financial terms, tells customers how to use the nearby automated teller machines, including issuing a debit card and changing a PIN at the bank's intelligent automated machines, and points to nearby facilities. A camera detects when the customer walks away and ends the session. The reporter of a hands on test found that loans and account opening were not available at the kiosk and that detailed rates appeared on the screen instead of being spoken. No usage or satisfaction results have been published.",2022,[35,28,374],[376],[],[409,410,415],{"url":390,"title":391,"publisher":392},{"url":411,"title":412,"publisher":413,"date":414},"https:\u002F\u002Fwww.dailian.co.kr\u002Fnews\u002Fview\u002F1203872\u002F","[AI뱅커가 온다②] “안녕하세요 고객님” 은행원 대체하는 가상인간","Dailian","2023-02-21",{"url":416,"title":417,"publisher":369},"https:\u002F\u002Fwww.aistudios.com\u002Fko\u002Fcustomers\u002Fkb-koomin-bank","DeepBrain AI, Case study of KB Koomin Bank",{"level":266,"checkedAt":221},"kb-kookmin-bank-ai-banker-kiosk",{"title":421,"useCases":422,"organization":423,"vendors":427,"summary":429,"stage":241,"year":430,"channels":431,"languages":432,"metrics":433,"outcomeDisclosed":232,"sources":434,"verification":444,"grade":336,"id":445,"organizationSlug":269},"Suncorp: Ava, a digital employee for AAMI motor insurance customers",[225],{"name":424,"anonymized":232,"country":425,"region":234,"industry":22,"wikidata":426},"Suncorp Group","AU","Q2366028",[428],{"name":238,"role":239},"In June 2021 Suncorp launched Ava, a digital employee with a face, as a pilot for some motor insurance customers of its AAMI brand. Customers talk to Ava through their microphone and camera while they scope a motor policy on the AAMI site, and she was trained to answer questions on coverage, price, discounts, excess, optional extras, claims and payment options. A selection of desktop users was randomly placed in the new experience with the option to interact with her. Suncorp built Ava with Soul Machines in four months and had set no end date for the pilot. No results were disclosed.",2021,[28,33],[245],[],[435,440],{"url":436,"title":437,"publisher":438,"date":439},"https:\u002F\u002Finsurancenews.com.au\u002Fdaily\u002Fava-makes-her-debut-suncorp-unveils-digital-employee","Ava makes her debut: Suncorp unveils digital employee","insuranceNEWS.com.au","2021-06-01",{"url":441,"title":442,"publisher":443,"date":439},"https:\u002F\u002Fwww.indailyqld.com.au\u002Fnews\u002Farchive\u002F2021\u002F06\u002F01\u002Fyoure-out-of-luck-aami-meet-ava-the-new-voice-and-face-of-suncorp","You're out of luck, AAMI: Meet Ava, the new voice, and face, of Suncorp","InDaily",{"level":266,"checkedAt":221},"suncorp-aami-ava-virtual-employee",{"title":447,"useCases":448,"organization":449,"vendors":453,"summary":455,"stage":241,"year":242,"channels":456,"languages":457,"metrics":458,"outcomeDisclosed":232,"sources":459,"verification":469,"grade":336,"id":470,"organizationSlug":471},"NatWest: Cora digital human prototype with Soul Machines",[225],{"name":450,"anonymized":232,"country":451,"region":177,"industry":21,"wikidata":452},"NatWest Group","GB","Q13218598",[454],{"name":238,"role":239},"In February 2018 NatWest announced advanced testing of a digital human version of its Cora assistant, built with Soul Machines. The prototype lets customers hold a two way spoken conversation on a computer screen, tablet or phone and answers basic questions such as how to log in to online banking, how to apply for a mortgage and what to do about a lost card. NatWest said it would deploy the technology only if the pilot succeeded, and early testing suggested that customers who had avoided digital services might be more willing to talk to a digital human. No results were published.",[28,33,34],[245],[],[460,464],{"url":461,"title":462,"publisher":463},"https:\u002F\u002Fitbrief.co.uk\u002Fstory\u002Fkiwi-ai-companys-digital-human-being-tested-uk-bank","Kiwi AI company's digital human being tested at UK bank","IT Brief UK",{"url":465,"title":466,"publisher":467,"date":468},"https:\u002F\u002Ffinancefeeds.com\u002Fnatwest-embraces-ai-starts-testing-digital-human-cora\u002F","NatWest embraces AI, starts testing digital human Cora","FinanceFeeds","2018-02-22",{"level":266,"checkedAt":221},"natwest-cora-digital-human-pilot","natwest-group",0,[474,483],{"kpi":49,"label":475,"unit":249,"aggregate":232,"higherIsBetter":255,"n":476,"nUpTo":472,"median":477,"min":248,"max":313,"byClaimant":478,"vendorOnly":232,"points":480},"Interactions handled",2,12500,{"organization":479,"vendor":479,"regulator":472,"independent":472},1,[481,482],{"evidenceId":337,"organization":298,"value":313,"qualifier":250,"claimant":315,"grade":336,"pooled":255},{"evidenceId":268,"organization":231,"value":248,"qualifier":250,"claimant":252,"grade":267,"pooled":255},{"kpi":50,"label":484,"unit":320,"aggregate":255,"higherIsBetter":255,"n":479,"nUpTo":472,"median":319,"min":319,"max":319,"byClaimant":485,"vendorOnly":255,"points":486},"Accuracy",{"organization":472,"vendor":479,"regulator":472,"independent":472},[487],{"evidenceId":337,"organization":298,"value":319,"qualifier":321,"claimant":315,"grade":336,"pooled":255},{"low":489,"high":490},4800,86400,[492,567,585,599,621,640,659,671],{"slug":213,"title":493,"shortTitle":494,"definition":495,"status":9,"industries":496,"functions":502,"patterns":503,"audience":36,"autonomy":37,"adoptionStage":505,"segment":39,"evidenceCount":506,"publicEvidenceCount":507,"organizations":508,"bestGrade":560,"headline":561,"lastVerified":566,"indexable":255},"AI agent for first line contact centre service","First line contact center","An AI first line contact center agent answers inbound customer contact on phone, chat and messaging, resolving general and routine questions end to end in the customer's own language. It routes everything complex, sensitive or regulated to the right human team, with the context attached.",[497,21,498,499,500,501,23],"cross-industry","payments","telecommunications","travel-and-hospitality","retail-and-ecommerce",[25],[29,31,30,504],"classification-and-routing","mainstream",60,53,[509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,450,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559],"ABN AMRO","Air Canada","Air India","Airbnb","Groupe Al Omrane","Asiacell","AtlantaSanad Assurance","Attijariwafa bank","Bank ABC","Bank of America","Banque Misr","Batelco","BBVA México","Bank of the Philippine Islands","BT Group","China Unicom Sichuan","Commercial International Bank (CIB)","Commonwealth Bank of Australia","Dubai Land Department (DLD)","Dukhan Bank","Equity Bank Kenya","Etihad Airways","Etisalat Misr","Ingka Group","JetBlue","Jordan Ahli Bank","Kenya Power","Khaleeji Bank","Klarna","Kotak Mahindra Bank","Lufthansa Group","Magyar Telekom","Mashreq","Mobily","New Look","Nordea","Ooredoo Qatar","OTE Group","Pegasus Airlines","Poste Italiane","Qatar Airways","Qatar Islamic Bank","Royal Air Maroc","Roads and Transport Authority, Dubai (RTA)","Telkomsel","Together Credit Union","Vietcombank","Vodacom","Vodafone Germany","Vodafone","Zenith Bank","A",{"kpi":51,"label":562,"unit":320,"n":563,"nUpTo":479,"kind":564,"value":565,"qualifier":321,"claimant":269,"organization":269,"vendorReported":232},"Containment rate",13,"median",50,"2026-09-27",{"slug":214,"title":568,"shortTitle":569,"definition":570,"status":9,"industries":571,"functions":573,"patterns":575,"audience":36,"autonomy":37,"adoptionStage":576,"segment":39,"evidenceCount":577,"publicEvidenceCount":577,"organizations":578,"bestGrade":267,"headline":269,"lastVerified":566,"indexable":255},"AI home loan assistant with pre qualification","Home loan assistant","An AI home loan assistant with pre qualification answers a customer's home loan questions and runs indicative borrowing estimates, then books the customer with a mortgage specialist. It covers rates, loan to value, fees and the documents needed, runs the affordability estimate from the bank's published rules, and stays grounded in the bank's current, versioned product and policy documents.",[21,572],"real-estate",[574,26,25],"lending-and-credit",[30,29,31],"early-adopters",7,[579,580,581,582,450,583,584],"Better.com","Figure","InstaMortgage","Loft","Newrez","Safe Rate",{"slug":215,"title":586,"shortTitle":587,"definition":588,"status":9,"industries":589,"functions":590,"patterns":591,"audience":36,"autonomy":37,"adoptionStage":576,"segment":594,"evidenceCount":86,"publicEvidenceCount":86,"organizations":595,"bestGrade":267,"headline":269,"lastVerified":566,"indexable":255},"Conversational AI for insurance quote and buy","Conversational quote and buy","Conversational AI for insurance quote and buy is a customer facing agent that sells insurance directly in a conversation, asking rating questions and returning a price. It asks the rating questions in plain language, explains cover options, returns a price from the insurer's rating engine, handles objections and takes payment to bind the policy, with a licensed human available for advice and anything outside its limits.",[22],[26,25],[29,592,593,31],"agentic-workflow","recommendation-and-personalization","distribution",[596,597,598],"Aviva","Lemonade","Plymouth Rock Assurance",{"slug":216,"title":600,"shortTitle":601,"definition":602,"status":9,"industries":603,"functions":604,"patterns":606,"audience":36,"autonomy":37,"adoptionStage":576,"segment":39,"evidenceCount":577,"publicEvidenceCount":609,"organizations":610,"bestGrade":267,"headline":615,"lastVerified":566,"indexable":255},"AI assistant for digital account onboarding and KYC","Digital onboarding","A digital onboarding assistant is a customer facing AI that guides a new applicant, a person or a small merchant, through opening a digital account, card or relationship. It collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[21,498,23],[605,26,25],"onboarding-and-kyc",[29,607,608,592],"document-processing","computer-vision",4,[611,612,613,614],"Albo","Deutsche Bank","M-DAQ Global","OCBC",{"kpi":616,"label":617,"unit":618,"n":479,"nUpTo":472,"kind":619,"value":620,"qualifier":321,"claimant":315,"organization":613,"vendorReported":255},"productivity-gain","Productivity gain","multiplier","reported",30,{"slug":217,"title":622,"shortTitle":623,"definition":624,"status":9,"industries":625,"functions":626,"patterns":628,"audience":629,"autonomy":630,"adoptionStage":576,"segment":39,"evidenceCount":87,"publicEvidenceCount":87,"organizations":631,"bestGrade":267,"headline":638,"lastVerified":566,"indexable":255},"AI assistant for telecom retail stores, from associate copilot to digital human kiosk","Retail store and kiosk assistant","A retail store and kiosk assistant is AI for telecom shops that gives store associates quick, sourced answers on plans, promotions, devices and the customer's account during the conversation. It can also greet and serve customers directly on an in store screen or kiosk, sometimes as a digital human, handing them to an associate when they are ready to buy or need help.",[499],[26,25,627],"knowledge-management",[30,28,29,593],"employee-facing","assist",[632,633,634,635,636,637],"Bouygues Telecom","Deutsche Telekom","Orange Slovakia","Singtel","T-Mobile","Vodafone New Zealand",{"kpi":50,"label":484,"unit":320,"n":479,"nUpTo":472,"kind":619,"value":639,"qualifier":321,"claimant":315,"organization":632,"vendorReported":255},95,{"slug":218,"title":641,"shortTitle":642,"definition":643,"status":9,"industries":644,"functions":645,"patterns":647,"audience":36,"autonomy":37,"adoptionStage":505,"segment":39,"evidenceCount":563,"publicEvidenceCount":648,"organizations":649,"bestGrade":267,"headline":657,"lastVerified":566,"indexable":255},"AI agent for account and card servicing","Account and card servicing","An AI account and card servicing agent is an AI agent that resolves routine banking account and card requests for customers, end to end. It handles things like 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.",[21,498],[25,646],"operations",[29,31,592,30],11,[650,651,652,526,653,654,529,655,450,656,559],"Abu Dhabi Commercial Bank (ADCB)","Abu Dhabi Islamic Bank (ADIB)","BPCE Group","DBS Bank","Emirates NBD","KB Kookmin Card","Simple (Attijariwafa bank)",{"kpi":51,"label":562,"unit":320,"n":86,"nUpTo":472,"kind":564,"value":658,"qualifier":321,"claimant":269,"organization":269,"vendorReported":232},85,{"slug":219,"title":660,"shortTitle":661,"definition":662,"status":9,"industries":663,"functions":664,"patterns":665,"audience":36,"autonomy":37,"adoptionStage":38,"segment":39,"evidenceCount":479,"publicEvidenceCount":479,"organizations":666,"bestGrade":267,"headline":667,"lastVerified":566,"indexable":255},"AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent for ATM and self service device assistance helps customers with problems at or around ATMs and similar machines. It handles issues such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and it opens and tracks the claim or hands it to a person when it cannot be resolved.",[21],[25,646],[29,31,592],[450],{"kpi":668,"label":669,"unit":320,"n":479,"nUpTo":472,"kind":619,"value":670,"qualifier":321,"claimant":252,"organization":450,"vendorReported":232},"customer-satisfaction-uplift","Satisfaction uplift",150,{"slug":220,"title":672,"shortTitle":673,"definition":674,"status":9,"industries":675,"functions":677,"patterns":678,"audience":36,"autonomy":679,"adoptionStage":576,"evidenceCount":609,"publicEvidenceCount":609,"organizations":680,"bestGrade":267,"headline":269,"lastVerified":566,"indexable":255},"AI agent for branch finding and appointment booking","Branch and appointment booking","A branch and appointment booking agent is a conversational AI that finds the nearest suitable location and books an in person or video appointment with the right specialist. It checks opening hours and services offered, and records the visit reason so staff are prepared. In banking it answers where the nearest branch is and books the mortgage or business banker; retail, healthcare and property have the same job.",[497,21,501,676,572],"healthcare",[25,26],[29,592,30,31],"autonomous",[518,681,682,683],"Best Buy","Hemominas","MOGUL.sg",{"indexable":255,"reasons":685},[],[687,691,696,704,711,718,724,729,736,743,749,755,761,768,775,780,786,792,798,804,809,814,820,826,832,837,841,847,854,858,863,870,875,881,887,892,897],{"id":166,"label":688,"issuer":176,"region":177,"url":178,"description":689,"useCases":690,"indexable":255},"EU AI Act","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.",295,{"id":167,"label":692,"issuer":176,"region":177,"url":693,"description":694,"useCases":695,"indexable":255},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":697,"label":698,"issuer":699,"region":700,"url":701,"description":702,"useCases":703,"indexable":255},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":705,"label":706,"issuer":707,"region":183,"url":708,"description":709,"useCases":710,"indexable":255},"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.",115,{"id":712,"label":713,"issuer":714,"region":177,"url":715,"description":716,"useCases":717,"indexable":255},"uk-gdpr","UK GDPR","Information Commissioner's Office","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.",84,{"id":719,"label":720,"issuer":176,"region":177,"url":721,"description":722,"useCases":723,"indexable":255},"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.",73,{"id":171,"label":725,"issuer":726,"region":177,"url":727,"description":728,"useCases":507,"indexable":255},"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.",{"id":730,"label":731,"issuer":732,"region":234,"url":733,"description":734,"useCases":735,"indexable":255},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":737,"label":738,"issuer":739,"region":234,"url":740,"description":741,"useCases":742,"indexable":255},"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":744,"label":745,"issuer":176,"region":177,"url":746,"description":747,"useCases":748,"indexable":255},"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.",23,{"id":750,"label":751,"issuer":752,"region":700,"url":753,"description":754,"useCases":748,"indexable":255},"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":756,"label":757,"issuer":758,"region":183,"url":759,"description":760,"useCases":748,"indexable":255},"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.",{"id":762,"label":763,"issuer":764,"region":183,"url":765,"description":766,"useCases":767,"indexable":255},"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.",20,{"id":769,"label":770,"issuer":771,"region":177,"url":772,"description":773,"useCases":774,"indexable":255},"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":168,"label":776,"issuer":176,"region":177,"url":777,"description":778,"useCases":779,"indexable":255},"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.",15,{"id":781,"label":782,"issuer":783,"region":700,"url":784,"description":785,"useCases":779,"indexable":255},"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.",{"id":787,"label":788,"issuer":789,"region":183,"url":790,"description":791,"useCases":779,"indexable":255},"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":793,"label":794,"issuer":176,"region":177,"url":795,"description":796,"useCases":797,"indexable":255},"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":799,"label":800,"issuer":801,"region":183,"url":802,"description":803,"useCases":563,"indexable":255},"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.",{"id":805,"label":806,"issuer":176,"region":177,"url":807,"description":808,"useCases":563,"indexable":255},"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":810,"label":811,"issuer":176,"region":177,"url":812,"description":813,"useCases":563,"indexable":255},"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":815,"label":816,"issuer":817,"region":700,"url":818,"description":819,"useCases":563,"indexable":255},"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":821,"label":822,"issuer":182,"region":183,"url":823,"description":824,"useCases":825,"indexable":255},"us-ecoa-reg-b","ECOA and Regulation B","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":827,"label":828,"issuer":829,"region":177,"url":830,"description":831,"useCases":648,"indexable":255},"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.",{"id":833,"label":834,"issuer":176,"region":177,"url":835,"description":836,"useCases":648,"indexable":255},"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":169,"label":838,"issuer":176,"region":177,"url":839,"description":840,"useCases":648,"indexable":255},"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":842,"label":843,"issuer":732,"region":234,"url":844,"description":845,"useCases":846,"indexable":255},"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.",10,{"id":848,"label":849,"issuer":850,"region":183,"url":851,"description":852,"useCases":853,"indexable":255},"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":170,"label":855,"issuer":176,"region":177,"url":856,"description":857,"useCases":853,"indexable":255},"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.",{"id":172,"label":859,"issuer":860,"region":300,"url":861,"description":862,"useCases":87,"indexable":255},"CBUAE guidance on AI and ML","Central Bank of the UAE","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":864,"label":865,"issuer":866,"region":177,"url":867,"description":868,"useCases":869,"indexable":255},"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.",5,{"id":871,"label":872,"issuer":176,"region":177,"url":873,"description":874,"useCases":609,"indexable":255},"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.",{"id":876,"label":877,"issuer":878,"region":177,"url":879,"description":880,"useCases":609,"indexable":255},"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":882,"label":883,"issuer":884,"region":234,"url":885,"description":886,"useCases":86,"indexable":255},"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.",{"id":888,"label":889,"issuer":176,"region":177,"url":890,"description":891,"useCases":86,"indexable":255},"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":893,"label":894,"issuer":732,"region":234,"url":895,"description":896,"useCases":86,"indexable":255},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":898,"label":899,"issuer":900,"region":183,"url":901,"description":902,"useCases":86,"indexable":255},"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.",1792049855030]