[{"data":1,"prerenderedAt":680},["ShallowReactive",2],{"uc-client-meeting-notes-and-crm-update":3,"uc-regulations":477},{"useCase":4,"evidence":205,"blitsAiDeployments":357,"benchmarks":358,"indicative":370,"related":373,"indexability":475,"includeUnpublished":211},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":28,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":50,"macroEstimates":90,"feasibility":91,"implementation":105,"risk":148,"blitsAi":181,"faq":183,"related":193,"datePublished":200,"dateModified":200,"lastVerified":200,"changelog":201,"slug":204},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","AI meeting notes and CRM updates for advisors","An AI notetaker drafts the file note, follow up and CRM record after each client meeting. Microsoft reports UniSuper advisers save about 30 minutes per interaction.","published","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[12,13,14,15],"AI notetaker for financial advisors","advice file note automation","meeting summary to CRM","client meeting transcription",[17,18],"wealth-and-asset-management","banking",[20,21,22],"sales","regulatory-compliance","operations",[24,25,26,27],"summarization","speech-analytics","agentic-workflow","content-generation",[29,30,31,32],"microsoft-teams","voice","internal-tools","email","employee-facing","copilot","mainstream","front-office","After every client meeting an advisor has to write a file note, record what the client said about\ngoals, risk and circumstances, list what was agreed, send a follow up and update the CRM. With the\nnext meeting waiting, this work is easy to postpone, and a note written days later can miss what\nthe client actually said, which weakens the firm's record when a recommendation is later questioned.\n\nThe work is also expensive: it takes senior, client facing time, or an assistant who sat in the\nmeeting. And when notes are short, the CRM, which should hold the richest view of the client,\nholds little of the conversation, so the next meeting starts from memory.",[],"1. **Consent first.** The advisor tells the client the meeting will be transcribed by an AI\n   notetaker and records the client's consent; without it, nothing is recorded.\n2. **Capture.** The notetaker joins the video call or the phone line and produces a transcript\n   with speaker separation.\n3. **Draft the file note.** A summary is generated in the firm's template: attendees, topics,\n   client goals and circumstances mentioned, decisions, action items with owners, and anything the\n   client asked to be checked.\n4. **Advisor review.** The advisor corrects and approves the note and the draft follow up email.\n   Nothing is filed or sent without that approval.\n5. **Write back.** The approved note, tasks and key facts are written into the CRM and the\n   transcript is retained under the firm's record keeping policy.",[41,42,43],"employee-productivity","compliance","customer-experience",[45,46,47,48,49],"time-saved-per-task","hours-saved","productivity-gain","employee-adoption","accuracy",{"referenceOrg":51,"inputs":52,"formula":85,"currency":86,"period":87,"resultLabel":88,"caveat":89},"A wealth manager with 500 client facing advisors",[53,58,65,72,78],{"key":54,"label":55,"low":56,"high":56,"unit":54,"note":57},"advisors","Client facing advisors",500,"The reference firm.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"meetingsPerWeek","Client meetings per advisor per week",3,6,"meetings per advisor per week","Editorial assumption, replace with your own CRM activity data.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"minutesSaved","Minutes of write up saved per meeting",20,40,"minutes per meeting","Brackets the one reported saving on this page (UniSuper, about 30 minutes per interaction) and sits below the 45 minutes per meeting a Quilter Cheviot investment manager assumes in the firm's estimate; the low end allows for review time. Replace with your own pilot data.",{"key":73,"label":74,"low":75,"high":75,"unit":76,"note":77},"weeks","Working weeks per year",46,"weeks per year","Editorial assumption.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"hourlyCost","Fully loaded advisor cost per hour",80,150,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","advisors * meetingsPerWeek * minutesSaved / 60 * weeks * hourlyCost","USD","per year","Value of advisor time released from meeting write up","Counts released time only. It leaves out licence and transcription costs, the time to review each note, and the harder to price benefit of a more complete record in complaints and audits.",[],{"complexity":92,"complexityNote":93,"dataPrerequisites":94,"integrations":99},"medium","Transcription and summarization are mature. The work is in consent capture, the note template, retention rules for recordings and transcripts, and a reliable write back into the CRM.",[95,96,97,98],"An agreed file note template per meeting type (review, new advice, service)","Consent wording and a place to record the client's consent","Retention and deletion rules for audio, transcripts and notes","CRM field mapping for notes, tasks and client facts",[100,101,102,103,104],"Video meeting and telephony platforms","Speech to text with speaker separation","CRM such as Salesforce or Microsoft Dynamics 365","Email for the draft follow up","Records management or archive for retained transcripts",{"steps":106,"guardrails":122,"humanInTheLoop":128,"kpisToInstrument":129,"failureModes":135},[107,110,113,116,119],{"title":108,"detail":109},"Agree the note standard with compliance","Define what a good file note contains for each meeting type and which statements must be captured verbatim (for example a client's stated risk appetite or a refusal of advice).",{"title":111,"detail":112},"Design consent and retention","Write the consent script, record consent in the CRM, and decide how long audio and transcripts are kept and where. Some clients will decline; the process must work without AI.",{"title":114,"detail":115},"Pilot with a small group of advisors","Measure time to a finished note, edit rate and advisor satisfaction per meeting type, and collect notes that went wrong to improve the template and prompts.",{"title":117,"detail":118},"Automate the write back","Once notes are reliable, write the approved note, tasks and structured facts into the CRM through its API rather than copy and paste.",{"title":120,"detail":121},"Sample and supervise","Supervisors review a sample of notes against transcripts each month, with extra attention to meetings where advice was given.",[123,124,125,126,127],"No recording without recorded client consent, with an easy opt out","Advisor approval before any note is filed or any message is sent","Notes limited to what was said; no inferred emotions, health conditions or vulnerability labels without a human decision","PII masking and access control on transcripts, with retention limits","Every AI generated note labelled as such in the CRM","The advisor reviews, corrects and approves every note and follow up; supervisors sample notes against transcripts. Suspected vulnerability or complaints mentioned in a meeting go to a human process, not to an automated flag alone.",[130,131,132,133,134],"Median minutes from meeting end to approved note","Share of meetings with a complete note within 24 hours","Advisor edit rate per note section","Consent rate and opt outs","Supervisor sample findings per month",[136,139,142,145],{"title":137,"detail":138},"Rubber stamped notes","Advisors approve drafts without reading them, so errors enter the record. Track time spent reviewing and sample notes against transcripts.",{"title":140,"detail":141},"Missing or misattributed statements","Speaker separation fails on a phone line and a client's words are attributed to the advisor. Test on real audio and flag low confidence passages.",{"title":143,"detail":144},"Consent gaps","Recording starts before consent is captured, or consent is not stored. Make consent a hard gate in the workflow.",{"title":146,"detail":147},"Over retention","Audio and transcripts are kept indefinitely by default. Apply retention rules from day one.",{"euAiAct":149,"regulations":152,"guidance":161,"controls":174,"incidents":180},{"tier":150,"basis":151},"minimal","Transcribing and summarizing meetings for an employee is not a use listed in Annex III, and the advisor reviews every note before it is filed or sent. The tier would change if the tool inferred emotions: emotion recognition is high risk under Annex III point 1(c), and inferring the emotions of employees at work is prohibited under Article 5(1)(f). Both stay out of scope.",[153,154,155,156,157,158,159,160],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","dora","mas-ai-risk-management","iso-42001","mifid-ii",[162,168],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"ESMA public statement on the use of AI in the provision of retail investment services","European Securities and Markets Authority","europe","https://www.esma.europa.eu/sites/default/files/2024-05/ESMA35-335435667-5924__Public_Statement_on_AI_and_investment_services.pdf","Expects investment firms to keep comprehensive records on their use of AI, and applies MiFID II conduct and record keeping duties to AI supported advice.",{"title":169,"issuer":170,"region":171,"url":172,"note":173},"Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT)","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/feat","Transparency and accountability principles for data driven tools used in client relationships.",[175,176,177,178,179],"Consent capture and storage for every recorded meeting","Retention schedule for audio, transcripts and notes aligned with record keeping rules","Labelling of AI drafted notes and an audit trail of advisor edits","Monthly supervisory sampling of notes against transcripts","Inventory entry with an accountable owner and a documented note template",[],{"howToBuild":182},"On Blits.ai the capture step uses **speech to text** with **self hosted transcription and speaker\ndiarization**, so audio can be processed on Blits.ai infrastructure, with EU and UAE data residency. An\n**agentic workflow** then drafts the file note in the firm's template with an **AI agent** using\n**structured output**, and pauses for **human in the loop** approval by the advisor before\nanything is filed or sent.\n\nApproved notes and tasks are written to the CRM through **custom functions** (REST calls) or the\nready made **Microsoft Dynamics 365** tool, and the follow up is drafted for **email**.\n**PII masking** at the gateway and **guardrails** control what reaches the model, the workflow's\n**audit trail** records each run and approval, and **test suites** check note quality against a\nset of reference transcripts on every prompt or model change.",[184,187,190],{"question":185,"answer":186},"How much time does an AI notetaker save an advisor?","The one reported saving is about half an hour: Microsoft reports UniSuper advisers save roughly 30 minutes per client interaction. Quilter's estimate of more than 13,000 hours a month assumes 45 minutes saved per meeting, which is a projection rather than a measured result. Bank of America says the capability can save advisors up to four hours per meeting, which it presents as potential rather than a measured result.",{"question":188,"answer":189},"Do we need client consent to record and transcribe meetings?","Morgan Stanley and Merrill both run their notetakers with client consent. Whether consent is legally required depends on the jurisdiction and the lawful basis you rely on, so agree it with legal and compliance, and make sure the process still works when a client says no.",{"question":191,"answer":192},"Can the AI note replace the advisor's own record?","No. The note is a draft; the advisor approves it and remains responsible for its accuracy. Supervisors should sample notes against transcripts, especially for meetings where advice was given.",[194,195,196,197,198,199],"client-briefing-and-call-report-copilot","meeting-summarization-and-action-items","suitability-assessment-assistant","next-best-action-for-advisors","wealth-advisor-knowledge-assistant","call-quality-and-compliance-monitoring","2026-09-27",[202],{"date":200,"note":203},"First published","client-meeting-notes-and-crm-update",[206,235,259,285,316,332],{"title":207,"useCases":208,"organization":209,"vendors":214,"summary":217,"stage":218,"year":219,"channels":220,"languages":221,"metrics":223,"outcomeDisclosed":211,"sources":224,"verification":229,"grade":232,"id":233,"organizationSlug":234},"Merrill and Bank of America Private Bank: AI Powered Meeting Journey",[194,204],{"name":210,"anonymized":211,"country":212,"region":213,"industry":17},"Bank of America",false,"US","north-america",[215],{"name":210,"role":216},"in-house","Merrill Wealth Management and Bank of America Private Bank rolled out an AI meeting solution at full scale in March 2026. It consolidates client relationship insights and recent activity into meeting preparation material, takes notes in virtual meetings with client consent, and turns the decisions into a summary, tasks and documentation afterwards. The bank says the capability can save advisors up to four hours per meeting; it presents this as potential, not as a measured result, so it is not recorded as a metric here.","scaled",2026,[31],[222],"en",[],[225],{"url":226,"title":227,"publisher":210,"date":228},"https://newsroom.bankofamerica.com/content/newsroom/press-releases/2026/03/merrill-and-bank-of-america-private-bank-launch-ai-powered-meeti.html","Merrill and Bank of America Private Bank Launch AI-Powered Meeting Journey","2026-03-26",{"level":230,"checkedAt":231},"source-verified","2026-09-26","B","bank-of-america-merrill-ai-meeting-journey","bank-of-america",{"title":236,"useCases":237,"organization":238,"vendors":240,"summary":244,"stage":245,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":211,"sources":250,"verification":256,"grade":232,"id":257,"organizationSlug":258},"Morgan Stanley: AI @ Morgan Stanley Debrief meeting notes for financial advisors",[204],{"name":239,"anonymized":211,"country":212,"region":213,"industry":17},"Morgan Stanley",[241],{"name":242,"role":243},"OpenAI","model-provider","Morgan Stanley Wealth Management launched AI @ Morgan Stanley Debrief in June 2024. With client consent, the tool takes notes in client meetings, surfaces action items, summarizes the key points, drafts a follow up email for the advisor to edit and send at their discretion, and saves a note into Salesforce. The release quotes advisors on the time saved on note taking (one cites about half an hour per meeting) but gives no firm wide measurement.","production",2024,[31,32],[222],[],[251],{"url":252,"title":253,"publisher":239,"date":254,"archivedUrl":255},"https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch","Launch of AI @ Morgan Stanley Debrief","2024-06-26","https://web.archive.org/web/2026/https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch",{"level":230,"checkedAt":231},"morgan-stanley-debrief-meeting-notes","morgan-stanley",{"title":260,"useCases":261,"organization":263,"vendors":266,"summary":270,"stage":245,"year":271,"channels":272,"languages":273,"metrics":274,"outcomeDisclosed":211,"sources":275,"verification":281,"grade":282,"id":283,"organizationSlug":284},"Quilter: Microsoft 365 Copilot for meeting notes and investment writing",[204,262],"portfolio-reporting-and-commentary",{"name":264,"anonymized":211,"country":265,"region":165,"industry":17},"Quilter","GB",[267],{"name":268,"role":269},"Microsoft","platform","Quilter, a UK wealth manager, rolled out Microsoft 365 Copilot and names meetings and transcriptions as its biggest use case. Microsoft reports that Quilter estimates Copilot will save more than 13,000 hours per month of post call admin time; an investment manager at Quilter Cheviot builds that estimate from an assumed 45 minutes saved per client meeting across 174 investment managers doing about 100 meetings each. Both figures are projections, not measured savings, so neither is recorded as a metric. Quilter also tested turning a portfolio manager interview transcript into an investment commentary: about 15 minutes of prompting and half an hour of editing instead of a few days, which it describes as a one off test.",2025,[29,31],[222],[],[276],{"url":277,"title":278,"publisher":279,"archivedUrl":280},"https://www.microsoft.com/en/customers/story/23237-quilter-microsoft-365-copilot","Quilter achieves fastest-ever tech ROI with Microsoft 365 Copilot","Microsoft Customer Stories","https://web.archive.org/web/20250517152254/https://www.microsoft.com/en/customers/story/23237-quilter-microsoft-365-copilot",{"level":230,"checkedAt":200},"C","quilter-copilot-meeting-notes",null,{"title":286,"useCases":287,"organization":289,"vendors":292,"summary":298,"stage":245,"year":271,"channels":299,"languages":301,"metrics":302,"outcomeDisclosed":310,"sources":311,"verification":314,"grade":282,"id":315,"organizationSlug":284},"SEB: AI agent suggests responses and summarizes calls in wealth management",[288,204],"live-agent-assist",{"name":290,"anonymized":211,"country":291,"region":165,"industry":18},"SEB","SE",[293,295],{"name":294,"role":269},"Google Cloud",{"name":296,"role":297},"Bain & Company","integrator","SEB, a Nordic corporate bank, worked with Bain & Company to build an AI agent on Google Cloud for its wealth management division. The agent suggests responses during conversations with customers and generates call summaries afterwards. Google Cloud reports a 15% efficiency gain.",[300],"agent-desktop",[],[303],{"kpi":47,"value":304,"unit":305,"qualifier":306,"claimant":307,"quote":308,"sourceUrl":309},15,"percent","exact","vendor","The agent, built with Google Cloud, enhances end-customer conversations with suggested responses and generates call summaries, helping to increase efficiency by 15%.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",true,[312],{"url":309,"title":313,"publisher":294},"Real world gen AI use cases from the world's leading organizations",{"level":230,"checkedAt":231},"seb-wealth-advisor-agent-assist",{"title":317,"useCases":318,"organization":319,"vendors":322,"summary":324,"stage":245,"year":246,"channels":325,"languages":326,"metrics":327,"outcomeDisclosed":211,"sources":328,"verification":330,"grade":282,"id":331,"organizationSlug":284},"Commerzbank: AI agent documents client advisory calls",[204],{"name":320,"anonymized":211,"country":321,"region":165,"industry":18},"Commerzbank","DE",[323],{"name":294,"role":269},"Commerzbank implemented an AI agent on Gemini 1.5 Pro that automates the documentation of client calls, a manual task for its financial advisors. Google Cloud reports a significant reduction in processing time, which lets advisors spend more time with clients, but gives no figure.",[30,31],[],[],[329],{"url":309,"title":313,"publisher":294},{"level":230,"checkedAt":231},"commerzbank-client-call-documentation",{"title":333,"useCases":334,"organization":335,"vendors":338,"summary":340,"stage":245,"year":246,"channels":341,"languages":342,"metrics":343,"outcomeDisclosed":310,"sources":351,"verification":355,"grade":282,"id":356,"organizationSlug":284},"UniSuper: automated file notes for financial advisers",[204],{"name":336,"anonymized":211,"country":337,"region":171,"industry":17},"UniSuper","AU",[339],{"name":268,"role":269},"UniSuper, an Australian superannuation fund, uses Microsoft 365 Copilot to produce file notes that summarise the key details of each adviser conversation with a member held on Microsoft Teams. Microsoft reports advisers save roughly 30 minutes per client interaction; the fund also says the notes give it better visibility of interaction quality. The annual hours and extra members advised are projections and are not recorded.",[29],[222],[344],{"kpi":45,"value":345,"unit":346,"qualifier":347,"period":348,"claimant":307,"quote":349,"sourceUrl":350},30,"minutes","approximately","per client interaction","Advisors are saving roughly 30 minutes per client interaction on Microsoft Teams by using automated, bespoke file notes that summarise key details of each conversation.","https://news.microsoft.com/source/asia/features/super-thinking-how-unisuper-is-using-microsoft-365-copilot-to-deliver-faster-and-better-outcomes-for-members/",[352],{"url":350,"title":353,"publisher":354},"Super thinking: How UniSuper is using Microsoft 365 Copilot to deliver faster and better outcomes for members","Microsoft Source Asia",{"level":230,"checkedAt":231},"unisuper-copilot-advice-file-notes",0,[359,365],{"kpi":47,"label":360,"unit":305,"aggregate":310,"higherIsBetter":310,"n":361,"nUpTo":357,"median":304,"min":304,"max":304,"byClaimant":362,"vendorOnly":310,"points":363},"Productivity gain",1,{"organization":357,"vendor":361,"regulator":357,"independent":357},[364],{"evidenceId":315,"organization":290,"value":304,"qualifier":306,"claimant":307,"grade":282,"pooled":310},{"kpi":45,"label":366,"unit":346,"aggregate":310,"higherIsBetter":310,"n":361,"nUpTo":357,"median":345,"min":345,"max":345,"byClaimant":367,"vendorOnly":310,"points":368},"Time saved per task",{"organization":357,"vendor":361,"regulator":357,"independent":357},[369],{"evidenceId":356,"organization":336,"value":345,"qualifier":347,"claimant":307,"grade":282,"pooled":310},{"low":371,"high":372},1840000,13800000,[374,389,407,420,441,452],{"slug":194,"title":375,"shortTitle":376,"definition":377,"status":9,"industries":378,"functions":380,"patterns":382,"audience":33,"autonomy":34,"adoptionStage":384,"segment":385,"evidenceCount":61,"publicEvidenceCount":61,"organizations":386,"bestGrade":232,"headline":284,"lastVerified":200,"indexable":310},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","An AI copilot for relationship managers, mainly in corporate and commercial banking, whose main job is preparation: before a client meeting it assembles a briefing pack from filings, news, internal notes, product holdings and upcoming maturities, and afterwards it turns the banker's notes into a structured call report and CRM update. Unlike a meeting notetaker, which centres on capturing the conversation, it centres on the credit and cross sell context around the meeting; wealth advisor tools that also prepare meetings overlap with it. The banker reviews every output.",[18,17,379],"capital-markets",[20,381],"knowledge-management",[383,24,27,26],"rag-knowledge-assistant","early-adopters","specialized-businesses",[210,387,388],"Scotiabank","Standard Chartered",{"slug":195,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":398,"patterns":399,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":232,"headline":284,"lastVerified":200,"indexable":310},"AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[394,395,396,397],"cross-industry","government","technology","professional-services",[381,22],[24,25],5,[402,403,404,405,406],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3","Government Digital Service",{"slug":196,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":414,"audience":33,"autonomy":34,"adoptionStage":416,"segment":36,"evidenceCount":417,"publicEvidenceCount":417,"organizations":418,"bestGrade":232,"headline":284,"lastVerified":231,"indexable":310},"AI assistant for investment suitability assessment and reports","Suitability assessment","An AI assistant that checks whether a proposed product or portfolio fits a client's risk tolerance, objectives, knowledge, experience and financial situation against the firm's rules, flags mismatches, and drafts the suitability rationale and report for the advisor to confirm, while hard rule failures are decided by deterministic checks, not by the model.",[17,18],[21,20,413],"risk-management",[26,27,415],"classification-and-routing","emerging",2,[239,419],"Vanguard",{"slug":197,"title":421,"shortTitle":422,"definition":423,"status":9,"industries":424,"functions":425,"patterns":428,"audience":33,"autonomy":431,"adoptionStage":384,"segment":36,"evidenceCount":400,"publicEvidenceCount":400,"organizations":432,"bestGrade":232,"headline":437,"lastVerified":200,"indexable":310},"AI next best action prompts for wealth advisors","Advisor next best action","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[17,18],[20,426,427],"marketing","analytics-and-reporting",[429,430,27],"recommendation-and-personalization","prediction-and-scoring","assist",[433,434,435,239,436],"CIMB Niaga","Citi","JPMorgan Chase","UBS",{"kpi":48,"label":438,"unit":305,"n":361,"nUpTo":357,"kind":439,"value":81,"qualifier":306,"claimant":440,"organization":436,"vendorReported":211},"Employee adoption","reported","organization",{"slug":198,"title":442,"shortTitle":443,"definition":444,"status":9,"industries":445,"functions":446,"patterns":448,"audience":33,"autonomy":431,"adoptionStage":35,"segment":36,"evidenceCount":62,"publicEvidenceCount":62,"organizations":450,"bestGrade":232,"headline":284,"lastVerified":231,"indexable":310},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","A conversational assistant that answers a wealth advisor's or relationship manager's questions in seconds from the firm's own research, house view, product documentation and policies, with every answer linked to the source document so the advisor can check it before using it with a client.",[17,18],[381,20,447],"customer-service",[383,449],"conversational-agent",[210,434,435,239,436,451],"Yes Bank",{"slug":199,"title":453,"shortTitle":454,"definition":455,"status":9,"industries":456,"functions":461,"patterns":462,"audience":463,"autonomy":464,"adoptionStage":384,"evidenceCount":400,"publicEvidenceCount":400,"organizations":465,"bestGrade":282,"headline":471,"lastVerified":200,"indexable":310},"AI quality and compliance monitoring of every customer interaction","Call quality and compliance","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[394,18,457,458,459,460],"insurance","energy-and-utilities","telecommunications","retail-and-ecommerce",[447,21,22],[25,415,24],"back-office","supervised-agent",[466,467,468,469,470],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":472,"label":473,"unit":305,"n":361,"nUpTo":357,"kind":439,"value":474,"qualifier":347,"claimant":307,"organization":466,"vendorReported":310},"quality-score-uplift","Quality score uplift",10,{"indexable":310,"reasons":476},[],[478,484,489,496,503,508,514,520,525,532,538,544,551,557,563,568,575,581,587,593,599,605,610,614,619,626,633,638,643,650,657,663,669,674],{"id":153,"label":479,"issuer":480,"region":165,"url":481,"description":482,"useCases":483,"indexable":310},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":154,"label":485,"issuer":480,"region":165,"url":486,"description":487,"useCases":488,"indexable":310},"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":159,"label":490,"issuer":491,"region":492,"url":493,"description":494,"useCases":495,"indexable":310},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":497,"label":498,"issuer":499,"region":213,"url":500,"description":501,"useCases":502,"indexable":310},"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":157,"label":504,"issuer":480,"region":165,"url":505,"description":506,"useCases":507,"indexable":310},"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":155,"label":509,"issuer":510,"region":165,"url":511,"description":512,"useCases":513,"indexable":310},"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":156,"label":515,"issuer":516,"region":165,"url":517,"description":518,"useCases":519,"indexable":310},"FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":158,"label":521,"issuer":170,"region":171,"url":522,"description":523,"useCases":524,"indexable":310},"MAS AI risk management guidelines","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":526,"label":527,"issuer":528,"region":171,"url":529,"description":530,"useCases":531,"indexable":310},"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":533,"label":534,"issuer":535,"region":492,"url":536,"description":537,"useCases":68,"indexable":310},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":539,"label":540,"issuer":541,"region":213,"url":542,"description":543,"useCases":68,"indexable":310},"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":545,"label":546,"issuer":547,"region":165,"url":548,"description":549,"useCases":550,"indexable":310},"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":552,"label":553,"issuer":554,"region":492,"url":555,"description":556,"useCases":304,"indexable":310},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":558,"label":559,"issuer":480,"region":165,"url":560,"description":561,"useCases":562,"indexable":310},"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":564,"label":565,"issuer":480,"region":165,"url":566,"description":567,"useCases":562,"indexable":310},"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":569,"label":570,"issuer":571,"region":213,"url":572,"description":573,"useCases":574,"indexable":310},"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":576,"label":577,"issuer":480,"region":165,"url":578,"description":579,"useCases":580,"indexable":310},"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":582,"label":583,"issuer":584,"region":213,"url":585,"description":586,"useCases":580,"indexable":310},"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":588,"label":589,"issuer":590,"region":492,"url":591,"description":592,"useCases":580,"indexable":310},"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":594,"label":595,"issuer":480,"region":165,"url":596,"description":597,"useCases":598,"indexable":310},"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":600,"label":601,"issuer":602,"region":213,"url":603,"description":604,"useCases":598,"indexable":310},"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":606,"label":607,"issuer":170,"region":171,"url":608,"description":609,"useCases":474,"indexable":310},"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.",{"id":160,"label":611,"issuer":480,"region":165,"url":612,"description":613,"useCases":474,"indexable":310},"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":615,"label":616,"issuer":480,"region":165,"url":617,"description":618,"useCases":474,"indexable":310},"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":620,"label":621,"issuer":622,"region":165,"url":623,"description":624,"useCases":625,"indexable":310},"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":627,"label":628,"issuer":629,"region":213,"url":630,"description":631,"useCases":632,"indexable":310},"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.",8,{"id":634,"label":635,"issuer":480,"region":165,"url":636,"description":637,"useCases":632,"indexable":310},"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":639,"label":640,"issuer":480,"region":165,"url":641,"description":642,"useCases":62,"indexable":310},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":644,"label":645,"issuer":646,"region":647,"url":648,"description":649,"useCases":400,"indexable":310},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":651,"label":652,"issuer":653,"region":165,"url":654,"description":655,"useCases":656,"indexable":310},"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.",4,{"id":658,"label":659,"issuer":660,"region":165,"url":661,"description":662,"useCases":656,"indexable":310},"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":664,"label":665,"issuer":666,"region":171,"url":667,"description":668,"useCases":61,"indexable":310},"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":670,"label":671,"issuer":480,"region":165,"url":672,"description":673,"useCases":61,"indexable":310},"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":675,"label":676,"issuer":677,"region":213,"url":678,"description":679,"useCases":61,"indexable":310},"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.",1790598302681]