[{"data":1,"prerenderedAt":662},["ShallowReactive",2],{"uc-wealth-advisor-knowledge-assistant":3,"uc-regulations":458},{"useCase":4,"evidence":206,"blitsAiDeployments":359,"benchmarks":360,"indicative":367,"related":370,"indexability":456,"includeUnpublished":212},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":47,"macroEstimates":87,"feasibility":88,"implementation":101,"risk":147,"blitsAi":182,"faq":184,"related":194,"datePublished":200,"dateModified":200,"lastVerified":201,"changelog":202,"slug":205},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","AI knowledge assistant for wealth advisors","Advisor assistants answer from house research, product and policy documents, with sources. Morgan Stanley says 98% of its Financial Advisor teams adopted one.","published","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.",[12,13,14,15],"advisor knowledge copilot","relationship manager assistant","RM copilot","wealth advisor chatbot",[17,18],"wealth-and-asset-management","banking",[20,21,22],"knowledge-management","sales","customer-service",[24,25],"rag-knowledge-assistant","conversational-agent",[27,28],"internal-tools","microsoft-teams","employee-facing","assist","mainstream","front-office","A wealth advisor is expected to know the firm's view on markets, sectors and asset classes, the\nterms of hundreds of products, tax and structuring notes for several jurisdictions, and the\npolicies that govern what may be offered to whom. That knowledge lives in research libraries,\nproduct term sheets, intranet pages and email, and it changes often. Finding the right paragraph\nwhile a client waits is slow, so advisors lean on memory, ask a colleague or promise to call back.\n\nThe result is inconsistent answers, lost time before and during client conversations, and a real\nconduct risk when an advisor paraphrases an outdated view or a product rule from memory. Junior\nadvisors and relationship managers in new markets are hit hardest, because they do not yet know\nwhere anything lives.",[],"1. **Curate the corpus.** Research notes, the house view, product documents, tax and structuring\n   notes and policies are loaded into a knowledge base with an owner, a publication date and an\n   audience per document.\n2. **Ask in plain language.** The advisor asks \"what is our current view on European banks\" or\n   \"can this structured note be sold to a client in Hong Kong\" in chat or inside the collaboration\n   tool.\n3. **Retrieve and answer with citations.** Hybrid search finds the relevant passages; the model\n   answers only from them and links each statement to its source document and date.\n4. **Respect entitlements.** The assistant only retrieves documents the advisor may see, and it\n   refuses questions it cannot answer from approved content instead of guessing.\n5. **Learn from feedback.** Thumbs down, unanswered questions and stale document hits go to the\n   content owners, who fix the corpus rather than the prompt.",[37,38,39,40],"employee-productivity","compliance","customer-experience","speed",[42,43,44,45,46],"employee-adoption","handling-time-reduction","time-saved-per-task","interactions-handled","accuracy",{"referenceOrg":48,"inputs":49,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A wealth manager with 500 client facing advisors",[50,55,62,69,75],{"key":51,"label":52,"low":53,"high":53,"unit":51,"note":54},"advisors","Client facing advisors",500,"The reference firm.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"questionsPerWeek","Knowledge questions per advisor per week",5,15,"questions per advisor per week","Editorial assumption, replace with your own search and help desk volumes.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"minutesSaved","Minutes saved per question",3,10,"minutes per question","Conservative against the benchmark on this page (J.P. Morgan reports advisers find the right information up to 95% faster); most questions are short lookups.",{"key":70,"label":71,"low":72,"high":72,"unit":73,"note":74},"weeks","Working weeks per year",46,"weeks per year","Editorial assumption.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyCost","Fully loaded advisor cost per hour",80,150,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","advisors * questionsPerWeek * minutesSaved / 60 * weeks * hourlyCost","USD","per year","Value of advisor time released from searching","Time released is only value if advisors spend it with clients. The estimate leaves out the cost of running the assistant and curating content, and the harder to measure benefit of fewer answers given from outdated material.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"low","The model work is simple; the effort is in the content. Research, product and policy documents need owners, review dates and audience tags, and entitlements must follow the advisor's booking centre and licence.",[92,93,94,95],"Research library and house view with publication dates and authors","Product term sheets, key information documents and product governance rules per market","Tax, structuring and policy notes with an owner and a review date","Entitlement data that maps advisors to booking centres, licences and client segments",[97,98,99,100],"Research and content management systems","Document repositories such as SharePoint","Identity and access management for document level permissions","Collaboration tools such as Microsoft Teams, or the advisor desktop",{"steps":102,"guardrails":121,"humanInTheLoop":127,"kpisToInstrument":128,"failureModes":134},[103,106,109,112,115,118],{"title":104,"detail":105},"Start with one corpus and one audience","Pick the content advisors search most (usually the house view and product documentation) for one booking centre. Measure what they ask in the first weeks before adding more sources.",{"title":107,"detail":108},"Fix the content before the model","Remove superseded documents, tag every document with an owner, audience and expiry date, and agree who updates it. An assistant that retrieves a superseded document gives a wrong answer with a valid looking citation.",{"title":110,"detail":111},"Make citations mandatory","Every answer shows its source passages and dates, and the assistant refuses when retrieval finds nothing relevant. Advisors must be able to check an answer in one click.",{"title":113,"detail":114},"Enforce entitlements at retrieval time","Filter documents by the advisor's permissions before they reach the model, so a restricted research note or a product not approved in that market never appears in an answer.",{"title":116,"detail":117},"Build a test set from real questions","Collect a few hundred real advisor questions with approved answers and run them on every content or model change, including questions that must be refused.",{"title":119,"detail":120},"Roll out with training and feedback loops","Train advisors that the assistant informs and they decide, publish usage and feedback per desk, and route every thumbs down to the content owner.",[122,123,124,125,126],"Answers only from approved, dated content, with a refusal when nothing relevant is retrieved","Citation of the source document and date on every answer","Document level permissions applied before retrieval, per booking centre and licence","No personalized recommendations; the assistant informs, the advisor advises","PII masking so client names and account data are not sent to the model unless required","The advisor decides what, if anything, to tell a client and stays responsible for the advice. Content owners review flagged answers weekly and research or product governance approves any new corpus before it is added.",[129,130,131,132,133],"Weekly active advisors as a share of licensed advisors","Share of questions answered with a citation versus refused","Answer accuracy on a monthly human reviewed sample","Median time to answer compared with the previous search process","Feedback rate and top unanswered topics",[135,138,141,144],{"title":136,"detail":137},"Stale house view","The assistant quotes last quarter's view because the old note was never retired. Expire documents automatically and prefer the newest version at retrieval.",{"title":139,"detail":140},"Answers that look like advice","Advisors paste a fluent answer into a client email without checking it. Train on the assist posture, keep citations visible and log what is copied.",{"title":142,"detail":143},"Entitlement leakage","A restricted or wrong market document appears in an answer. Filter by permission before retrieval, not after generation.",{"title":145,"detail":146},"Adoption stalls after launch","Advisors try it, get a poor answer and stop. Seed the corpus with the most searched content and publish improvements.",{"euAiAct":148,"regulations":151,"guidance":158,"controls":175,"incidents":181},{"tier":149,"basis":150},"limited","Article 50(1) requires that people who interact directly with an AI system are informed of it, unless this is obvious from the context, as it usually is for an internal assistant labelled as AI; Article 50(2) requires providers of systems that generate text to mark the output as AI generated in a machine readable way. Helping advisors find information is not an Annex III use and not a prohibited practice under Article 5. It would become high risk only if the system were used to evaluate the creditworthiness of clients (point 5(b)) or to evaluate or make decisions about advisors (point 4(b)). If the assistant were opened to clients, they would have to be told they are dealing with AI.",[152,153,154,155,156,157],"eu-ai-act","gdpr","mifid-ii","dora","mas-ai-risk-management","iso-42001",[159,165,171],{"title":160,"issuer":161,"region":162,"url":163,"note":164},"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","Decisions remain the responsibility of the management body whether taken by people or AI tools, including third party AI used by staff; MiFID II organisational, conduct and record keeping requirements apply.",{"title":166,"issuer":167,"region":168,"url":169,"note":170},"Artificial Intelligence (AI) Model Risk Management (information paper)","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2024/artificial-intelligence-model-risk-management","Good practices observed in MAS's mid 2024 thematic review of banks' AI and generative AI model risk management, covering governance and oversight, key risk management systems and processes, and development and deployment.",{"title":172,"issuer":167,"region":168,"url":173,"note":174},"Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT) in the Use of Artificial Intelligence and Data Analytics in Singapore's Financial Sector","https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/feat","Foundational principles for firms offering financial products and services on the responsible use of AI and data analytics, including internal governance, accountability and transparency.",[176,177,178,179,180],"Entry in the AI inventory with an accountable business owner and a content owner per corpus","Document ownership, audience tags and expiry dates enforced in the knowledge base","Retrieval and answer logs kept for supervision and investigation","Regression test set run on every model or content change","Clear written rule that the assistant supports but does not replace the advisor's judgment",[],{"howToBuild":183},"On Blits.ai this is an **AI agent** grounded in a **knowledge base** that holds the research\nlibrary, house view and product documents, retrieved with **hybrid search** (vector plus BM25).\nDocument ingestion covers PDF, Word, PowerPoint and email files, and the document library keeps\nversions so an outdated note can be reverted or removed. The agent is instructed to answer only\nfrom retrieved passages and to cite them.\n\nAdvisors reach it in **Microsoft Teams** or through the API inside their own desktop.\n**Guardrails** check inputs and outputs, **PII masking** keeps client data out of prompts, and\nrole based access controls who can change the agent. **Test suites** replay real advisor\nquestions with LLM grading against approved answers on every change, and analytics and feedback\nshow adoption and unanswered topics. The platform is model agnostic and can run in the EU or UAE\nregion for data residency.",[185,188,191],{"question":186,"answer":187},"How many advisors actually use these assistants?","Where firms publish figures, adoption is high. Morgan Stanley said in June 2024 that 98% of its Financial Advisor teams had adopted its assistant, and Bank of America reports more than 23 million interactions with ask MERRILL and ask PRIVATE BANK in 2024.",{"question":189,"answer":190},"How do you stop the assistant from giving wrong answers to clients?","Ground it only in approved, dated content, show the source with every answer and make it refuse when nothing relevant is found. The advisor, not the assistant, talks to the client and checks the source first.",{"question":192,"answer":193},"Is this the same as enterprise knowledge search?","It uses the same retrieval pattern, but the corpus and controls are specific to advice: research and house view, product governance per market, and entitlements by booking centre and licence. Those controls matter because the answers feed conversations with clients.",[195,196,197,198,199],"enterprise-knowledge-search","investment-research-summarization","next-best-action-for-advisors","client-meeting-notes-and-crm-update","suitability-assessment-assistant","2026-09-27","2026-09-26",[203],{"date":200,"note":204},"First published","wealth-advisor-knowledge-assistant",[207,236,266,289,317,339],{"title":208,"useCases":209,"organization":210,"vendors":215,"summary":218,"stage":219,"year":220,"channels":221,"languages":222,"metrics":224,"outcomeDisclosed":212,"sources":225,"verification":231,"grade":233,"id":234,"organizationSlug":235},"Citi Wealth: AskWealth assistant and Advisor Insights",[205,196,197],{"name":211,"anonymized":212,"country":213,"region":214,"industry":17},"Citi",false,"US","global",[216],{"name":211,"role":217},"in-house","Citi Wealth launched two AI tools built by its Data, Analytics and Innovation team. AskWealth is a generative AI assistant that gives service teams, advisors and managers answers across the wealth business, so that advisors can reach market insights and research when clients ask questions; after a launch in Asia it became available to Citi Wealth colleagues worldwide. Advisor Insights is a dashboard of timely messages about market moves, portfolios and events, including Chief Investment Office insights, piloted with Citigold and Citi Private Client advisors in North America with a wider rollout planned for Q4 2025 and Q1 2026. Citi says the tools will save hours of time but published no figures.","production",2025,[27],[223],"en",[],[226],{"url":227,"title":228,"publisher":229,"date":230},"https://www.citigroup.com/global/news/press-release/2025/citi-wealth-launches-advisor-insights-askwealth","Citi Wealth Launches \"Advisor Insights\" Pilot and \"AskWealth,\" AI-Driven \"Gamechangers\" for Client Communications","Citigroup","2025-08-25",{"level":232,"checkedAt":201},"source-verified","B","citi-wealth-askwealth-and-advisor-insights","citi",{"title":237,"useCases":238,"organization":239,"vendors":242,"summary":244,"stage":245,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":258,"sources":259,"verification":263,"grade":233,"id":264,"organizationSlug":265},"Bank of America: ask MERRILL and ask PRIVATE BANK knowledge assistants for advisers",[195,205],{"name":240,"anonymized":212,"country":213,"region":241,"industry":17},"Bank of America","north-america",[243],{"name":240,"role":217},"Merrill and Bank of America Private Bank teams use ask MERRILL and ask PRIVATE BANK, built on the technology behind Erica, to curate the information they need for clients. For more complex requests, the chat can connect teams with experts at the bank. The bank reports more than 23 million interactions with the two tools in 2024.","scaled",2024,[27],[223],[250],{"kpi":45,"value":251,"unit":252,"qualifier":253,"period":254,"claimant":255,"quote":256,"sourceUrl":257},23000000,"count","at-least","calendar year 2024","organization","In 2024, there were more than 23 million interactions with ask MERRILL and ask PRIVATE BANK, an increase of 1 million over 2023, helping employees more proactively connect with clients about timely and relevant opportunities.","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/04/ai-adoption-by-bofa-s-global-workforce-improves-productivity--cl.html",true,[260],{"url":257,"title":261,"publisher":240,"date":262},"AI Adoption by BofA's Global Workforce Improves Productivity, Client Service","2025-04-08",{"level":232,"checkedAt":201},"bank-of-america-ask-merrill-and-ask-private-bank","bank-of-america",{"title":267,"useCases":268,"organization":269,"vendors":271,"summary":275,"stage":245,"year":276,"channels":277,"languages":278,"metrics":279,"outcomeDisclosed":258,"sources":280,"verification":286,"grade":233,"id":287,"organizationSlug":288},"Morgan Stanley: AI @ Morgan Stanley Assistant gives advisers access to the firm's intellectual capital",[195,205],{"name":270,"anonymized":212,"country":213,"region":241,"industry":17},"Morgan Stanley",[272],{"name":273,"role":274},"OpenAI","model-provider","Morgan Stanley Wealth Management fully rolled out the AI @ Morgan Stanley Assistant in September 2023, a generative AI chatbot that gives Financial Advisors quick access to the firm's intellectual capital. The rollout followed the firm's March 2023 announcement of OpenAI as its strategic partner. In its June 2024 release the firm said that 98% of Financial Advisor teams had adopted it. It was followed by AI @ Morgan Stanley Debrief, which drafts meeting notes and follow up emails with client consent.",2023,[27],[223],[],[281],{"url":282,"title":283,"publisher":270,"date":284,"archivedUrl":285},"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/20260903124043/https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch",{"level":232,"checkedAt":200},"morgan-stanley-ai-assistant-knowledge-search","morgan-stanley",{"title":290,"useCases":291,"organization":292,"vendors":294,"summary":296,"stage":219,"year":220,"channels":297,"languages":298,"metrics":299,"outcomeDisclosed":258,"sources":308,"verification":313,"grade":314,"id":315,"organizationSlug":316},"J.P. Morgan: Coach AI for private client advisers",[205,197],{"name":293,"anonymized":212,"country":213,"region":241,"industry":17},"JPMorgan Chase",[295],{"name":293,"role":217},"J.P. Morgan's private client advisers use an internal generative AI tool, Coach AI, to find research and content for client conversations more quickly. The firm's asset and wealth management chief executive credited its AI tools, which pull clients' trading patterns and anticipate their questions, with helping advisers respond to clients during the April 2025 market sell off. Its asset and wealth management chief information officer said advisers find the right information up to 95% faster. The firm's aim of growing adviser client books by 50% over three to five years is a target, not a result.",[27],[223],[300],{"kpi":301,"value":302,"unit":303,"qualifier":304,"period":305,"claimant":255,"quote":306,"sourceUrl":307},"search-time-reduction",95,"percent","up-to","time to find information for a client conversation","Our advisers are finding the right information up to 95% faster - which means they spend less time searching and more time engaging in meaningful conversations with clients","https://www.aol.com/news/jpmorgan-says-ai-helped-boost-170825172.html",[309],{"url":307,"title":310,"publisher":311,"date":312},"JPMorgan says AI helped boost sales, add clients in market turmoil","Reuters via AOL","2025-05-05",{"level":232,"checkedAt":201},"C","jpmorgan-coach-ai-advisers","jpmorgan-chase",{"title":318,"useCases":319,"organization":320,"vendors":323,"summary":327,"stage":219,"year":246,"channels":328,"languages":329,"metrics":330,"outcomeDisclosed":212,"sources":331,"verification":336,"grade":314,"id":337,"organizationSlug":338},"UBS: UBS Red smart assistants for client advisors",[205,196],{"name":321,"anonymized":212,"country":322,"region":162,"industry":17},"UBS","CH",[324],{"name":325,"role":326},"Microsoft","platform","UBS built two domain specific assistants, together called UBS Red, on Azure AI Search and Azure OpenAI Service to give client advisors fast, multilingual access to the bank's investment advice and product content during client work. UBS digitized about 60,000 investment advice and product documents into a queryable knowledge base, which it says saves considerable time in meeting preparation and research. Within 10 months the wider Azure OpenAI footprint reached key wealth, banking and operations divisions in the Switzerland, Hong Kong and Singapore booking centres. No usage or time saving figure specific to UBS Red is published.",[27],[],[],[332],{"url":333,"title":334,"publisher":335},"https://www.microsoft.com/en/customers/story/19796-ubs-azure","UBS and Microsoft unite: Co-creating the future of banking with Azure AI","Microsoft Customer Stories",{"level":232,"checkedAt":201},"ubs-red-client-advisor-assistants",null,{"title":340,"useCases":341,"organization":342,"vendors":345,"summary":347,"stage":348,"year":246,"channels":349,"languages":350,"metrics":351,"outcomeDisclosed":212,"sources":352,"verification":357,"grade":314,"id":358,"organizationSlug":338},"Yes Bank: RM Assist Chatbot (Ask Genie) for relationship managers",[205],{"name":343,"anonymized":212,"country":344,"region":168,"industry":18},"Yes Bank","IN",[346],{"name":325,"role":326},"Yes Bank developed RM Assist (Ask Genie), an internal chatbot on Azure OpenAI that gives relationship managers answers to customer queries from the bank's repository of product and policy documents and helps them prepare product pitches. Microsoft's page, which also names Power Apps Copilot, presents it as a way to improve first time resolution of customer interactions. The benefits are described as expectations; no live usage or outcome figures are published.","announced",[27],[],[],[353],{"url":354,"title":355,"publisher":356},"https://www.microsoft.com/en-in/aifirstmovers/yes-bank","Yes Bank: Answers to all questions","Microsoft India",{"level":232,"checkedAt":201},"yes-bank-rm-assist-chatbot",0,[361],{"kpi":45,"label":362,"unit":252,"aggregate":212,"higherIsBetter":258,"n":363,"nUpTo":359,"median":251,"min":251,"max":251,"byClaimant":364,"vendorOnly":212,"points":365},"Interactions handled",1,{"organization":363,"vendor":359,"regulator":359,"independent":359},[366],{"evidenceId":264,"organization":240,"value":251,"qualifier":253,"claimant":255,"grade":233,"pooled":258},{"low":368,"high":369},460000,8625000,[371,388,408,423,443],{"slug":195,"title":372,"shortTitle":373,"definition":374,"status":9,"industries":375,"functions":380,"patterns":382,"audience":29,"autonomy":30,"adoptionStage":31,"evidenceCount":384,"publicEvidenceCount":384,"organizations":385,"bestGrade":233,"headline":338,"lastVerified":200,"indexable":258},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[376,18,17,377,378,379],"cross-industry","insurance","government","professional-services",[20,381,22],"operations",[24,25,383],"summarization",4,[240,270,386,387],"SIGNAL IDUNA","Wells Fargo",{"slug":196,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":394,"patterns":396,"audience":29,"autonomy":399,"adoptionStage":400,"segment":32,"evidenceCount":384,"publicEvidenceCount":384,"organizations":401,"bestGrade":233,"headline":403,"lastVerified":200,"indexable":258},"AI summaries of investment research and the house view","Research summaries","An AI assistant that condenses long research reports, overnight market moves and the house view into short, sourced briefings for advisors and analysts, answers \"what is our view on X\" on demand, and adapts approved research for different client segments and languages, with every figure traced to the original research.",[17,393,18],"capital-markets",[395,21,20],"analytics-and-reporting",[383,24,397,398],"content-generation","translation","copilot","early-adopters",[211,402,270,321],"Deutsche Bank",{"kpi":44,"label":404,"unit":405,"n":359,"nUpTo":363,"kind":406,"value":407,"qualifier":304,"claimant":255,"organization":402,"vendorReported":212},"Time saved per task","minutes","reported",120,{"slug":197,"title":409,"shortTitle":410,"definition":411,"status":9,"industries":412,"functions":413,"patterns":415,"audience":29,"autonomy":30,"adoptionStage":400,"segment":32,"evidenceCount":58,"publicEvidenceCount":58,"organizations":418,"bestGrade":233,"headline":420,"lastVerified":200,"indexable":258},"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],[21,414,395],"marketing",[416,417,397],"recommendation-and-personalization","prediction-and-scoring",[419,211,293,270,321],"CIMB Niaga",{"kpi":42,"label":421,"unit":303,"n":363,"nUpTo":359,"kind":406,"value":78,"qualifier":422,"claimant":255,"organization":321,"vendorReported":212},"Employee adoption","exact",{"slug":198,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":428,"patterns":430,"audience":29,"autonomy":399,"adoptionStage":31,"segment":32,"evidenceCount":433,"publicEvidenceCount":433,"organizations":434,"bestGrade":233,"headline":439,"lastVerified":200,"indexable":258},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","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.",[17,18],[21,429,381],"regulatory-compliance",[383,431,432,397],"speech-analytics","agentic-workflow",6,[240,435,270,436,437,438],"Commerzbank","Quilter","SEB","UniSuper",{"kpi":440,"label":441,"unit":303,"n":363,"nUpTo":359,"kind":406,"value":59,"qualifier":422,"claimant":442,"organization":437,"vendorReported":258},"productivity-gain","Productivity gain","vendor",{"slug":199,"title":444,"shortTitle":445,"definition":446,"status":9,"industries":447,"functions":448,"patterns":450,"audience":29,"autonomy":399,"adoptionStage":452,"segment":32,"evidenceCount":453,"publicEvidenceCount":453,"organizations":454,"bestGrade":233,"headline":338,"lastVerified":201,"indexable":258},"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],[429,21,449],"risk-management",[432,397,451],"classification-and-routing","emerging",2,[270,455],"Vanguard",{"indexable":258,"reasons":457},[],[459,465,470,476,483,488,495,502,507,514,521,527,534,540,546,551,558,564,570,576,582,588,593,597,602,609,616,621,626,633,639,645,651,656],{"id":152,"label":460,"issuer":461,"region":162,"url":462,"description":463,"useCases":464,"indexable":258},"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":153,"label":466,"issuer":461,"region":162,"url":467,"description":468,"useCases":469,"indexable":258},"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":157,"label":471,"issuer":472,"region":214,"url":473,"description":474,"useCases":475,"indexable":258},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":477,"label":478,"issuer":479,"region":241,"url":480,"description":481,"useCases":482,"indexable":258},"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":155,"label":484,"issuer":461,"region":162,"url":485,"description":486,"useCases":487,"indexable":258},"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":489,"label":490,"issuer":491,"region":162,"url":492,"description":493,"useCases":494,"indexable":258},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":496,"label":497,"issuer":498,"region":162,"url":499,"description":500,"useCases":501,"indexable":258},"uk-consumer-duty","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":156,"label":503,"issuer":167,"region":168,"url":504,"description":505,"useCases":506,"indexable":258},"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":508,"label":509,"issuer":510,"region":168,"url":511,"description":512,"useCases":513,"indexable":258},"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":515,"label":516,"issuer":517,"region":214,"url":518,"description":519,"useCases":520,"indexable":258},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":522,"label":523,"issuer":524,"region":241,"url":525,"description":526,"useCases":520,"indexable":258},"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":528,"label":529,"issuer":530,"region":162,"url":531,"description":532,"useCases":533,"indexable":258},"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":535,"label":536,"issuer":537,"region":214,"url":538,"description":539,"useCases":59,"indexable":258},"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":541,"label":542,"issuer":461,"region":162,"url":543,"description":544,"useCases":545,"indexable":258},"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":547,"label":548,"issuer":461,"region":162,"url":549,"description":550,"useCases":545,"indexable":258},"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":552,"label":553,"issuer":554,"region":241,"url":555,"description":556,"useCases":557,"indexable":258},"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":559,"label":560,"issuer":461,"region":162,"url":561,"description":562,"useCases":563,"indexable":258},"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":565,"label":566,"issuer":567,"region":241,"url":568,"description":569,"useCases":563,"indexable":258},"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":571,"label":572,"issuer":573,"region":214,"url":574,"description":575,"useCases":563,"indexable":258},"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":577,"label":578,"issuer":461,"region":162,"url":579,"description":580,"useCases":581,"indexable":258},"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":583,"label":584,"issuer":585,"region":241,"url":586,"description":587,"useCases":581,"indexable":258},"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":589,"label":590,"issuer":167,"region":168,"url":591,"description":592,"useCases":66,"indexable":258},"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":154,"label":594,"issuer":461,"region":162,"url":595,"description":596,"useCases":66,"indexable":258},"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":598,"label":599,"issuer":461,"region":162,"url":600,"description":601,"useCases":66,"indexable":258},"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":603,"label":604,"issuer":605,"region":162,"url":606,"description":607,"useCases":608,"indexable":258},"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":610,"label":611,"issuer":612,"region":241,"url":613,"description":614,"useCases":615,"indexable":258},"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":617,"label":618,"issuer":461,"region":162,"url":619,"description":620,"useCases":615,"indexable":258},"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":622,"label":623,"issuer":461,"region":162,"url":624,"description":625,"useCases":433,"indexable":258},"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":627,"label":628,"issuer":629,"region":630,"url":631,"description":632,"useCases":58,"indexable":258},"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":634,"label":635,"issuer":636,"region":162,"url":637,"description":638,"useCases":384,"indexable":258},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":640,"label":641,"issuer":642,"region":162,"url":643,"description":644,"useCases":384,"indexable":258},"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":646,"label":647,"issuer":648,"region":168,"url":649,"description":650,"useCases":65,"indexable":258},"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":652,"label":653,"issuer":461,"region":162,"url":654,"description":655,"useCases":65,"indexable":258},"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":657,"label":658,"issuer":659,"region":241,"url":660,"description":661,"useCases":65,"indexable":258},"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.",1790598302401]