[{"data":1,"prerenderedAt":555},["ShallowReactive",2],{"uc-goal-based-financial-planning-assistant":3,"uc-regulations":349},{"useCase":4,"evidence":199,"blitsAiDeployments":257,"benchmarks":258,"indicative":259,"related":262,"indexability":347,"includeUnpublished":205},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":49,"macroEstimates":83,"feasibility":84,"implementation":97,"risk":140,"blitsAi":176,"faq":178,"related":188,"datePublished":194,"dateModified":194,"lastVerified":194,"changelog":195,"slug":198},"AI assistant for goal based financial planning","Goal based planning","AI assistants for goal based financial planning","AI assistants turn client goals into scenarios from a planning engine the firm can audit, explain trade offs plainly and prepare plans for advisor sign off.","published","An AI assistant that turns a client's goals into projections and what if scenarios using a rules based planning engine, explains the trade offs in plain language and prepares the plan for an advisor to validate, with every assumption disclosed and reproducible.",[12,13,14,15],"goal planning copilot","financial plan scenario assistant","retirement planning assistant for advisors","what if planning assistant",[17,18],"wealth-and-asset-management","banking",[20,21,22],"sales","customer-service","product-and-pricing",[24,25,26],"conversational-agent","content-generation","agentic-workflow",[28,29,30],"internal-tools","web-chat","mobile-app","employee-facing","copilot","emerging","front-office","Clients think in goals: retire at 60, pay for a child's university, buy a second home. Turning\nthose goals into a plan means collecting a lot of information, running projections across several\nscenarios and then explaining uncertainty in a way a client understands. If a firm measures how much\nof that effort is data gathering and document assembly, a full written plan can look costly to\nprovide for smaller client relationships.\n\nLanguage models are good at the conversation and the explanation, but they are not a reliable\nsource of exact arithmetic and they cannot see everything in a complex personal situation. The design question is how to use them for what they are good at\nwhile the numbers come from an engine the firm can audit.",[],"1. **Gather goals and facts.** A conversation, with the client or the advisor, captures goals,\n   timelines, income, assets, liabilities and constraints, and flags what is missing.\n2. **Run the engine.** A planning engine the firm can audit (rule based cash flow projections, or\n   Monte Carlo scenario models with recorded assumptions and a fixed seed) calculates the plan\n   with documented assumptions for returns, inflation and taxes.\n3. **Explore what ifs.** The client or advisor asks \"what if I retire two years later\" and the\n   assistant reruns the engine and compares the scenarios.\n4. **Explain in plain language.** The model narrates the results, the trade offs and the\n   uncertainty, quoting numbers only from the engine output.\n5. **Advisor validates.** The advisor reviews the plan, adjusts it for what the model cannot see,\n   and signs it off before it is presented as advice.",[39,40,41,42],"employee-productivity","inclusion-and-access","customer-experience","revenue-growth",[44,45,46,47,48],"time-saved-per-task","productivity-gain","users-served","customer-satisfaction","conversion-rate-uplift",{"referenceOrg":50,"inputs":51,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A wealth manager with 200 financial planners",[52,57,64,71],{"key":53,"label":54,"low":55,"high":55,"unit":53,"note":56},"planners","Financial planners",200,"The reference firm.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"plansPerYear","Plans or plan reviews per planner per year",50,100,"plans per planner per year","Editorial assumption, replace with your own planning volumes.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"hoursSaved","Hours saved per plan on data gathering, scenarios and write up",1,2,"hours per plan","Editorial assumption, replace with your own. No public source on this page states a time saving for AI assisted plans.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"hourlyCost","Fully loaded planner cost per hour",80,150,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","planners * plansPerYear * hoursSaved * hourlyCost","USD","per year","Value of planner time released per year","Leaves out the larger but less certain effect of serving clients who did not get a written plan before, the cost of the planning engine and the review time that remains.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"medium","If the firm already runs a planning engine, the work is connecting the conversation to it, keeping every number from the engine, and designing the advisor review so plans remain advice the firm stands behind.",[88,89,90,91],"A validated planning engine with documented capital market and tax assumptions","Client fact find data from CRM and account aggregation","Approved explanations of key concepts (risk, sequence of returns, inflation)","Disclosures and plan templates per market",[93,94,95,96],"Financial planning engine through an API","CRM and account aggregation","Document generation for the plan report","Advisor desktop or client portal",{"steps":98,"guardrails":114,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[99,102,105,108,111],{"title":100,"detail":101},"Keep the maths in the engine","Connect the assistant to the existing planning engine through an API and forbid the model from calculating projections itself. Every number in the narrative must come from an engine call.",{"title":103,"detail":104},"Start with advisors, not clients","Use the assistant to prepare fact finds, scenarios and plan drafts for advisors first. Client self service comes later, once explanations and refusals are proven.",{"title":106,"detail":107},"Write the explanation library","Agree plain language explanations of assumptions and uncertainty with compliance, so the model narrates within approved wording.",{"title":109,"detail":110},"Define what the assistant must hand over","Complex situations (business owners, cross border tax, estate structures, vulnerable clients) go to the advisor with a summary rather than an automated plan.",{"title":112,"detail":113},"Test scenario consistency","Build test cases where the right answer is known and check that the assistant calls the engine correctly, reports the numbers exactly and discloses the assumptions.",[115,116,117,118,119],"Numbers only from the planning engine, never generated by the language model","Every assumption disclosed and reproducible in the plan output","Projections labelled as illustrations, not promises","Handover of complex or vulnerable client situations to an advisor","Advisor sign off before a plan is presented as advice","The advisor reviews and signs off every plan, can override assumptions with a recorded reason, and owns the advice. The planning engine's assumptions are approved and reviewed periodically by an investment committee or equivalent.",[122,123,124,125,126],"Time from first conversation to signed off plan","Share of plans where the advisor changed numbers or assumptions, and why","Number of clients with a current written plan","Engine call errors and narrative number mismatches found in tests","Client satisfaction with plan explanations",[128,131,134,137],{"title":129,"detail":130},"Model does the maths","The narrative contains a number that no engine call produced. Enforce engine only numbers and check the output automatically.",{"title":132,"detail":133},"False precision","A single projected value is presented as a promise. Show ranges and scenarios with the assumptions.",{"title":135,"detail":136},"Complex cases forced through","A business owner or cross border case gets a generic plan. Detect complexity early and hand over.",{"title":138,"detail":139},"Guidance crossing into advice","A client facing version starts recommending products. Keep product recommendations in the advised process with suitability checks.",{"euAiAct":141,"regulations":144,"guidance":151,"controls":169,"incidents":175},{"tier":142,"basis":143},"context-dependent","Planning support for advisors is not listed in Annex III. A client facing version must disclose that the client is talking to AI (Article 50). It becomes high risk if it is used to assess the creditworthiness of individuals (Annex III point 5(b)) or for risk assessment and pricing of life or health insurance for individuals (Annex III point 5(c)).",[145,146,147,148,149,150],"eu-ai-act","gdpr","uk-consumer-duty","mas-ai-risk-management","iso-42001","mifid-ii",[152,158,164],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Harnessing AI in the Financial Planning Profession","CFP Board","north-america","https://www.cfp.net/-/media/files/cfp-board/knowledge/reports-and-research/harnessing-ai-in-the-financial-planning-profession-cfp-board-report.pdf","Scenario based report (October 2025) on how AI may change financial planning by 2030 and what planners should do now.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"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","Applies MiFID II conduct and organisational duties when AI supports investment advice, including transparency to clients about its use.",{"title":165,"issuer":166,"region":161,"url":167,"note":168},"PS22/9: A new Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/publication/policy/ps22-9.pdf","The policy statement sets rules for four outcomes under the Duty, including a consumer understanding outcome, which applies directly to how projections and uncertainty are explained.",[170,171,172,173,174],"Planning engine and its assumptions inventoried, validated and version controlled","Automated check that narrative numbers match engine output","Standard disclosures on every projection","Advisor sign off recorded for each plan","Periodic review of explanation wording by compliance",[],{"howToBuild":177},"On Blits.ai the conversation runs in an **AI agent** that gathers goals and facts, with a\n**flow** for the fixed parts of the fact find and **custom functions** that call the firm's\nplanning engine through its API, so every projection comes from the engine. A **knowledge base**\nholds the approved explanations of assumptions and concepts, and **structured output** keeps\nnumbers in separate fields that a custom function can compare with the engine response.\n\nThe advisor version runs in **Microsoft Teams** or inside internal tools through the **REST API\nchannel**; a client version can use the **web chat** channel, or the API channel inside the firm's\nown app, with **rich cards** and charts to compare scenarios. **Guardrails** stop\nproduct recommendations and hand complex cases to a human through **human handover**, and **test\nsuites** replay known scenarios on every change. **Multi language** support covers markets where\nclients plan in their own language.",[179,182,185],{"question":180,"answer":181},"Can a language model do financial projections?","It should not. Use it to gather information and explain results, and let a planning engine the firm can audit do the calculations, so every number is reproducible and auditable. Vanguard's Digital Advisor, for example, uses an algorithm to build and rebalance portfolios for a client's goals, and labels its projections and goal forecasts as hypothetical and not guarantees.",{"question":183,"answer":184},"Does AI replace the financial planner?","Not in the design on this page: the assistant prepares and explains, and the planner validates and signs off. Fully automated services exist, such as Vanguard's Digital Advisor; Vanguard's Personal Advisor offers ongoing financial planning and access to an advisor for those investing $50,000 or more. CIMB Niaga's agents are described as helping bank staff give tailored advice and proactive guidance.",{"question":186,"answer":187},"Is a client facing planning assistant regulated advice?","It depends on what it does. Explaining concepts and running projections is usually guidance. Recommending specific products to a client is investment advice under MiFID II and needs the full suitability process. ESMA's 2024 statement says MiFID II conduct duties still apply when firms use AI, and the firm stays responsible for the outcome.",[189,190,191,192,193],"suitability-assessment-assistant","next-best-action-for-advisors","financial-wellbeing-coach","portfolio-drift-monitoring-and-rebalancing","wealth-advisor-knowledge-assistant","2026-09-27",[196],{"date":194,"note":197},"First published","goal-based-financial-planning-assistant",[200,230],{"title":201,"useCases":202,"organization":203,"vendors":207,"summary":210,"stage":211,"year":212,"channels":213,"languages":214,"metrics":216,"outcomeDisclosed":205,"sources":217,"verification":225,"grade":227,"id":228,"organizationSlug":229},"Vanguard: Digital Advisor goal based portfolios with automatic rebalancing",[198,192,189],{"name":204,"anonymized":205,"country":206,"region":155,"industry":17},"Vanguard",false,"US",[208],{"name":204,"role":209},"in-house","Vanguard Digital Advisor is an all digital advice service that gathers a client's goals, time horizon and risk tolerance, uses an algorithm to build a portfolio for them, and keeps it on track with ongoing monitoring. It rebalances when a portfolio drifts more than 5% from the recommended allocation and adjusts holdings when a client adds goals. Vanguard discloses limits of the automated assessment, for example that it does not assess the suitability of selling existing holdings, and it labels its projections and goal forecasts as hypothetical and educational, not guarantees. The service was live by 2020: a Vanguard page archived in December 2020 refers to Digital Advisor clients who enrolled before 1 October 2020.","production",2020,[],[215],"en",[],[218,221],{"url":219,"title":220,"publisher":204},"https://investor.vanguard.com/advice/digital-advisor","Vanguard Digital Advisor",{"url":222,"title":223,"publisher":204,"date":224},"https://web.archive.org/web/20201231135303/https://investor.vanguard.com/advice/digital-advisor","Vanguard Digital Advisor (Wayback Machine capture of 31 December 2020)","2020-12-31",{"level":226,"checkedAt":194},"source-verified","B","vanguard-digital-advisor",null,{"title":231,"useCases":232,"organization":233,"vendors":237,"summary":244,"stage":211,"year":245,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":205,"sources":249,"verification":254,"grade":255,"id":256,"organizationSlug":229},"CIMB Niaga: AI agents that help staff give proactive, life stage guidance",[190,198],{"name":234,"anonymized":205,"country":235,"region":236,"industry":18},"CIMB Niaga","ID","asia-pacific",[238,241],{"name":239,"role":240},"Google Cloud","platform",{"name":242,"role":243},"Artefact","integrator","CIMB Niaga, one of Indonesia's largest banks, built purpose built AI agents with its AI Center of Excellence and Artefact on Google Cloud. The agents help bank staff offer tailored advice and proactive guidance matched to a customer's financial goals and life stage. No outcome figures were published.",2026,[28],[],[],[250],{"url":251,"title":252,"publisher":239,"date":253},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","1,302 real-world gen AI use cases from the world's leading organizations","2026-04-22",{"level":226,"checkedAt":194},"C","cimb-niaga-proactive-guidance-agents",0,[],{"low":260,"high":261},800000,6000000,[263,277,302,320,335],{"slug":189,"title":264,"shortTitle":265,"definition":266,"status":9,"industries":267,"functions":268,"patterns":271,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":68,"publicEvidenceCount":68,"organizations":273,"bestGrade":227,"headline":229,"lastVerified":275,"indexable":276},"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],[269,20,270],"regulatory-compliance","risk-management",[26,25,272],"classification-and-routing",[274,204],"Morgan Stanley","2026-09-26",true,{"slug":190,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":285,"audience":31,"autonomy":288,"adoptionStage":289,"segment":34,"evidenceCount":290,"publicEvidenceCount":290,"organizations":291,"bestGrade":227,"headline":295,"lastVerified":194,"indexable":276},"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],[20,283,284],"marketing","analytics-and-reporting",[286,287,25],"recommendation-and-personalization","prediction-and-scoring","assist","early-adopters",5,[234,292,293,274,294],"Citi","JPMorgan Chase","UBS",{"kpi":296,"label":297,"unit":298,"n":67,"nUpTo":257,"kind":299,"value":74,"qualifier":300,"claimant":301,"organization":294,"vendorReported":205},"employee-adoption","Employee adoption","percent","reported","exact","organization",{"slug":191,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":307,"patterns":308,"audience":309,"autonomy":310,"adoptionStage":289,"segment":34,"evidenceCount":311,"publicEvidenceCount":312,"organizations":313,"bestGrade":227,"headline":229,"lastVerified":194,"indexable":276},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[18],[21,283],[24,286,287,26],"customer-facing","supervised-agent",8,6,[314,315,316,317,318,319],"Bank of America","Commonwealth Bank of Australia","Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":192,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":325,"patterns":327,"audience":329,"autonomy":32,"adoptionStage":33,"segment":330,"evidenceCount":331,"publicEvidenceCount":332,"organizations":333,"bestGrade":227,"headline":229,"lastVerified":194,"indexable":276},"AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[17,18],[326,270,284],"operations",[328,26,287,25],"anomaly-detection","back-office","middle-office",4,3,[274,334,204],"SimCorp",{"slug":193,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":340,"patterns":342,"audience":31,"autonomy":288,"adoptionStage":344,"segment":34,"evidenceCount":312,"publicEvidenceCount":312,"organizations":345,"bestGrade":227,"headline":229,"lastVerified":275,"indexable":276},"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],[341,20,21],"knowledge-management",[343,24],"rag-knowledge-assistant","mainstream",[314,292,293,274,294,346],"Yes Bank",{"indexable":276,"reasons":348},[],[350,356,361,368,375,381,388,393,399,406,413,419,426,433,439,444,451,457,463,469,475,481,487,491,496,503,509,514,519,526,532,538,544,549],{"id":145,"label":351,"issuer":352,"region":161,"url":353,"description":354,"useCases":355,"indexable":276},"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":146,"label":357,"issuer":352,"region":161,"url":358,"description":359,"useCases":360,"indexable":276},"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":149,"label":362,"issuer":363,"region":364,"url":365,"description":366,"useCases":367,"indexable":276},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":369,"label":370,"issuer":371,"region":155,"url":372,"description":373,"useCases":374,"indexable":276},"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":376,"label":377,"issuer":352,"region":161,"url":378,"description":379,"useCases":380,"indexable":276},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":382,"label":383,"issuer":384,"region":161,"url":385,"description":386,"useCases":387,"indexable":276},"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":147,"label":389,"issuer":166,"region":161,"url":390,"description":391,"useCases":392,"indexable":276},"FCA Consumer Duty","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":148,"label":394,"issuer":395,"region":236,"url":396,"description":397,"useCases":398,"indexable":276},"MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":400,"label":401,"issuer":402,"region":236,"url":403,"description":404,"useCases":405,"indexable":276},"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":407,"label":408,"issuer":409,"region":364,"url":410,"description":411,"useCases":412,"indexable":276},"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":414,"label":415,"issuer":416,"region":155,"url":417,"description":418,"useCases":412,"indexable":276},"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":420,"label":421,"issuer":422,"region":161,"url":423,"description":424,"useCases":425,"indexable":276},"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":427,"label":428,"issuer":429,"region":364,"url":430,"description":431,"useCases":432,"indexable":276},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":434,"label":435,"issuer":352,"region":161,"url":436,"description":437,"useCases":438,"indexable":276},"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":440,"label":441,"issuer":352,"region":161,"url":442,"description":443,"useCases":438,"indexable":276},"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":445,"label":446,"issuer":447,"region":155,"url":448,"description":449,"useCases":450,"indexable":276},"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":452,"label":453,"issuer":352,"region":161,"url":454,"description":455,"useCases":456,"indexable":276},"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":458,"label":459,"issuer":460,"region":155,"url":461,"description":462,"useCases":456,"indexable":276},"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":464,"label":465,"issuer":466,"region":364,"url":467,"description":468,"useCases":456,"indexable":276},"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":470,"label":471,"issuer":352,"region":161,"url":472,"description":473,"useCases":474,"indexable":276},"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":476,"label":477,"issuer":478,"region":155,"url":479,"description":480,"useCases":474,"indexable":276},"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":482,"label":483,"issuer":395,"region":236,"url":484,"description":485,"useCases":486,"indexable":276},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":150,"label":488,"issuer":352,"region":161,"url":489,"description":490,"useCases":486,"indexable":276},"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":492,"label":493,"issuer":352,"region":161,"url":494,"description":495,"useCases":486,"indexable":276},"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":497,"label":498,"issuer":499,"region":161,"url":500,"description":501,"useCases":502,"indexable":276},"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":504,"label":505,"issuer":506,"region":155,"url":507,"description":508,"useCases":311,"indexable":276},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":510,"label":511,"issuer":352,"region":161,"url":512,"description":513,"useCases":311,"indexable":276},"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":515,"label":516,"issuer":352,"region":161,"url":517,"description":518,"useCases":312,"indexable":276},"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":520,"label":521,"issuer":522,"region":523,"url":524,"description":525,"useCases":290,"indexable":276},"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":527,"label":528,"issuer":529,"region":161,"url":530,"description":531,"useCases":331,"indexable":276},"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":533,"label":534,"issuer":535,"region":161,"url":536,"description":537,"useCases":331,"indexable":276},"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":539,"label":540,"issuer":541,"region":236,"url":542,"description":543,"useCases":332,"indexable":276},"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":545,"label":546,"issuer":352,"region":161,"url":547,"description":548,"useCases":332,"indexable":276},"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":550,"label":551,"issuer":552,"region":155,"url":553,"description":554,"useCases":332,"indexable":276},"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.",1790598296843]