[{"data":1,"prerenderedAt":589},["ShallowReactive",2],{"uc-sales-quote-and-estimate-generation":3,"uc-regulations":376},{"useCase":4,"evidence":181,"blitsAiDeployments":267,"benchmarks":268,"indicative":280,"related":283,"indexability":374,"includeUnpublished":187},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":23,"patterns":26,"channels":30,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":86,"feasibility":87,"implementation":100,"risk":141,"blitsAi":161,"faq":163,"related":173,"datePublished":176,"dateModified":176,"lastVerified":176,"changelog":177,"slug":180},"AI quote and estimate generation from customer requirements","Quote and estimate generation","AI quote generation and sales estimating","Draft quotes from drawings, item lists and roof photos. dida says Enpal cut solar quote work from 120 to 15 minutes; Microsoft says ODP bids take hours, not days.","published","AI that turns what a customer sends, such as a product list, a drawing, a roof photo or a request for quotation, into a draft quote: it reads the input, matches items to the catalog, calculates quantities and applies the organization's price rules, and hands the draft to a seller or estimator who checks and sends it.",[12,13,14,15,16,17],"AI quoting","AI estimating","automated quote generation","RFQ to quote automation","AI configure price quote","SKU matching for quotes",[19,20,21,22],"cross-industry","manufacturing","energy-and-utilities","retail-and-ecommerce",[24,25],"sales","product-and-pricing",[27,28,29],"document-processing","agentic-workflow","computer-vision",[31,32,33],"internal-tools","microsoft-teams","email","employee-facing","copilot","early-adopters","In many business to business and project sales, the quote is the bottleneck. A distributor\nreceives a customer's list of thousands of items in the customer's own descriptions and has to\nmatch each one to its own catalog. A solar installer has to measure a roof from a satellite image\nbefore it can say how many panels fit. A steel or building products supplier has to read\narchitectural drawings to know what to price. The work is skilled, slow and repetitive, so\ncustomers can wait days for an answer.\n\nManual quoting also produces errors: a miscounted roof, a wrong substitute product, a price rule\napplied inconsistently. dida, which built the roof assessment step of Enpal's quotes, describes Enpal's old process as\ntaking a salesperson 120 minutes per quote and as error prone, leading to inaccurate projections of\ncost and energy production.",[],"1. **Read the request.** The AI reads what the customer sent: an email, a spreadsheet of items,\n   a PDF drawing or an image of a roof or site, and extracts items, dimensions and quantities.\n2. **Match to the catalog.** Each item is matched to the organization's own products or\n   configurable options, with a confidence score and alternatives where no exact match exists.\n3. **Calculate.** Quantities, dimensions and technical constraints are calculated with\n   deterministic rules or models (for example the usable roof area and the number of panels),\n   not left to a language model's arithmetic.\n4. **Price.** List prices, customer contracts, discounts and margin rules are applied from the\n   pricing system, with anything outside the seller's authority flagged for approval.\n5. **Review and send.** The seller or estimator checks the draft, adjusts it, and sends the\n   quote from the CRM or quoting system; accepted and rejected quotes feed back into matching.",[41,42,43,44],"speed","revenue-growth","employee-productivity","risk-reduction",[46,47,48,49,50],"handling-time-reduction","processing-time-reduction","users-served","accuracy","conversion-rate-uplift",{"referenceOrg":52,"inputs":53,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"A distributor or installer that issues 10,000 custom quotes a year",[54,60,67,74],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"quotes","Custom quotes per year",10000,"quotes per year","The reference organization.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"minutesPerQuote","Staff time per quote today",60,120,"minutes per quote","Editorial assumption; dida reports 120 minutes per solar quote at Enpal before automation. Replace with a time study of your own quotes.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"reduction","Share of quoting time saved",0.4,0.8,"fraction of time","Conservative against the benchmark on this page (dida reports an 87.5% reduction at Enpal), because most catalogs are less uniform than solar roofs.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"hourlyCost","Fully loaded cost of a seller or estimator",40,70,"USD per hour","Editorial assumption, replace with your own cost.","quotes * minutesPerQuote / 60 * reduction * hourlyCost","USD","per year","Quoting time released","Time value only. It leaves out the build and integration cost, and the revenue effect of faster quotes, which Microsoft reports as 20% more sales opportunities a quarter at ODP but which depends on the market.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":95},"medium","Reading the input is usually the easier part; the difficulty is a clean product catalog with attributes good enough to match against, and a pricing system the AI can call instead of guessing prices.",[91,92,93,94],"A product catalog with structured attributes and approved substitutes","Price lists, customer contract prices and discount rules in a system of record","A set of past requests with the quotes that were sent, to measure matching accuracy","Technical rules for configuration and quantity calculation",[96,97,98,99],"CRM or configure, price, quote (CPQ) system","ERP or pricing engine for prices, stock and margins","Document and email intake for requests","Mapping or imaging services where the quote depends on a site",{"steps":101,"guardrails":117,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":128},[102,105,108,111,114],{"title":103,"detail":104},"Pick one quote type with volume","Start with a quote type that is frequent and fairly standard, such as a list of catalog items or a single product family, and measure the minutes and errors per quote today.",{"title":106,"detail":107},"Keep prices out of the model","Let the AI match and count, and let the pricing system price. Every price in the draft should come from a call to the system of record, never from generated text.",{"title":109,"detail":110},"Measure matching accuracy","Use past requests and the quotes actually sent as a test set, and report precision per product family before sellers rely on it.",{"title":112,"detail":113},"Show confidence and alternatives","Mark low confidence matches and unusual quantities so the seller checks those lines first, and keep the seller able to adjust anything.",{"title":115,"detail":116},"Learn from sent quotes","Feed the seller's corrections and the quote outcome back into matching and into the test set, and review the lines sellers change most often.",[118,119,120,121],"Prices, discounts and availability only from the pricing and ERP systems, never generated","Discounts or margins outside a seller's authority routed for approval","Human review of every quote before it is sent to a customer","Technical calculations done by deterministic rules or validated models, with the inputs shown","The seller or estimator reviews and sends every quote and owns the price. Pricing managers own the rules and approve exceptions, and product specialists maintain the catalog attributes and approved substitutes the matching relies on.",[124,125,126,127],"Minutes of staff time per quote and elapsed time from request to quote","Line level matching accuracy and the share of lines sellers change","Quote to order conversion, before and after","Quotes with pricing or quantity errors found after sending",[129,132,135,138],{"title":130,"detail":131},"Plausible but wrong matches","The AI picks a similar product that does not meet the customer's specification. Show confidence per line and test matching on past quotes.",{"title":133,"detail":134},"Invented prices","A language model fills in a price or discount it was never given. Keep pricing in the system of record and block generated prices.",{"title":136,"detail":137},"Garbage catalog in, garbage quote out","Matching fails because product attributes are incomplete. Fix the catalog data first; product content enrichment helps here.",{"title":139,"detail":140},"Speed without margin control","Faster quotes with inconsistent discounts erode margin. Enforce discount authority in the workflow, not in the prompt.",{"euAiAct":142,"regulations":145,"guidance":148,"controls":155,"incidents":160},{"tier":143,"basis":144},"minimal","Drafting business quotes for a seller to review is not an Annex III use and does not interact with the customer as an AI system. It would need a fresh assessment if the system set individual consumer prices or terms in areas such as credit or insurance, where Annex III point 5 can apply.",[146,147],"eu-ai-act","gdpr",[149],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"Algorithms: how they can reduce competition and harm consumers","UK Competition and Markets Authority","europe","https://www.gov.uk/government/publications/algorithms-how-they-can-reduce-competition-and-harm-consumers","Describes how pricing algorithms and personalized pricing can harm competition and consumers, relevant when quote tools also set prices.",[156,157,158,159],"Pricing rules and discount authority enforced in the quoting system, with an audit trail","A record of the AI draft and the seller's changes for every quote sent","Periodic review of matching accuracy and of quote errors reported by customers","Data protection review when quotes use images or data about a customer's home or site",[],{"howToBuild":162},"On Blits.ai this is an **agentic workflow**. A dialog flow on the **email channel** or\n**Microsoft Teams** receives the request and its attachment and triggers the workflow, which can\nalso start on a schedule or from an API token. An **AI agent** extracts the items with\n**structured output** and matches them to the catalog held in a **SQL knowledge base** or\nsearched through **hybrid retrieval**. **Custom functions** call the pricing engine, ERP or CRM\nthrough REST (SAP, Salesforce and NetSuite are in the integration catalog, and Microsoft\nDynamics 365 is a ready made business system tool) so that every price comes from the system of\nrecord, and the **file generation** tool returns the draft quote.\n\n**Human in the loop** confirmation above a configurable threshold holds large or unusual quotes\nfor the seller or a pricing manager, and the **tool execution policy** limits what the agent may\ncall. **Test suites** replay past requests against the quotes actually sent, the run history\nkeeps an audit trail per quote, and the platform is model agnostic.",[164,167,170],{"question":165,"answer":166},"Can AI produce a customer quote without a salesperson?","For simple, standard requests it can draft the whole quote. At Enpal the tool is used by Enpal staff and at ODP by sales representatives; DeAcero's public description does not say how its proposals are reviewed. The safer design keeps a seller reviewing every quote and takes prices from the pricing system, never from a language model.",{"question":168,"answer":169},"How much faster does AI make quoting?","It depends on the input. dida reports that Enpal's solar quote went from 120 minutes to 15, a reduction of 87.5%, and Microsoft reports that ODP Business Solutions now returns pricing bids in hours instead of one to two days.",{"question":171,"answer":172},"Is this the same as CPQ software?","It sits in front of it. Configure, price, quote systems hold the rules and prices; the AI reads unstructured customer requests and fills the quote, so sellers spend less time on data entry.",[174,175],"business-connectivity-quoting-and-service-assistant","product-content-and-catalog-enrichment","2026-09-27",[178],{"date":176,"note":179},"First published","sales-quote-and-estimate-generation",[182,210,232],{"title":183,"useCases":184,"organization":185,"vendors":190,"summary":194,"stage":195,"year":196,"channels":197,"languages":198,"metrics":199,"outcomeDisclosed":200,"sources":201,"verification":205,"grade":207,"id":208,"organizationSlug":209},"DeAcero: agents that turn architectural blueprints into cost proposals",[180],{"name":186,"anonymized":187,"country":188,"region":189,"industry":20},"DeAcero",false,"MX","latin-america",[191],{"name":192,"role":193},"Google Cloud","platform","DeAcero, a Mexican steel producer, built agents on Google's Gemini Enterprise Agent Platform with multimodal models that analyze architectural blueprints in PDF format and turn them into detailed cost proposals for customers. Google Cloud reports that this sharply cut DeAcero's response time to customers but gives no figure.","production",2026,[31],[],[],true,[202],{"url":203,"title":204,"publisher":192},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real-world gen AI use cases from the world's leading organizations",{"level":206,"checkedAt":176},"source-verified","C","deacero-blueprint-cost-proposal-agents",null,{"title":211,"useCases":212,"organization":213,"vendors":217,"summary":220,"stage":195,"year":221,"channels":222,"languages":223,"metrics":224,"outcomeDisclosed":200,"sources":225,"verification":230,"grade":207,"id":231,"organizationSlug":209},"The ODP Corporation: sales assistant that matches SKUs and drafts quotes for pricing bids",[180],{"name":214,"anonymized":187,"country":215,"region":216,"industry":22},"The ODP Corporation","US","north-america",[218],{"name":219,"role":193},"Microsoft","The ODP Corporation, parent of Office Depot and ODP Business Solutions, built a sales assistant on Azure OpenAI and Azure AI Search that lets representatives generate quotes in natural language. Its SKU matching cross references large product lists in minutes instead of days and produces quote ready summaries and tables; in one anecdote a representative cross referenced 4,000 competitor items. Microsoft reports that pricing bids now take hours instead of one to two days, that tailored quotes save representatives five to eight hours a week, and that the assistant drives 20% more sales opportunities a quarter.",2025,[31],[],[],[226],{"url":227,"title":228,"publisher":219,"date":229},"https://www.microsoft.com/en/customers/story/24030-the-odp-corporation-azure-ai-foundry","The ODP Corporation transforms HR, sales, and retail workflows with Azure AI app platform","2025-05-15",{"level":206,"checkedAt":176},"odp-corporation-sales-quote-assistant",{"title":233,"useCases":234,"organization":235,"vendors":238,"summary":243,"stage":195,"year":244,"channels":245,"languages":246,"metrics":247,"outcomeDisclosed":200,"sources":261,"verification":265,"grade":207,"id":266,"organizationSlug":209},"Enpal: machine learning roof analysis automates solar panel quotes",[180],{"name":236,"anonymized":187,"country":237,"region":152,"industry":21},"Enpal","DE",[239,242],{"name":240,"role":241},"dida","integrator",{"name":192,"role":193},"Enpal, a German solar energy company, worked with the AI firm dida to automate the roof assessment and panel sizing step of its solar quotes, which a salesperson had done by hand. A model trained on rooftop images from the Google Maps Platform detects the usable roof area and obstacles, projective geometry estimates the roof angle, and further steps calculate the number of panels and visualize their placement. dida says that during the six month build Enpal was able to manually adjust details such as the dimensions of a roof. The tool sizes the installation rather than setting prices. dida reports that the process now takes 15 minutes instead of 120 and that 150 Enpal employees use the tool.",2024,[31],[],[248,256],{"kpi":46,"value":249,"unit":250,"qualifier":251,"period":252,"claimant":253,"quote":254,"sourceUrl":255},87.5,"percent","exact","staff time per solar quote, from 120 to 15 minutes","vendor","Thanks to our solution, and the efficiency of building it with Google Cloud, what was once a manual process taking an Enpal salesperson 120 minutes to complete is now an automated process of just 15 minutes: a reduction of 87.5%.","https://cloud.google.com/blog/topics/customers/dida-creates-a-custom-ai-solution-with-google-cloud",{"kpi":48,"value":257,"unit":258,"qualifier":251,"period":259,"claimant":253,"quote":260,"sourceUrl":255},150,"count","Enpal employees using the tool, four years after launch","Four years on, this has risen to 150 Enpal employees, each saving 87.5% of their time, which they can now dedicate to other, more specialized tasks.",[262],{"url":255,"title":263,"publisher":192,"date":264},"How dida automates sales processes with mathematics and machine learning","2024-06-20",{"level":206,"checkedAt":176},"enpal-solar-quote-automation",0,[269,275],{"kpi":46,"label":270,"unit":250,"aggregate":200,"higherIsBetter":200,"n":271,"nUpTo":267,"median":249,"min":249,"max":249,"byClaimant":272,"vendorOnly":200,"points":273},"Handling time reduction",1,{"organization":267,"vendor":271,"regulator":267,"independent":267},[274],{"evidenceId":266,"organization":236,"value":249,"qualifier":251,"claimant":253,"grade":207,"pooled":200},{"kpi":48,"label":276,"unit":258,"aggregate":187,"higherIsBetter":200,"n":271,"nUpTo":267,"median":257,"min":257,"max":257,"byClaimant":277,"vendorOnly":200,"points":278},"Users served",{"organization":267,"vendor":271,"regulator":267,"independent":267},[279],{"evidenceId":266,"organization":236,"value":257,"qualifier":251,"claimant":253,"grade":207,"pooled":200},{"low":281,"high":282},160000,1120000,[284,313,334,352],{"slug":174,"title":285,"shortTitle":286,"definition":287,"status":9,"industries":288,"functions":290,"patterns":292,"audience":297,"autonomy":298,"adoptionStage":36,"segment":299,"evidenceCount":300,"publicEvidenceCount":300,"organizations":301,"bestGrade":307,"headline":308,"lastVerified":176,"indexable":200},"AI assistant for B2B telecom quoting, sales and service","B2B quoting and service","An AI assistant that serves business customers of a telecom operator and the sellers who look after them: it answers product, pricing and contract questions, prepares configurations and quotes for connectivity, mobile fleets and devices, drafts responses to tenders, and handles routine service requests and fault tickets, with a sales or service specialist approving anything binding.",[289],"telecommunications",[24,291,25],"customer-service",[293,294,28,295,296],"conversational-agent","rag-knowledge-assistant","recommendation-and-personalization","content-generation","customer-facing","supervised-agent","front-office",5,[302,303,304,305,306],"Lumen Technologies","SoftBank Corp.","Telefónica España","Verizon","Vodafone Business","B",{"kpi":309,"label":310,"unit":250,"n":271,"nUpTo":267,"kind":311,"value":78,"qualifier":251,"claimant":312,"organization":303,"vendorReported":187},"containment-rate","Containment rate","reported","organization",{"slug":175,"title":314,"shortTitle":315,"definition":316,"status":9,"industries":317,"functions":318,"patterns":321,"audience":323,"autonomy":298,"adoptionStage":324,"evidenceCount":325,"publicEvidenceCount":325,"organizations":326,"bestGrade":307,"headline":331,"lastVerified":176,"indexable":200},"AI product content and catalog enrichment for online retail","Product content and catalog enrichment","AI that writes and repairs product content at catalog scale: it drafts titles, descriptions and image alt text, and extracts missing attributes such as color, size and material from supplier text and product images, then checks its own output before the content is published to the store and to search engines. A human owns the rules, the quality thresholds and the exceptions.",[22,19],[319,320],"marketing","operations",[296,29,322],"classification-and-routing","back-office","mainstream",4,[327,328,329,330],"Amazon","eBay","Etsy","Walmart",{"kpi":332,"label":333,"unit":250,"n":271,"nUpTo":267,"kind":311,"value":77,"qualifier":251,"claimant":312,"organization":327,"vendorReported":187},"quality-score-uplift","Quality score uplift",{"slug":335,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":343,"patterns":344,"audience":297,"autonomy":346,"adoptionStage":36,"evidenceCount":325,"publicEvidenceCount":325,"organizations":347,"bestGrade":307,"headline":209,"lastVerified":176,"indexable":200},"branch-and-appointment-booking-agent","AI agent for branch finding and appointment booking","Branch and appointment booking","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[19,340,22,341,342],"banking","healthcare","real-estate",[291,24],[293,28,294,345],"voice-agent","autonomous",[348,349,350,351],"Bank of America","Best Buy","Hemominas","MOGUL.sg",{"slug":353,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":360,"patterns":362,"audience":297,"autonomy":298,"adoptionStage":36,"segment":299,"evidenceCount":363,"publicEvidenceCount":364,"organizations":365,"bestGrade":207,"headline":369,"lastVerified":176,"indexable":200},"digital-onboarding-assistant","AI assistant for digital account onboarding and KYC","Digital onboarding","A customer facing AI assistant that guides a new applicant, a person or a small merchant, through a digital account, card or relationship application: it collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[340,358,359],"payments","wealth-and-asset-management",[361,24,291],"onboarding-and-kyc",[293,27,29,28],6,3,[366,367,368],"Albo","Deutsche Bank","M-DAQ Global",{"kpi":370,"label":371,"unit":372,"n":271,"nUpTo":267,"kind":311,"value":373,"qualifier":251,"claimant":253,"organization":368,"vendorReported":200},"productivity-gain","Productivity gain","multiplier",30,{"indexable":200,"reasons":375},[],[377,383,388,396,403,409,416,423,431,438,445,451,458,465,471,476,483,489,495,501,507,513,519,524,529,536,543,548,553,560,566,572,578,583],{"id":146,"label":378,"issuer":379,"region":152,"url":380,"description":381,"useCases":382,"indexable":200},"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":147,"label":384,"issuer":379,"region":152,"url":385,"description":386,"useCases":387,"indexable":200},"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":389,"label":390,"issuer":391,"region":392,"url":393,"description":394,"useCases":395,"indexable":200},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":397,"label":398,"issuer":399,"region":216,"url":400,"description":401,"useCases":402,"indexable":200},"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":404,"label":405,"issuer":379,"region":152,"url":406,"description":407,"useCases":408,"indexable":200},"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":410,"label":411,"issuer":412,"region":152,"url":413,"description":414,"useCases":415,"indexable":200},"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":417,"label":418,"issuer":419,"region":152,"url":420,"description":421,"useCases":422,"indexable":200},"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":424,"label":425,"issuer":426,"region":427,"url":428,"description":429,"useCases":430,"indexable":200},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":432,"label":433,"issuer":434,"region":427,"url":435,"description":436,"useCases":437,"indexable":200},"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":439,"label":440,"issuer":441,"region":392,"url":442,"description":443,"useCases":444,"indexable":200},"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":446,"label":447,"issuer":448,"region":216,"url":449,"description":450,"useCases":444,"indexable":200},"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":452,"label":453,"issuer":454,"region":152,"url":455,"description":456,"useCases":457,"indexable":200},"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":459,"label":460,"issuer":461,"region":392,"url":462,"description":463,"useCases":464,"indexable":200},"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":466,"label":467,"issuer":379,"region":152,"url":468,"description":469,"useCases":470,"indexable":200},"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":472,"label":473,"issuer":379,"region":152,"url":474,"description":475,"useCases":470,"indexable":200},"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":477,"label":478,"issuer":479,"region":216,"url":480,"description":481,"useCases":482,"indexable":200},"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":484,"label":485,"issuer":379,"region":152,"url":486,"description":487,"useCases":488,"indexable":200},"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":490,"label":491,"issuer":492,"region":216,"url":493,"description":494,"useCases":488,"indexable":200},"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":496,"label":497,"issuer":498,"region":392,"url":499,"description":500,"useCases":488,"indexable":200},"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":502,"label":503,"issuer":379,"region":152,"url":504,"description":505,"useCases":506,"indexable":200},"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":508,"label":509,"issuer":510,"region":216,"url":511,"description":512,"useCases":506,"indexable":200},"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":514,"label":515,"issuer":426,"region":427,"url":516,"description":517,"useCases":518,"indexable":200},"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":520,"label":521,"issuer":379,"region":152,"url":522,"description":523,"useCases":518,"indexable":200},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":525,"label":526,"issuer":379,"region":152,"url":527,"description":528,"useCases":518,"indexable":200},"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":530,"label":531,"issuer":532,"region":152,"url":533,"description":534,"useCases":535,"indexable":200},"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":537,"label":538,"issuer":539,"region":216,"url":540,"description":541,"useCases":542,"indexable":200},"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":544,"label":545,"issuer":379,"region":152,"url":546,"description":547,"useCases":542,"indexable":200},"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":549,"label":550,"issuer":379,"region":152,"url":551,"description":552,"useCases":363,"indexable":200},"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":554,"label":555,"issuer":556,"region":557,"url":558,"description":559,"useCases":300,"indexable":200},"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":561,"label":562,"issuer":563,"region":152,"url":564,"description":565,"useCases":325,"indexable":200},"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":567,"label":568,"issuer":569,"region":152,"url":570,"description":571,"useCases":325,"indexable":200},"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":573,"label":574,"issuer":575,"region":427,"url":576,"description":577,"useCases":364,"indexable":200},"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":579,"label":580,"issuer":379,"region":152,"url":581,"description":582,"useCases":364,"indexable":200},"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":584,"label":585,"issuer":586,"region":216,"url":587,"description":588,"useCases":364,"indexable":200},"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.",1790598303422]