[{"data":1,"prerenderedAt":618},["ShallowReactive",2],{"uc-treasury-team-assistant":3,"uc-regulations":400},{"useCase":4,"evidence":185,"blitsAiDeployments":285,"benchmarks":286,"indicative":287,"related":290,"indexability":398,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":26,"channels":29,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":46,"indicativeValue":50,"macroEstimates":90,"feasibility":91,"implementation":103,"risk":141,"blitsAi":163,"faq":165,"related":175,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI assistant for corporate treasury teams","Treasury team assistant","AI treasury assistant for cash and policy","Treasury teams ask an AI assistant about live cash and policy. Bandwidth queries bank data in plain language; WPP built a Treasurer Bot on its own treasury policies.","published","An AI treasury assistant answers a corporate treasury team's questions in plain language, from live cash and bank data to internal policy, instead of staff stitching exports together.",[12,13,14,15,16],"treasury chatbot","treasurer bot","treasury copilot","conversational treasury analytics","AI for treasury reporting",[18,19,20,21],"cross-industry","technology","telecommunications","professional-services",[23,24,25],"treasury","knowledge-management","analytics-and-reporting",[27,28],"conversational-agent","rag-knowledge-assistant",[30,31],"internal-tools","microsoft-teams","employee-facing","assist","early-adopters","A corporate treasury often sees its cash through several bank portals with separate logins, an\nERP, a treasury management system and spreadsheets. Atlar's\ncustomer story on Bandwidth describes cash positions spread across multiple bank portals, so a\nfull picture meant working through each portal one at a time. The AFP's summary of ServiceNow's submission says its treasury\nspent 40 to 60 hours each week collecting and reconciling data from disconnected systems, banks and\nentities as the company expanded across more than 50 countries.\n\nQuestions from the rest of the business add load. Microsoft's Worldwide Payment Solutions team\nreceived too many emails about financing programs, tools and policies that needed a manual answer.\nWPP's group treasury set out to give its global team self service access to treasury policy,\ndelegated authority limits and how to guides.\n\nAn assistant sits on top of the data and documents treasury already owns, so staff ask a question\nand get an answer from the live source.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"The AFP's summary of ServiceNow's 2026 Pinnacle Awards submission says its treasury spent 40 to 60 hours each week manually collecting and reconciling data from disconnected systems, banks and entities.","Finalists Named for the AFP 2026 Pinnacle Awards: Bandwidth, Google, ServiceNow","https:\u002F\u002Fwww.financialprofessionals.org\u002Fabout\u002Flearn-more\u002Fpress-releases\u002FDetails\u002Ffinalists-named-for-the-afp-2026-pinnacle-awards-bandwidth-google-servicenow",2026,"1. **Connect the sources.** Bank balances and transactions come in through a multibank connection\n   or a treasury data layer. Bandwidth uses Atlar's bank connectivity, and ServiceNow first\n   integrated more than 10 source systems into one place. Policy answers need the treasury policy,\n   reporting and committee papers, as in WPP's Treasurer Bot.\n2. **Keep the user's permissions.** Atlar's own post on its Claude connector says Claude queries\n   live treasury data the same way a user inside Atlar would, with the same permissions, and that\n   every query is logged in Atlar's audit trail. That describes Atlar's connector in general, not\n   Bandwidth's deployment. Whatever the build, the assistant should inherit each user's access.\n3. **Ask in plain language.** A treasurer asks how much sits in euros, what cleared overnight or\n   where the company stands this morning. The assistant turns the question into a query on live\n   data and answers from it, not from a report prepared in advance.\n4. **Answer policy and stakeholder questions.** The same kind of assistant answers from policy\n   documents. WPP's team uses it to look up policy and validate proposed actions, and Microsoft's\n   bot, Leonard, answers business questions about financing programs in Microsoft Teams.\n5. **Hand over actions.** Most cited assistants answer and explain. Microsoft's Leonard was also\n   trained to cancel financing requests in Microsoft's approval workflow. Moving money, sweeping or\n   hedging should remain a separate, approved step, and read only access is the safer starting point.",[44,45],"employee-productivity","speed",[47,48,49],"hours-saved","search-time-reduction","productivity-gain",{"referenceOrg":51,"inputs":52,"formula":85,"currency":86,"period":87,"resultLabel":88,"caveat":89},"A treasury team of six with relationships at about fifteen banks",[53,59,66,71,78],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"teamSize","Treasury staff who pull cash and policy information",6,"people","The reference team.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65,"sourceUrl":40},"hoursPerWeek","Hours per person per week spent gathering balances, stitching exports and finding policy",4,10,"hours per person per week","Editorial assumption, replace with your own time study. The AFP's summary of ServiceNow's submission cites 40 to 60 hours a week for the whole team before it centralised its data.",{"key":67,"label":68,"low":69,"high":69,"unit":67,"note":70},"weeks","Working weeks per year",46,"Editorial assumption.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"share","Share of that time the assistant removes",0.15,0.35,"fraction of time","Editorial assumption. Bandwidth's treasurer says the approach saves hours every week without giving a number.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"hourlyCost","Loaded cost of a treasury hour",60,100,"USD per hour","Editorial assumption, replace with your own loaded cost.","teamSize * hoursPerWeek * weeks * share * hourlyCost","USD","per year","Treasury time released","Leaves out the cost of the data connectivity and the assistant, and the value of faster decisions on funding and idle balances. The time share depends on whether the data is already centralised, which is the larger part of the work in the published cases.",[],{"complexity":92,"complexityNote":93,"dataPrerequisites":94,"integrations":98},"medium","The build needs bank connectivity or a treasury data layer with permissions that carry through to the assistant, a governed query path over cash data, and a cleaned set of treasury policy documents. ServiceNow built its central data architecture before it added AI. A policy only assistant, such as Microsoft's FAQ bot, needs far less.",[95,96,97],"Bank balances and transactions in one queryable store, with account and entity metadata","Treasury policy, delegated authority limits and how to guides in current, versioned documents","A list of the questions stakeholders ask most often, taken from email and chat history",[99,100,101,102],"Multibank connectivity or a treasury management system","ERP for payables, receivables and forecasts","Identity provider, so the assistant inherits each user's access","Microsoft Teams or the treasury portal as the place staff ask",{"steps":104,"guardrails":120,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":131},[105,108,111,114,117],{"title":106,"detail":107},"List the questions","Collect two months of questions the team and its stakeholders ask, split into data questions (balances, flows) and policy questions (limits, procedures). Microsoft's bot started from an Excel FAQ library.",{"title":109,"detail":110},"Centralise the data first","Put balances and transactions from all banks and entities in one governed store before adding the assistant. ServiceNow did this before it added a chatbot.",{"title":112,"detail":113},"Start read only","Release an assistant that answers and shows the query or document behind the answer. Do not connect it to payment or trade initiation.",{"title":115,"detail":116},"Test on known questions","Build a test set from the question list with answers checked by treasury, and run it after every change to data, prompts or model.",{"title":118,"detail":119},"Extend with approvals","Add any action, such as preparing a payment batch or a sweep proposal, only as a draft for a named approver.",[121,122,123,124],"The assistant sees only the accounts and documents the asking user may see","Every answer from data shows the query or source behind it, and every query is logged","No payment, sweep or trade is initiated by the assistant","Policy answers cite the current policy version and say when no document covers the question","Treasury staff use the answers as input and remain accountable for decisions. Questions the assistant cannot answer, or where it finds no policy, go to a named treasury owner. The policy owner reviews the answers the bot gives on policy at a set interval.",[127,128,129,130],"Hours per week the team spends gathering data and answering stakeholder questions, before and after","Share of questions answered correctly on the test set, and the number escalated to a person","Active users per month and questions per user","Time from question to answer for the ten most common cash questions",[132,135,138],{"title":133,"detail":134},"Fragmented data behind a fluent answer","Accounts at some banks are missing, and the answer reads as complete. Show coverage, list the accounts included and warn when material accounts are absent.",{"title":136,"detail":137},"Outdated policy","The assistant answers from an old limit or procedure. Version the documents, retire old ones and show the version date in the answer.",{"title":139,"detail":140},"Leaking access","The assistant retrieves data the asking user may not see. Pass the user's identity through and test with accounts of different permission levels.",{"euAiAct":142,"regulations":145,"guidance":150,"controls":157,"incidents":162},{"tier":143,"basis":144},"limited","A treasury assistant interacts directly with staff, so Article 50(1) requires that they are informed they are dealing with an AI system unless that is obvious from the context. It is not listed in Annex III and makes no decision about a natural person.",[146,147,148,149],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001",[151],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","A structure to map and manage accuracy, access and misuse risks of an internal assistant on sensitive financial data.",[158,159,160,161],"Role based access that applies to the assistant as it does to the user","Audit log of questions, queries and answers","Model inventory entry with an owner, test results and a change process","Review of the policy documents the assistant relies on, with named owners",[],{"howToBuild":164},"On Blits.ai a treasury assistant is an **AI agent** with two kinds of knowledge. A **SQL\nknowledge base** over the governed cash and transaction tables answers data questions, with\n**custom functions** for calls into a treasury management system or bank connectivity API.\nThe **knowledge base with hybrid retrieval** answers policy questions from the treasury policy,\nreporting and committee papers. The team reaches it in **Microsoft Teams** or through the **API\nchannel** in an internal portal.\n\n**Guardrails** keep the agent to treasury topics and **PII masking** protects personal data in\ndocuments. Anything beyond reading, such as a payment batch, runs as an **agentic workflow** with\n**human in the loop approval**, and unanswered questions go through **human handover** to a\ntreasury colleague. **Test suites** run the known question set after each change, **monitors**\ncheck the assistant on a schedule, and **analytics** show which questions arrive. Models are\nselectable per agent, and the platform runs in EU or UAE regions for data residency.",[166,169,172],{"question":167,"answer":168},"What can a treasury assistant answer today?","Bandwidth's treasury asks how much sits in euros, what cleared overnight and where the company stands this morning, and gets answers from live bank data. WPP's Treasurer Bot answers from treasury policy, core reporting and past committee papers. Microsoft's Leonard answers business questions about financing programs, tools and policies.",{"question":170,"answer":171},"Does it save measurable time?","The published cases give no figures. Bandwidth's treasurer says it saves hours every week, and Microsoft lists time savings among the benefits of Leonard. ServiceNow's 40 to 60 weekly hours of manual data work is the baseline it started from, not a result.",{"question":173,"answer":174},"Should an assistant be able to move money?","None of the cited deployments describes it doing so. A safer pattern is to keep the assistant read only and have it draft any payment or sweep for a named approver.",[176,177,178,179],"treasury-cash-flow-forecasting","corporate-client-servicing-assistant","governed-text-to-sql-analytics","enterprise-knowledge-search","2026-10-13",[182],{"date":180,"note":183},"First published","treasury-team-assistant",[186,221,238,264],{"title":187,"useCases":188,"organization":189,"vendors":194,"summary":198,"stage":199,"year":41,"channels":200,"languages":201,"metrics":203,"outcomeDisclosed":204,"sources":205,"verification":216,"grade":218,"id":219,"organizationSlug":220},"Bandwidth: natural language questions on live cash data with Atlar",[184],{"name":190,"anonymized":191,"country":192,"region":154,"industry":20,"wikidata":193},"Bandwidth",false,"US","Q4854759",[195],{"name":196,"role":197},"Atlar","platform","Bandwidth, a Nasdaq listed cloud communications provider, kept cash positions in several bank portals with separate logins, so a full picture meant working through each portal in turn. With Atlar's bank connectivity, its treasury team asks questions in plain language, for example how much sits in euros or what cleared overnight, and gets answers from live data instead of a prepared report. The AFP lists the project as a 2026 Pinnacle Awards finalist and says the team built its treasury platform using Atlar's bank connectivity and Claude. The treasurer says it saves hours every week.","production",[30],[202],"en",[],true,[206,210,213],{"url":207,"title":208,"publisher":196,"date":209},"https:\u002F\u002Fwww.atlar.com\u002Fblog\u002Fbandwidth-partners-with-atlar-to-query-data-in-real-time","Bandwidth Partners with Atlar to Query Data in Real Time","2026-07-14",{"url":40,"title":39,"publisher":211,"date":212},"Association for Financial Professionals (AFP)","2026-09-09",{"url":214,"title":215,"publisher":196},"https:\u002F\u002Fwww.atlar.com\u002Fblog\u002Fbring-atlar-into-claude","Bring Atlar into Claude",{"level":217,"checkedAt":180},"source-verified","C","bandwidth-atlar-treasury-cash-queries",null,{"title":222,"useCases":223,"organization":224,"vendors":227,"summary":230,"stage":199,"year":41,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":191,"sources":234,"verification":236,"grade":218,"id":237,"organizationSlug":220},"ServiceNow: treasury data foundation, chatbot and machine learning forecasting",[184],{"name":225,"anonymized":191,"country":192,"region":154,"industry":19,"wikidata":226},"ServiceNow","Q7455653",[228],{"name":225,"role":229},"in-house","As ServiceNow grew across more than 50 countries, its treasury team spent 40 to 60 hours a week collecting and reconciling data from disconnected systems, banks and entities. It first built a central treasury data architecture over more than 10 source systems, then added real time dashboards, a treasury chatbot, machine learning cash forecasting, automated FX hedging across more than 40 currency pairs and AI powered counterparty exposure monitoring. The AFP names the project as a finalist for its 2026 Pinnacle Awards. No result figures are published.",[30],[202],[],[235],{"url":40,"title":39,"publisher":211,"date":212},{"level":217,"checkedAt":180},"servicenow-treasury-data-and-chatbot",{"title":239,"useCases":240,"organization":241,"vendors":246,"summary":248,"stage":199,"year":249,"channels":250,"languages":251,"metrics":252,"outcomeDisclosed":191,"sources":253,"verification":262,"grade":218,"id":263,"organizationSlug":220},"WPP: Treasurer Bot for the global treasury team",[184],{"name":242,"anonymized":191,"country":243,"region":244,"industry":21,"wikidata":245},"WPP","GB","europe","Q1318049",[247],{"name":242,"role":229},"WPP's group treasury built a Treasurer Bot on the company's own AI environment. Its knowledge comes from WPP's treasury practices and policies, core reporting and past treasury committee papers, and the global treasury team uses it to look up policy, validate proposed actions and onboard new team members. WPP plans a lighter version for finance teams that manage FX risk in the agencies. It won the Judges' Choice award at the Adam Smith Awards 2025. No result figures are published.",2025,[30],[202],[],[254,258],{"url":255,"title":256,"publisher":257},"https:\u002F\u002Ftreasurytoday.com\u002Fasa-2025-winners\u002Ftreasurer-bot-takes-up-key-role-at-advertising-giant\u002F","Treasurer Bot takes up key role at advertising giant","Treasury Today (Adam Smith Awards 2025)",{"url":259,"title":260,"publisher":261},"https:\u002F\u002Ftreasurytoday.com\u002Finsight-and-analysis\u002Fadam-smith-awards-winners-announced-recognising-corporate-treasury-innovation-and-best-practice\u002F","Adam Smith Awards winners announced, recognising corporate treasury innovation and best practice","Treasury Today",{"level":217,"checkedAt":180},"wpp-treasurer-bot",{"title":265,"useCases":266,"organization":267,"vendors":270,"summary":272,"stage":199,"year":273,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":191,"sources":277,"verification":282,"grade":218,"id":283,"organizationSlug":284},"Microsoft Treasury: Financing Assistant chatbot (Leonard)",[184],{"name":268,"anonymized":191,"country":192,"region":154,"industry":19,"wikidata":269},"Microsoft","Q2283",[271],{"name":268,"role":229},"Microsoft's Worldwide Payment Solutions team received many emails from the business that needed a manual answer about financing programs, internal tools and policies. A finance analyst turned an Excel FAQ library into a Q&A chatbot on Azure Bot Service, deployed in Microsoft Teams within weeks. The bot, named Leonard, uses natural language processing with active learning, and was later connected to the financing approval workflow so users can check or cancel requests. Microsoft reported time savings without figures; the write up won the Adam Smith Awards 2018 Judges' Choice.",2018,[31,30],[202],[],[278],{"url":279,"title":280,"publisher":281},"https:\u002F\u002Ftreasurytoday.com\u002Fasa-2018-winners\u002Fjudges-choice-winner-microsoft-financing-assistant-bot\u002F","Judges' Choice Winner: Microsoft Financing Assistant Bot","Treasury Today (Adam Smith Awards 2018)",{"level":217,"checkedAt":180},"microsoft-financing-assistant-leonard","microsoft",0,[],{"low":288,"high":289},9936,96599.99999999999,[291,327,352,370],{"slug":176,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":300,"patterns":302,"audience":32,"autonomy":33,"adoptionStage":34,"segment":306,"evidenceCount":307,"publicEvidenceCount":307,"organizations":308,"bestGrade":316,"headline":317,"lastVerified":326,"indexable":204},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","AI cash flow forecasting for corporate treasury is machine learning that categorises a company's cash flows and forecasts its cash positions across accounts and currencies for the treasury team. Conversational analytics can sit on top of it. Some banks offer it inside their cash management platforms, where it also answers treasurers' questions in plain language, so the team decides on funding and idle balances with better information and less spreadsheet work.",[296,18,297,298,299],"banking","logistics-and-transportation","retail-and-ecommerce","manufacturing",[23,301,25],"finance-and-accounting",[303,304,27,305],"prediction-and-scoring","classification-and-routing","agentic-workflow","specialized-businesses",7,[309,310,311,312,313,314,315],"Amtrak","Ant International","Bank of America","Capital A Berhad (AirAsia Group)","Domino's Pizza","JPMorgan Chase","Prysmian","B",{"kpi":49,"label":318,"unit":319,"n":320,"nUpTo":321,"kind":322,"value":323,"qualifier":324,"claimant":325,"organization":314,"vendorReported":191},"Productivity gain","percent",2,1,"reported",90,"approximately","organization","2026-09-27",{"slug":177,"title":328,"shortTitle":329,"definition":330,"status":9,"industries":331,"functions":333,"patterns":336,"audience":338,"autonomy":339,"adoptionStage":34,"segment":306,"evidenceCount":340,"publicEvidenceCount":341,"organizations":342,"bestGrade":316,"headline":347,"lastVerified":326,"indexable":204},"AI assistant for corporate and commercial client servicing","Corporate client servicing","An AI corporate client servicing assistant answers finance and treasury teams' questions inside the corporate banking portal, app and messaging channels, and resolves routine requests end to end. Questions include payment status, balances, cut off times, fees and how to submit an instruction, and anything it cannot finish is handed to a service specialist who has an AI copilot.",[296,332],"payments",[334,335],"customer-service","operations",[27,28,305,337],"summarization","customer-facing","supervised-agent",8,5,[311,343,344,345,346],"China Merchants Bank","Citi","DBS Bank","HSBC",{"kpi":348,"label":349,"unit":319,"n":321,"nUpTo":285,"kind":322,"value":350,"qualifier":351,"claimant":325,"organization":311,"vendorReported":191},"contact-deflection","Contact deflection",16,"exact",{"slug":178,"title":353,"shortTitle":354,"definition":355,"status":9,"industries":356,"functions":359,"patterns":361,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":56,"publicEvidenceCount":56,"organizations":363,"bestGrade":316,"headline":220,"lastVerified":326,"indexable":204},"Governed text to SQL analytics assistant","Governed SQL analytics","A governed text to SQL analytics assistant turns a business user's plain language question into a query against governed data. It runs the query under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[18,296,357,298,19,358],"insurance","pharma-and-life-sciences",[25,360],"it-and-engineering",[27,362,28],"code-generation",[364,365,366,367,368,369],"Bayer","CWT","LinkedIn","National Renewable Energy Laboratory","Uber Technologies","United Rentals",{"slug":179,"title":371,"shortTitle":372,"definition":373,"status":9,"industries":374,"functions":377,"patterns":378,"audience":32,"autonomy":33,"adoptionStage":379,"evidenceCount":380,"publicEvidenceCount":380,"organizations":381,"bestGrade":316,"headline":394,"lastVerified":326,"indexable":204},"AI enterprise knowledge search for employees","Enterprise knowledge search","Enterprise knowledge search is an AI assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own documents. It draws the answer from policies, procedures, product manuals and research, with citations to the source documents, and only from documents the employee is allowed to see.",[18,296,375,357,376,21],"wealth-and-asset-management","government",[24,335,334],[28,27,337],"mainstream",13,[382,311,383,384,385,386,387,388,389,390,391,392,393],"All Nippon Airways","Booking.com","Cathay Financial Holdings","DZ BANK AG","Insurance Australia Group","Morgan Stanley","PLDT","Raiffeisen Bank International AG","SIGNAL IDUNA","Wells Fargo","Zain Jordan","Zillow",{"kpi":395,"label":396,"unit":319,"n":320,"nUpTo":285,"kind":322,"value":323,"qualifier":397,"claimant":325,"organization":392,"vendorReported":191},"employee-adoption","Employee adoption","at-least",{"indexable":204,"reasons":399},[],[401,407,412,419,422,429,435,442,450,457,464,470,476,483,490,497,503,509,515,520,525,532,538,545,550,555,560,565,571,576,583,588,594,600,607,612],{"id":146,"label":402,"issuer":403,"region":244,"url":404,"description":405,"useCases":406,"indexable":204},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":147,"label":408,"issuer":403,"region":244,"url":409,"description":410,"useCases":411,"indexable":204},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":149,"label":413,"issuer":414,"region":415,"url":416,"description":417,"useCases":418,"indexable":204},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":148,"label":152,"issuer":153,"region":154,"url":155,"description":420,"useCases":421,"indexable":204},"Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":423,"label":424,"issuer":425,"region":244,"url":426,"description":427,"useCases":428,"indexable":204},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":430,"label":431,"issuer":403,"region":244,"url":432,"description":433,"useCases":434,"indexable":204},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":436,"label":437,"issuer":438,"region":244,"url":439,"description":440,"useCases":441,"indexable":204},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":443,"label":444,"issuer":445,"region":446,"url":447,"description":448,"useCases":449,"indexable":204},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":451,"label":452,"issuer":453,"region":446,"url":454,"description":455,"useCases":456,"indexable":204},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":458,"label":459,"issuer":460,"region":154,"url":461,"description":462,"useCases":463,"indexable":204},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":465,"label":466,"issuer":403,"region":244,"url":467,"description":468,"useCases":469,"indexable":204},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":471,"label":472,"issuer":473,"region":415,"url":474,"description":475,"useCases":469,"indexable":204},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":477,"label":478,"issuer":479,"region":244,"url":480,"description":481,"useCases":482,"indexable":204},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":484,"label":485,"issuer":486,"region":154,"url":487,"description":488,"useCases":489,"indexable":204},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":491,"label":492,"issuer":493,"region":415,"url":494,"description":495,"useCases":496,"indexable":204},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":498,"label":499,"issuer":403,"region":244,"url":500,"description":501,"useCases":502,"indexable":204},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":504,"label":505,"issuer":506,"region":154,"url":507,"description":508,"useCases":502,"indexable":204},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":510,"label":511,"issuer":512,"region":154,"url":513,"description":514,"useCases":380,"indexable":204},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",{"id":516,"label":517,"issuer":403,"region":244,"url":518,"description":519,"useCases":380,"indexable":204},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":521,"label":522,"issuer":403,"region":244,"url":523,"description":524,"useCases":380,"indexable":204},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":526,"label":527,"issuer":528,"region":154,"url":529,"description":530,"useCases":531,"indexable":204},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":533,"label":534,"issuer":535,"region":415,"url":536,"description":537,"useCases":531,"indexable":204},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":539,"label":540,"issuer":541,"region":244,"url":542,"description":543,"useCases":544,"indexable":204},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":546,"label":547,"issuer":403,"region":244,"url":548,"description":549,"useCases":544,"indexable":204},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":551,"label":552,"issuer":403,"region":244,"url":553,"description":554,"useCases":544,"indexable":204},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":556,"label":557,"issuer":445,"region":446,"url":558,"description":559,"useCases":63,"indexable":204},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":561,"label":562,"issuer":403,"region":244,"url":563,"description":564,"useCases":63,"indexable":204},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":566,"label":567,"issuer":568,"region":154,"url":569,"description":570,"useCases":340,"indexable":204},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":572,"label":573,"issuer":403,"region":244,"url":574,"description":575,"useCases":307,"indexable":204},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":577,"label":578,"issuer":579,"region":580,"url":581,"description":582,"useCases":341,"indexable":204},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":584,"label":585,"issuer":403,"region":244,"url":586,"description":587,"useCases":62,"indexable":204},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":589,"label":590,"issuer":591,"region":244,"url":592,"description":593,"useCases":62,"indexable":204},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":595,"label":596,"issuer":597,"region":244,"url":598,"description":599,"useCases":62,"indexable":204},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":601,"label":602,"issuer":603,"region":446,"url":604,"description":605,"useCases":606,"indexable":204},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":608,"label":609,"issuer":403,"region":244,"url":610,"description":611,"useCases":606,"indexable":204},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":613,"label":614,"issuer":615,"region":154,"url":616,"description":617,"useCases":606,"indexable":204},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904354812]