[{"data":1,"prerenderedAt":632},["ShallowReactive",2],{"uc-developer-api-integration-assistant":3,"uc-regulations":428},{"useCase":4,"evidence":185,"blitsAiDeployments":303,"benchmarks":304,"indicative":332,"related":335,"indexability":426,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":21,"patterns":25,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":99,"risk":134,"blitsAi":162,"faq":164,"related":174,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI assistant for developers integrating a company's APIs","API integration assistant","AI assistant for developer portals and API docs","AI assistants on developer portals answer API questions, suggest endpoints and generate sample calls. Mapbox reports 30% fewer monthly support tickets with one.","published","An AI assistant on a developer portal and in its documentation that answers integration questions, recommends the right endpoints, helps debug connections and generates sample calls, grounded in the API catalogue, reference docs and test material, so clients and partners integrate faster with fewer support tickets.",[12,13,14,15],"developer portal assistant","API documentation chatbot","ask AI for docs","integration support copilot",[17,18,19,20],"cross-industry","banking","payments","technology",[22,23,24],"it-and-engineering","customer-service","onboarding-and-kyc",[26,27,28],"rag-knowledge-assistant","conversational-agent","code-generation",[30,31,32],"web-chat","api","agent-desktop","customer-facing","assist","early-adopters","specialized-businesses","Every company that sells through APIs, from a bank embedding payments and treasury services in a\nclient's ERP to a software platform, depends on outside developers getting their integration to\nwork. Those developers search long reference docs, try calls in a sandbox, hit an error and file a\nticket, then wait. Many of those questions already have an answer somewhere in the documentation\nor in a past ticket. The Mapbox case study on this page notes that by the time a ticket was filed,\nthe answer usually already existed in the documentation.\n\nFor a bank, slow integration delays the start of transaction revenue and ties up implementation\nmanagers and support engineers on repetitive questions. The assistant pattern already runs in\nproduction at software companies such as Mapbox, CircleCI and monday.com; the banking specific part is keeping it strictly away from production data,\nlive credentials and client entitlements.",[],"1. **Index the developer knowledge.** API reference, guides, SDKs, changelogs, sample code, test\n   scripts and resolved support tickets are indexed and refreshed as they change.\n2. **Answer in context.** In the docs, the portal or the sandbox, the assistant answers questions\n   with citations to the exact page or endpoint.\n3. **Recommend and generate.** It suggests which endpoints fit the use case and generates sample\n   requests and code in the developer's language, using sandbox values only.\n4. **Help debug.** Given an error message or a failing request (with secrets removed), it explains\n   the likely cause and the fix.\n5. **Escalate.** Anything it cannot answer, and anything touching production access or\n   entitlements, goes to a support engineer or implementation manager with the conversation.",[41,42,43,44],"customer-experience","cost-to-serve","speed","revenue-growth",[46,47,48,49,50,51],"contact-deflection","response-time-reduction","hours-saved","interactions-handled","containment-rate","processing-time-reduction",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A bank running 400 client and partner API integrations a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"integrations","Client and partner integrations per year",400,"integrations per year","The reference bank.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"supportHours","Support and implementation hours per integration",20,40,"hours per integration","Editorial assumption, replace with your own ticket and implementation data.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"deflection","Share of those hours the assistant saves",0.15,0.25,"fraction of hours","Kept below the 30% reduction in monthly support tickets from paid users that Mapbox reports on this page, because that figure covers tickets only and implementation hours usually fall less than ticket volume. Start from the low end unless your own data says otherwise.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyCost","Loaded cost of a support or implementation engineer hour",90,130,"USD per hour","Editorial assumption, replace with your own loaded cost.","integrations * supportHours * deflection * hourlyCost","USD","per year","Support and implementation time released, valued at loaded cost","Values engineering time only. It leaves out the revenue from integrations that go live sooner, the cost of the assistant, and the documentation improvements that its unanswered questions reveal.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":95},"low","Mostly a retrieval assistant over public or partner facing documentation. The effort is in keeping the index current, adding the sandbox and ticket knowledge, and enforcing the boundary with production systems.",[92,93,94],"Current API reference (for example OpenAPI specifications), guides and changelogs","Sample code and test scripts for the sandbox","Resolved support tickets, cleaned of client data",[96,97,98],"Developer portal and documentation site","Sandbox environment (read only for the assistant)","Support ticketing system for escalation",{"steps":100,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[101,104,107,110],{"title":102,"detail":103},"Clean and connect the sources","Start from the API specifications and guides, add resolved tickets after removing client data, and set a refresh schedule tied to documentation releases.",{"title":105,"detail":106},"Launch in the docs first","Put an ask AI entry point on the documentation pages, where developers already are, then add it to the portal and sandbox.",{"title":108,"detail":109},"Keep it in the sandbox","Generate samples with sandbox hosts and placeholder credentials only, and refuse questions that ask for production data or client specific configuration.",{"title":111,"detail":112},"Learn from unanswered questions","Review questions the assistant could not answer every week and fix the documentation, not only the prompts.",[114,115,116,117],"Access to documentation and sandbox only, never to production systems, live credentials or client data","Secrets and tokens pasted by developers are masked before they reach the model","Answers cite the documentation page or endpoint they rely on","Anything about production access or entitlements is routed to a human implementation manager","Support engineers and implementation managers handle escalations and anything involving production access. The developer relations or documentation team reviews unanswered and poorly rated questions weekly and owns the content.",[120,121,122,123],"Support tickets per integration, before and after","Time from sandbox access to first successful production call","Questions answered and share rated helpful","Unanswered questions per documentation area",[125,128,131],{"title":126,"detail":127},"Invented endpoints or parameters","The model generates plausible but wrong calls. Ground answers in the specification, cite it, and test generated samples against the sandbox.",{"title":129,"detail":130},"Secrets in the chat","Developers paste API keys or tokens. Mask secrets at input and warn the user.",{"title":132,"detail":133},"Outdated answers after a release","The index lags a new API version. Tie reindexing to documentation releases and show the version an answer refers to.",{"euAiAct":135,"regulations":138,"guidance":143,"controls":156,"incidents":161},{"tier":136,"basis":137},"limited","A chatbot that interacts with developers must disclose that it is AI (Article 50). Code generation for integration is not listed in Annex III.",[139,140,141,142],"eu-ai-act","gdpr","dora","iso-42001",[144,150],{"title":145,"issuer":146,"region":147,"url":148,"note":149},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Developers must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":151,"issuer":152,"region":153,"url":154,"note":155},"OWASP Top 10 for Large Language Model Applications","OWASP","global","https://genai.owasp.org/llm-top-10/","The 2025 list covers prompt injection, sensitive information disclosure and improper output handling, the main risks of an assistant that generates code.",[157,158,159,160],"Documented boundary between the assistant and production systems","Secret masking at input and logging without credentials","Conversation logs retained and reviewed for quality","Change control on the indexed sources",[],{"howToBuild":163},"On Blits.ai this is an **AI agent** with a **knowledge base** built from the API reference,\nguides and resolved tickets, ingested from files and crawled documentation pages that are\nrecrawled after each release, and retrieved with hybrid search so exact endpoint names match. It runs in the **web\nwidget** on the documentation site and developer portal, where code blocks with copy to clipboard\nrender in the chat.\n\n**Guardrails** keep it to integration topics, **PII masking** with custom patterns removes keys\nand tokens before text reaches a model, and **human handover** sends escalations to the support\ndesk with the conversation. **Response feedback** on poorly rated answers shows where the docs need work, and\n**test suites** check answers on known questions after each documentation release. The model can\nbe switched per agent without rebuilding.",[165,168,171],{"question":166,"answer":167},"Do developer assistants reduce support tickets?","Published vendor case studies say so. Mapbox reports a 30% monthly reduction in support tickets and CircleCI 28% faster support response times. At monday.com the assistant answers more than 125,000 technical queries a year. These are vendor case studies, not independent measurements.",{"question":169,"answer":170},"Are banks doing this?","Yes. U.S. Bank added a generative AI Developer Assistant to its Developer Portal that recommends APIs, helps troubleshoot and generates sample code, aiming to cut integration time by weeks.",{"question":172,"answer":173},"What must a bank keep out of the assistant?","Production data, live client credentials and entitlements. Keep it on documentation and the sandbox, mask any secrets developers paste, and route production questions to a person.",[175,176,177,178,179],"developer-coding-assistant","corporate-client-servicing-assistant","corporate-account-onboarding-orchestration","support-knowledge-article-generation","it-service-desk-resolution-agent","2026-09-27",[182],{"date":180,"note":183},"First published","developer-api-integration-assistant",[186,213,253,281],{"title":187,"useCases":188,"organization":189,"vendors":194,"summary":195,"stage":196,"year":197,"channels":198,"languages":199,"metrics":201,"outcomeDisclosed":191,"sources":202,"verification":207,"grade":210,"id":211,"organizationSlug":212},"U.S. Bank: generative AI Developer Assistant for API integration",[184],{"name":190,"anonymized":191,"country":192,"region":193,"industry":18},"U.S. Bank",false,"US","north-america",[],"U.S. Bank added a generative AI Developer Assistant to its Developer Portal for developers at clients, software providers and aggregators who embed its treasury, payments and data services. It answers integration questions, recommends the right APIs, helps troubleshoot and generates sample code, and promotes practices such as account tokenization. The bank aims to cut average integration time by weeks by reducing technical consultations and support tickets; no measured result is published yet.","production",2026,[30],[200],"en",[],[203],{"url":204,"title":205,"publisher":190,"date":206},"https://www.usbank.com/about-us-bank/news-and-stories/article-library/genai-assistant-helps-companies-embed-us-bank-solutions-into-their-platforms.html","GenAI assistant helps companies embed U.S. Bank solutions into their platforms","2026-01-26",{"level":208,"checkedAt":209},"source-verified","2026-09-26","B","us-bank-developer-assistant",null,{"title":214,"useCases":215,"organization":216,"vendors":218,"summary":222,"stage":196,"year":197,"channels":223,"languages":224,"metrics":225,"outcomeDisclosed":246,"sources":247,"verification":250,"grade":251,"id":252,"organizationSlug":212},"CircleCI: AI assistant in docs, product and support for pipeline developers",[184],{"name":217,"anonymized":191,"country":192,"region":193,"industry":20},"CircleCI",[219],{"name":220,"role":221},"Kapa.ai","platform","CircleCI fed an AI assistant its documentation, API references, forum threads, internal support knowledge and release notes, and put it in the docs, inside the CircleCI app and in front of its support team. It also exposes its configuration schema and API reference to coding agents through a hosted MCP endpoint, so generated pipeline configuration follows CircleCI's actual syntax. CircleCI reports 28% faster support response times, and Kapa.ai reports a 10% increase in coverage of languages other than English, without naming them.",[30,32,31],[200],[226,234,241],{"kpi":47,"value":227,"unit":228,"qualifier":229,"period":230,"claimant":231,"quote":232,"sourceUrl":233},28,"percent","exact","support response times","organization","The 28% faster response times increase the value of our support packages.","https://www.kapa.ai/customer-examples/circleci",{"kpi":49,"value":235,"unit":236,"qualifier":237,"period":238,"claimant":239,"quote":240,"sourceUrl":233},32000,"count","at-least","technical questions answered, period not stated","vendor","32,000+ technical questions answered",{"kpi":48,"value":242,"unit":243,"qualifier":237,"period":244,"claimant":239,"quote":245,"sourceUrl":233},500,"hours","per month","500+ support hours saved every month",true,[248],{"url":233,"title":249,"publisher":220},"How CircleCI improved support response times by 28% across docs, product, and support",{"level":208,"checkedAt":180},"C","circleci-docs-ai-assistant",{"title":254,"useCases":255,"organization":256,"vendors":258,"summary":260,"stage":261,"year":197,"channels":262,"languages":264,"metrics":265,"outcomeDisclosed":246,"sources":276,"verification":279,"grade":251,"id":280,"organizationSlug":212},"Mapbox: AI assistant across docs, app and support for developers",[184],{"name":257,"anonymized":191,"country":192,"region":193,"industry":20},"Mapbox",[259],{"name":220,"role":221},"Mapbox, whose maps and location APIs are used by millions of developers, connected an AI assistant to its public docs, SDKs and API references plus private support knowledge, refreshed weekly. The same assistant sits in the docs, a dedicated developer assistant page, the logged in account app, Discord and the support desk, where it drafts answers with sources for support engineers. Mapbox reports a 30% monthly reduction in support tickets, and Kapa.ai reports that 20% of questions are answered in languages other than English, without naming them.","scaled",[30,263,32],"social-messaging",[200],[266,270,272],{"kpi":49,"value":267,"unit":236,"qualifier":237,"period":84,"claimant":239,"quote":268,"sourceUrl":269},175000,"That consistency drives a 30% reduction in monthly support tickets from paid users, 175,000+ questions answered yearly (40,000+ support hours saved), and a 26% increase in questions asked to Kapa, with 20% answered in non-English.","https://www.kapa.ai/customer-examples/mapbox",{"kpi":48,"value":271,"unit":243,"qualifier":237,"period":84,"claimant":239,"quote":268,"sourceUrl":269},40000,{"kpi":46,"value":273,"unit":228,"qualifier":229,"period":274,"claimant":231,"quote":275,"sourceUrl":269},30,"monthly support tickets","We've seen fantastic results with Kapa.ai, recently achieving a 30% monthly reduction in support tickets and significant productivity gains for our Technical Support Engineers.",[277],{"url":269,"title":278,"publisher":220},"How Mapbox reduced monthly support tickets by 30% across web, Discord, and support",{"level":208,"checkedAt":180},"mapbox-docs-ai-assistant",{"title":282,"useCases":283,"organization":284,"vendors":288,"summary":290,"stage":196,"year":197,"channels":291,"languages":292,"metrics":293,"outcomeDisclosed":246,"sources":298,"verification":301,"grade":251,"id":302,"organizationSlug":212},"monday.com: AI assistant in developer docs and the API Playground",[184],{"name":285,"anonymized":191,"country":286,"region":287,"industry":20},"monday.com","IL","middle-east",[289],{"name":220,"role":221},"monday.com, whose developer ecosystem counts more than 100,000 customers building on its API, deployed an AI assistant in two places: an Ask AI widget in the developer documentation and an in product assistant inside the API Playground and developer center. It answers implementation and troubleshooting questions in real time for a global, multilingual developer base, where a slow answer risks a stalled integration. Kapa.ai reports that 10% of questions are answered in languages other than English, without naming them. Kapa.ai also claims over 50,000 support hours saved from repetitive questions, but this appears to be a modelled vendor estimate rather than a measured result, with no stated period.",[30],[200],[294],{"kpi":49,"value":295,"unit":236,"qualifier":237,"period":84,"claimant":239,"quote":296,"sourceUrl":297},125000,"125,000+ technical queries answered every year","https://www.kapa.ai/customer-examples/monday",[299],{"url":297,"title":300,"publisher":220},"How Monday.com scaled AI chat to 100,000+ customers",{"level":208,"checkedAt":180},"monday-com-developer-docs-assistant",0,[305,313,321,327],{"kpi":49,"label":306,"unit":236,"aggregate":191,"higherIsBetter":246,"n":307,"nUpTo":303,"median":295,"min":235,"max":267,"byClaimant":308,"vendorOnly":246,"points":309},"Interactions handled",3,{"organization":303,"vendor":307,"regulator":303,"independent":303},[310,311,312],{"evidenceId":280,"organization":257,"value":267,"qualifier":237,"claimant":239,"grade":251,"pooled":246},{"evidenceId":302,"organization":285,"value":295,"qualifier":237,"claimant":239,"grade":251,"pooled":246},{"evidenceId":252,"organization":217,"value":235,"qualifier":237,"claimant":239,"grade":251,"pooled":246},{"kpi":48,"label":314,"unit":243,"aggregate":191,"higherIsBetter":246,"n":315,"nUpTo":303,"median":316,"min":242,"max":271,"byClaimant":317,"vendorOnly":246,"points":318},"Hours saved",2,20250,{"organization":303,"vendor":315,"regulator":303,"independent":303},[319,320],{"evidenceId":280,"organization":257,"value":271,"qualifier":237,"claimant":239,"grade":251,"pooled":246},{"evidenceId":252,"organization":217,"value":242,"qualifier":237,"claimant":239,"grade":251,"pooled":246},{"kpi":46,"label":322,"unit":228,"aggregate":246,"higherIsBetter":246,"n":323,"nUpTo":303,"median":273,"min":273,"max":273,"byClaimant":324,"vendorOnly":191,"points":325},"Contact deflection",1,{"organization":323,"vendor":303,"regulator":303,"independent":303},[326],{"evidenceId":280,"organization":257,"value":273,"qualifier":229,"claimant":231,"grade":251,"pooled":246},{"kpi":47,"label":328,"unit":228,"aggregate":246,"higherIsBetter":246,"n":323,"nUpTo":303,"median":227,"min":227,"max":227,"byClaimant":329,"vendorOnly":191,"points":330},"Response time reduction",{"organization":323,"vendor":303,"regulator":303,"independent":303},[331],{"evidenceId":252,"organization":217,"value":227,"qualifier":229,"claimant":231,"grade":251,"pooled":246},{"low":333,"high":334},108000,520000,[336,360,377,389,406],{"slug":175,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":343,"patterns":344,"audience":345,"autonomy":346,"adoptionStage":347,"evidenceCount":348,"publicEvidenceCount":348,"organizations":349,"bestGrade":210,"headline":356,"lastVerified":180,"indexable":246},"AI coding assistant for software developers","Developer coding assistant","An AI assistant in the developer's IDE and code review flow that completes and generates code, explains unfamiliar modules, drafts unit tests and reviews pull requests for common defects, while generated code goes through the same review, testing and change controls as any other code.",[17,18,341,20,342],"capital-markets","professional-services",[22],[28],"employee-facing","copilot","mainstream",6,[350,351,352,353,354,355],"Accenture","ANZ","Bank of America","Citi","CME Group","Meta",{"kpi":357,"label":358,"unit":228,"n":307,"nUpTo":303,"kind":359,"value":64,"qualifier":229,"claimant":212,"organization":212,"vendorReported":191},"productivity-gain","Productivity gain","median",{"slug":176,"title":361,"shortTitle":362,"definition":363,"status":9,"industries":364,"functions":365,"patterns":367,"audience":33,"autonomy":370,"adoptionStage":35,"segment":36,"evidenceCount":371,"publicEvidenceCount":315,"organizations":372,"bestGrade":210,"headline":374,"lastVerified":180,"indexable":246},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[18,19],[23,366],"operations",[27,26,368,369],"agentic-workflow","summarization","supervised-agent",5,[352,373],"DBS Bank",{"kpi":46,"label":322,"unit":228,"n":323,"nUpTo":303,"kind":375,"value":376,"qualifier":229,"claimant":231,"organization":352,"vendorReported":191},"reported",16,{"slug":177,"title":378,"shortTitle":379,"definition":380,"status":9,"industries":381,"functions":382,"patterns":383,"audience":33,"autonomy":346,"adoptionStage":386,"segment":36,"evidenceCount":315,"publicEvidenceCount":315,"organizations":387,"bestGrade":210,"headline":212,"lastVerified":180,"indexable":246},"AI orchestration of corporate account opening and channel setup","Corporate onboarding operations","An AI agent that runs the operational setup of a corporate client after the due diligence has been approved: it reads mandates, board resolutions and signatory documents, prepares accounts, users, roles and payment entitlements for approval, configures channel access, and chases outstanding items with the client, turning a manual setup that passes between several teams into a tracked, guided flow.",[18],[24,366],[368,384,27,385],"document-processing","content-generation","emerging",[353,388],"Standard Chartered",{"slug":178,"title":390,"shortTitle":391,"definition":392,"status":9,"industries":393,"functions":396,"patterns":398,"audience":345,"autonomy":346,"adoptionStage":386,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":210,"headline":212,"lastVerified":180,"indexable":246},"AI for support knowledge article generation and maintenance","Knowledge article generation","AI that drafts knowledge base articles from resolved tickets, cases and conversations, detects questions the knowledge base does not answer and articles that are outdated or contradict each other, and proposes new or revised articles for a knowledge owner to review and publish.",[17,394,395],"government","automotive",[397,23,22],"knowledge-management",[385,369,399],"classification-and-routing",4,[402,403,404,405],"Centers for Disease Control and Prevention","Internal Revenue Service","U.S. National Science Foundation","Rivian",{"slug":179,"title":407,"shortTitle":408,"definition":409,"status":9,"industries":410,"functions":413,"patterns":414,"audience":345,"autonomy":370,"adoptionStage":347,"evidenceCount":415,"publicEvidenceCount":348,"organizations":416,"bestGrade":210,"headline":422,"lastVerified":180,"indexable":246},"AI agent for IT service desk resolution","IT service desk resolution","An AI agent in Microsoft Teams, Slack or the intranet that takes the high volume IT support queue, such as password and MFA resets, account unlocks, VPN, device and software requests, and resolves common requests by acting in the identity and IT service management systems, handing the rest to the right resolver group with the context attached.",[17,18,20,411,412],"retail-and-ecommerce","healthcare",[22,366],[27,368,26,399],8,[417,352,418,419,420,421],"7-Eleven Vietnam","Equinix","IBM","Mercari US","Vituity",{"kpi":423,"label":424,"unit":228,"n":315,"nUpTo":303,"kind":375,"value":425,"qualifier":229,"claimant":239,"organization":420,"vendorReported":246},"employee-adoption","Employee adoption",94,{"indexable":246,"reasons":427},[],[429,433,438,444,451,456,463,470,478,485,491,497,503,510,516,521,528,534,540,546,552,558,564,569,574,581,587,592,597,603,609,615,621,626],{"id":139,"label":430,"issuer":146,"region":147,"url":148,"description":431,"useCases":432,"indexable":246},"EU AI Act","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":140,"label":434,"issuer":146,"region":147,"url":435,"description":436,"useCases":437,"indexable":246},"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":142,"label":439,"issuer":440,"region":153,"url":441,"description":442,"useCases":443,"indexable":246},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":445,"label":446,"issuer":447,"region":193,"url":448,"description":449,"useCases":450,"indexable":246},"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":141,"label":452,"issuer":146,"region":147,"url":453,"description":454,"useCases":455,"indexable":246},"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":457,"label":458,"issuer":459,"region":147,"url":460,"description":461,"useCases":462,"indexable":246},"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":464,"label":465,"issuer":466,"region":147,"url":467,"description":468,"useCases":469,"indexable":246},"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":471,"label":472,"issuer":473,"region":474,"url":475,"description":476,"useCases":477,"indexable":246},"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":479,"label":480,"issuer":481,"region":474,"url":482,"description":483,"useCases":484,"indexable":246},"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":486,"label":487,"issuer":488,"region":153,"url":489,"description":490,"useCases":64,"indexable":246},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":492,"label":493,"issuer":494,"region":193,"url":495,"description":496,"useCases":64,"indexable":246},"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":498,"label":499,"issuer":500,"region":147,"url":501,"description":502,"useCases":376,"indexable":246},"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.",{"id":504,"label":505,"issuer":506,"region":153,"url":507,"description":508,"useCases":509,"indexable":246},"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":511,"label":512,"issuer":146,"region":147,"url":513,"description":514,"useCases":515,"indexable":246},"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":517,"label":518,"issuer":146,"region":147,"url":519,"description":520,"useCases":515,"indexable":246},"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":522,"label":523,"issuer":524,"region":193,"url":525,"description":526,"useCases":527,"indexable":246},"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":529,"label":530,"issuer":146,"region":147,"url":531,"description":532,"useCases":533,"indexable":246},"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":535,"label":536,"issuer":537,"region":193,"url":538,"description":539,"useCases":533,"indexable":246},"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":541,"label":542,"issuer":543,"region":153,"url":544,"description":545,"useCases":533,"indexable":246},"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":547,"label":548,"issuer":146,"region":147,"url":549,"description":550,"useCases":551,"indexable":246},"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":553,"label":554,"issuer":555,"region":193,"url":556,"description":557,"useCases":551,"indexable":246},"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":559,"label":560,"issuer":473,"region":474,"url":561,"description":562,"useCases":563,"indexable":246},"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":565,"label":566,"issuer":146,"region":147,"url":567,"description":568,"useCases":563,"indexable":246},"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":570,"label":571,"issuer":146,"region":147,"url":572,"description":573,"useCases":563,"indexable":246},"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":575,"label":576,"issuer":577,"region":147,"url":578,"description":579,"useCases":580,"indexable":246},"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":582,"label":583,"issuer":584,"region":193,"url":585,"description":586,"useCases":415,"indexable":246},"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":588,"label":589,"issuer":146,"region":147,"url":590,"description":591,"useCases":415,"indexable":246},"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":593,"label":594,"issuer":146,"region":147,"url":595,"description":596,"useCases":348,"indexable":246},"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":598,"label":599,"issuer":600,"region":287,"url":601,"description":602,"useCases":371,"indexable":246},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":604,"label":605,"issuer":606,"region":147,"url":607,"description":608,"useCases":400,"indexable":246},"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":610,"label":611,"issuer":612,"region":147,"url":613,"description":614,"useCases":400,"indexable":246},"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":616,"label":617,"issuer":618,"region":474,"url":619,"description":620,"useCases":307,"indexable":246},"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":622,"label":623,"issuer":146,"region":147,"url":624,"description":625,"useCases":307,"indexable":246},"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":627,"label":628,"issuer":629,"region":193,"url":630,"description":631,"useCases":307,"indexable":246},"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.",1790598296743]