[{"data":1,"prerenderedAt":621},["ShallowReactive",2],{"uc-internal-talent-marketplace-matching":3,"uc-regulations":412},{"useCase":4,"evidence":198,"blitsAiDeployments":311,"benchmarks":312,"indicative":324,"related":327,"indexability":410,"includeUnpublished":204},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":23,"patterns":25,"channels":28,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":45,"macroEstimates":85,"feasibility":86,"implementation":99,"risk":142,"blitsAi":177,"faq":179,"related":189,"datePublished":193,"dateModified":193,"lastVerified":193,"changelog":194,"slug":197},"AI internal talent marketplace for matching employees to projects, roles and mentors","Internal talent marketplace","AI internal talent marketplace and mobility","AI matches employees to internal gigs, roles and mentors by skills. Mastercard says 90% of its workforce is on its Unlocked talent marketplace.","published","An internal platform that uses AI to infer employees' skills and interests and recommend short term projects, open roles, mentors and learning to them, while showing managers which employees fit an opportunity, so that work is staffed from inside before hiring or contracting externally.",[12,13,14,15,16,17],"talent marketplace","internal mobility platform","opportunity marketplace","AI skills matching","internal gig marketplace","career pathing AI",[19,20,21,22],"cross-industry","manufacturing","payments","government",[24],"human-resources",[26,27],"recommendation-and-personalization","prediction-and-scoring",[29],"internal-tools","employee-facing","assist","early-adopters","In many large organizations employees find internal opportunities through their network and their\nmanager, if at all. Open roles and short projects are advertised unevenly, skills are recorded in\njob titles rather than in anything searchable, and managers who need help quickly hire a contractor\nbecause they cannot see who inside the company has the skill and the time. People who want to grow\nleave to do it elsewhere: Schneider Electric's internal surveys, as reported by its platform vendor\nGloat, found that nearly half of departing employees cited a lack of internal growth opportunities\nas their main reason.\n\nAt the same time, skills needs change faster than job architectures. HR teams want to know which\nskills they have, where the gaps are and how to redeploy people, but a skills inventory built by\nhand soon falls behind.",[],"1. **Build a skills profile.** The employee imports a CV or professional profile; AI extracts and\n   infers skills from it and from HR data, and the employee confirms, adds aspirations and\n   availability.\n2. **Post opportunities.** Managers post projects, gigs, open roles and mentoring offers with the\n   skills needed and the time involved.\n3. **Match both ways.** The platform recommends opportunities, mentors and learning to each\n   employee, and suggests candidates to the manager, ranked by skills fit and stated interest.\n4. **Apply and agree.** Employees apply, managers choose, and the employee's own manager agrees the\n   time commitment; the platform records the assignment.\n5. **Learn from the data.** Completed assignments update the skills profile, and aggregated skills\n   data shows HR where the gaps are for learning and hiring plans, as Mastercard describes.",[37,38,39,40],"employee-productivity","cost-to-serve","speed","inclusion-and-access",[42,43,44],"employee-adoption","cost-savings","users-served",{"referenceOrg":46,"inputs":47,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A company with 20,000 employees",[48,53,60,67,74],{"key":49,"label":50,"low":51,"high":51,"unit":49,"note":52},"employees","Employees with access to the marketplace",20000,"The reference company.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"gigShare","Share of employees who complete an internal project or gig in a year",0.01,0.02,"fraction of employees","Editorial assumption, replace with your own participation data. None of the evidence records gives an annual participation rate.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"hoursPerGig","Hours of work per project or gig",40,60,"hours per gig","Editorial assumption, replace with your own data. With these values the company logs 0.4 to 1.2 hours of internal gig work per employee a year. The evidence gives no annual rate and does not support one: Schneider Electric's hours are cumulative since its April 2020 launch with no end date, and Gloat's story is inconsistent (360,000 hours in its text, 550,000 in its header), which is 2.3 to 3.5 hours per employee in total for a workforce of about 155,000, so fewer per year.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"externalShare","Share of those hours that would otherwise have been bought from contractors or new hires",0.2,0.4,"fraction of hours","Editorial assumption; much internal gig work would otherwise not be done at all.",{"key":75,"label":76,"low":64,"high":77,"unit":78,"note":79},"contractorRate","Cost of an external contractor hour",100,"USD per hour","Editorial assumption.","employees * gigShare * hoursPerGig * externalShare * contractorRate","USD","per year","External contractor and hiring cost avoided","Counts only avoided external spend on gig work. It leaves out the effect on retention and hiring for open roles, the value of work that would otherwise not be done, the time employees spend away from their main job, and the platform and change management costs.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"medium","The matching technology is available off the shelf. The hard parts are a usable skills taxonomy, manager behaviour (posting work and releasing people), works council and data protection agreements, and making the platform fair and explainable enough to be trusted.",[90,91,92,93],"Employee profiles with skills, experience and aspirations, confirmed by the employee","A skills taxonomy or ontology mapped to roles","Open roles, projects and mentoring offers with required skills and time","Policy on eligibility, time allowance and manager approval",[95,96,97,98],"HR information system (employee records, org structure, job architecture)","Applicant tracking system for internal roles","Learning management system","Collaboration tools for notifications, such as Microsoft Teams",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Agree the rules before the platform","Decide who can take part, how much time employees may spend on gigs, how managers approve and how internal applications are treated. Involve works councils early, because the system processes employee data and influences career decisions.",{"title":105,"detail":106},"Pilot in one function","Schneider Electric piloted in HR before a global launch. Choose a function with enough project work, seed it with real opportunities and measure participation and manager feedback.",{"title":108,"detail":109},"Let employees own their profile","Show employees the skills the system inferred and let them correct them. Profiles people trust produce matches people accept.",{"title":111,"detail":112},"Test the matching for fairness","Compare recommendation and selection rates across groups, check that protected characteristics and proxies are not used, and document the results.",{"title":114,"detail":115},"Connect it to workforce planning","Use aggregated skills data to plan learning and hiring, and report outcomes such as roles filled internally and contractor spend avoided, not only registrations.",[117,118,119,120,121],"Recommendations only; managers and employees make every selection decision","No protected characteristics or obvious proxies in matching features","Employees can see, correct and delete inferred skills","Regular fairness testing of recommendations and outcomes, with results retained","Transparent explanation of why an opportunity or candidate was suggested","Employees decide what to apply for, managers decide whom to select, and the employee's own manager agrees the time. HR owns the taxonomy, reviews fairness reports and handles complaints about recommendations.",[124,125,126,127,128],"Active users per month as a share of employees, not only registrations","Roles and projects filled internally, and time to fill","Recommendation acceptance rate and selection rate by group","Contractor spend and external hires avoided for filled gigs","Retention of participants versus comparable non participants",[130,133,136,139],{"title":131,"detail":132},"Registrations without activity","High sign up figures hide low regular use. Report monthly active users and filled opportunities.",{"title":134,"detail":135},"Managers do not release people","Employees apply but their managers block the time. Set a time allowance policy and make releasing talent part of manager goals.",{"title":137,"detail":138},"Biased matching","Skills inferred from past roles reproduce past inequalities in who got which job. Test outcomes by group and let people correct their profiles.",{"title":140,"detail":141},"Stale skills data","Profiles are filled once and never updated. Refresh them from completed assignments and learning, and prompt employees periodically.",{"euAiAct":143,"regulations":146,"guidance":153,"controls":170,"incidents":176},{"tier":144,"basis":145},"context-dependent","Annex III point 4 lists AI used for the recruitment or selection of natural persons (4(a)) and AI used to make decisions affecting promotion, or to allocate tasks based on individual behaviour, personal traits or characteristics (4(b)). A marketplace that ranks employees for internal roles or allocates projects on the basis of inferred traits is therefore high risk. Recommending learning content or mentors to an employee who chooses freely is usually not. Deployers of the high risk part must inform workers' representatives and the affected employees before use (Article 26).",[147,148,149,150,151,152],"eu-ai-act","gdpr","uk-gdpr","nyc-local-law-144","iso-42001","nist-ai-rmf",[154,160,164],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4 covers employment, workers' management and access to self employment, including selection, promotion and task allocation.",{"title":161,"issuer":156,"region":157,"url":162,"note":163},"Article 26, obligations of deployers of high risk AI systems","https://artificialintelligenceact.eu/article/26/","Human oversight, logs, and informing workers' representatives and affected workers before a high risk system is used at the workplace.",{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Automated employment decision tools (Local Law 144)","New York City Department of Consumer and Worker Protection","north-america","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Employers may use an automated employment decision tool only after a bias audit, with the results published and notices given to employees or job candidates. Because the notices also cover employees, check whether a tool that ranks employees for internal roles is in scope under the law's definitions.",[171,172,173,174,175],"AI Act classification per feature, with the high risk features managed as such","Data protection impact assessment and works council agreement where required","Fairness testing before launch and at least yearly, with results retained","Named HR owner for the matching logic and for complaints","Supplier due diligence on model documentation, data use and hosting",[],{"howToBuild":178},"Blits.ai is not a talent marketplace product, but the conversational layer and the matching\nworkflow can be built on it. An **AI agent** in **Microsoft Teams** or the company's intranet\nhelps employees describe their skills and interests in their own words and answers questions\nabout the programme from a **knowledge base** of the mobility policy. **Custom functions** read\nopen projects and roles from the HR system (the integration catalog includes Workday) and write\nback applications.\n\nMatching runs as an **agentic workflow** with **structured output** that explains, for each\nsuggestion, which skills matched and which are missing, and any suggestion that reaches a\nmanager for an internal role waits for **human in the loop** handling, with a full audit trail.\n**Guardrails** stop the agent from asking about or using protected characteristics, **PII masking**\nand the **GDPR toolkit** handle retention and removal requests, and **test suites** can replay\nmatched employee profiles that differ only in a protected attribute to check for inconsistent\ntreatment.",[180,183,186],{"question":181,"answer":182},"What results do companies report from AI talent marketplaces?","Mastercard says 90% of its workforce is on its Unlocked marketplace, with 500,000 project hours delivered. Gloat reports that more than 2,300 Schneider Electric employees began to explore new roles in the first two months, and over USD 15 million in productivity gains and reduced recruitment costs since the 2020 launch. Registration is not the same as regular use, so track active users and filled opportunities.",{"question":184,"answer":185},"Is an internal talent marketplace high risk under the EU AI Act?","Parts of it can be. Ranking employees for internal roles or allocating tasks based on inferred traits falls under Annex III point 4. Recommending mentors or courses that the employee chooses freely usually does not. Classify each feature and inform workers' representatives before using a high risk one.",{"question":187,"answer":188},"Does it replace internal recruiters?","No. It makes opportunities visible and suggests matches; managers and recruiters still select. The gain is in speed and reach, especially for short projects that would otherwise go to a contractor.",[190,191,192],"recruitment-screening-and-interview-scheduling","employee-onboarding-assistant","hr-and-policy-assistant","2026-09-27",[195],{"date":193,"note":196},"First published","internal-talent-marketplace-matching",[199,227,250,274],{"title":200,"useCases":201,"organization":202,"vendors":207,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":215,"sources":216,"verification":222,"grade":224,"id":225,"organizationSlug":226},"Mastercard: AI matching in the Unlocked internal talent marketplace",[197],{"name":203,"anonymized":204,"country":205,"region":206,"industry":21},"Mastercard",false,"US","global",[],"Mastercard uses AI in Unlocked, its internal talent marketplace, to match employees to short term projects, volunteering, open roles, mentors and learning pathways, based on the skills they have and the skills they want to build. The company says 90% of its workforce is on the platform, with 500,000 project hours delivered, and that the skills data shows where it has gaps so it can plan learning paths or hiring. The same article describes an AI interview scheduling tool.","scaled",2024,[29],[213],"en",[],true,[217],{"url":218,"title":219,"publisher":220,"archivedUrl":221},"https://www.mastercard.com/news/perspectives/2024/at-the-inflection-of-ai-and-hr-how-we-re-equipping-employees-for-the-ai-era/","At the inflection of AI and HR: How we're equipping employees for the AI era","Mastercard Newsroom","https://web.archive.org/web/20260616181554/https://www.mastercard.com/news/perspectives/2024/at-the-inflection-of-ai-and-hr-how-we-re-equipping-employees-for-the-ai-era",{"level":223,"checkedAt":193},"source-verified","B","mastercard-unlocked-talent-marketplace","mastercard",{"title":228,"useCases":229,"organization":230,"vendors":232,"summary":236,"stage":237,"year":238,"channels":239,"languages":240,"metrics":241,"outcomeDisclosed":204,"sources":242,"verification":247,"grade":224,"id":248,"organizationSlug":249},"Federal Bureau of Prisons: Pathfinder suggests career pathways to employees",[197],{"name":231,"anonymized":204,"country":205,"region":167,"industry":22},"Federal Bureau of Prisons",[233],{"name":234,"role":235},"Microsoft Azure","platform","The Federal Bureau of Prisons, part of the US Department of Justice, runs Pathfinder to help its employees with career pathways. The system generates assessments from what the user enters, scores them and offers the employee options for career pathways. The inventory lists it as deployed since September 2023 on Azure. It is narrower than a full talent marketplace (no project or mentor matching is described) and no outcome figures are published.","production",2023,[29],[213],[],[243],{"url":244,"title":245,"publisher":246},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry DOJ-0159, Pathfinder)","Office of Management and Budget (GitHub)",{"level":223,"checkedAt":193},"federal-bureau-of-prisons-pathfinder-career-pathways",null,{"title":251,"useCases":252,"organization":253,"vendors":256,"summary":259,"stage":209,"year":260,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":204,"sources":264,"verification":272,"grade":224,"id":273,"organizationSlug":249},"Unilever: Flex Experiences AI platform matches employees with internal projects",[197],{"name":254,"anonymized":204,"country":255,"region":206,"industry":20},"Unilever","GB",[257],{"name":258,"role":235},"Gloat","Unilever's Flex Experiences platform uses AI to match employees with project opportunities across the business, so that people can work on projects for part of their time and build new skills while project leaders find the expertise they need. By 2020 it had been rolled out to more than 60,000 employees in more than 100 countries, and during the pandemic lockdowns of 2020 teams used it to staff urgent work quickly, such as an information and analytics (I&A) squad. Unilever publishes no outcome figures on the page cited.",2020,[29],[213],[],[265,269],{"url":266,"title":267,"publisher":254,"archivedUrl":268},"https://www.unilever.com/news/news-search/2020/an-exciting-new-normal-for-flexible-working/","An exciting new normal for flexible working","https://web.archive.org/web/2026/https://www.unilever.com/news/news-search/2020/an-exciting-new-normal-for-flexible-working/",{"url":270,"title":271,"publisher":258},"https://gloat.com/blog/unilever-ai-retain-develop-engage-talent-innermobility-gloat/","How Unilever implements AI to engage talents",{"level":223,"checkedAt":193},"unilever-flex-experiences-talent-marketplace",{"title":275,"useCases":276,"organization":277,"vendors":280,"summary":282,"stage":209,"year":260,"channels":283,"languages":284,"metrics":285,"outcomeDisclosed":215,"sources":301,"verification":308,"grade":309,"id":310,"organizationSlug":249},"Schneider Electric: Open Talent Market matches employees to gigs, roles and mentors",[197],{"name":278,"anonymized":204,"country":279,"region":206,"industry":20},"Schneider Electric","FR",[281],{"name":258,"role":235},"Schneider Electric's internal surveys, as reported by Gloat, showed that nearly half of departing employees cited a lack of internal growth opportunities as their main reason for leaving. The company launched Open Talent Market, an AI talent marketplace, with a pilot in HR and a global launch in April 2020. Employees build a profile with their skills and aspirations and receive recommendations for part time projects, internal positions and mentors; managers posting projects see employees whose skills fit. Gloat reports that more than 2,300 employees began to explore new roles within the first two months, alongside an adoption rate above 60% whose base it does not state, and cumulative figures of more than 360,000 unlocked hours and over USD 15 million in productivity gains and reduced recruitment costs.",[29],[213],[286,295],{"kpi":44,"value":287,"unit":288,"qualifier":289,"period":290,"baseline":291,"claimant":292,"quote":293,"sourceUrl":294},2300,"count","at-least","first two months after launch","employees who began to explore new roles through the platform; the same sentence gives an adoption rate above 60% without saying what it is a share of, and 2,300 is about 1.5% of the 155,000 workforce, so the percentage is not recorded","vendor","Within the first two months of launch, the platform achieved an adoption rate that surpassed 60%, enabling more than 2,300 employees to begin to explore new roles within the business.","https://gloat.com/resources/customer-stories-2/how-schneider-electric-increased-employee-retention/",{"kpi":43,"value":296,"unit":297,"currency":81,"qualifier":289,"period":298,"baseline":299,"claimant":292,"quote":300,"sourceUrl":294},15000000,"currency","cumulative since the April 2020 launch, end date not stated","productivity gains and reduced recruitment costs combined, as stated in the vendor's story; not an annual figure","To date, Schneider Electric’s talent marketplace has unlocked more than 360,000 hours and created a savings of over $15,000,000 in productivity gains and reduced recruitment costs.",[302,304],{"url":294,"title":303,"publisher":258},"How Schneider Electric increased employee retention",{"url":305,"title":306,"publisher":307},"https://www.cio.com/article/651553/schneider-electric-leverages-ai-to-help-develop-employees-careers.html","Schneider Electric leverages AI to help develop employees' careers","CIO",{"level":223,"checkedAt":193},"C","schneider-electric-open-talent-market",0,[313,319],{"kpi":43,"label":314,"unit":297,"currency":81,"aggregate":204,"higherIsBetter":215,"n":315,"nUpTo":311,"median":296,"min":296,"max":296,"byClaimant":316,"vendorOnly":215,"points":317},"Cost savings",1,{"organization":311,"vendor":315,"regulator":311,"independent":311},[318],{"evidenceId":310,"organization":278,"value":296,"qualifier":289,"claimant":292,"grade":309,"pooled":215},{"kpi":44,"label":320,"unit":288,"aggregate":204,"higherIsBetter":215,"n":315,"nUpTo":311,"median":287,"min":287,"max":287,"byClaimant":321,"vendorOnly":215,"points":322},"Users served",{"organization":311,"vendor":315,"regulator":311,"independent":311},[323],{"evidenceId":310,"organization":278,"value":287,"qualifier":289,"claimant":292,"grade":309,"pooled":215},{"low":325,"high":326},96000,960000,[328,356,373,392],{"slug":190,"title":329,"shortTitle":330,"definition":331,"status":9,"industries":332,"functions":335,"patterns":336,"audience":340,"autonomy":341,"adoptionStage":32,"evidenceCount":342,"publicEvidenceCount":342,"organizations":343,"bestGrade":224,"headline":348,"lastVerified":193,"indexable":215},"AI for recruitment screening and interview scheduling","Recruitment screening and scheduling","AI that answers candidates' questions, collects applications in conversation, schedules interviews and, where the organization chooses, assesses applications against the job requirements for a recruiter, who makes every selection decision. In the EU, the screening part is a high risk AI system under Annex III point 4 of the AI Act.",[19,22,333,334],"travel-and-hospitality","professional-services",[24],[337,338,27,339],"conversational-agent","classification-and-routing","agentic-workflow","customer-facing","copilot",5,[344,345,346,203,347],"Chipotle Mexican Grill","Gojob","U.S. Immigration and Customs Enforcement","Trace3",{"kpi":349,"label":350,"unit":351,"n":315,"nUpTo":311,"kind":352,"value":353,"qualifier":354,"claimant":355,"organization":203,"vendorReported":204},"processing-time-reduction","Cycle time reduction","percent","reported",90,"approximately","organization",{"slug":191,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":362,"patterns":364,"audience":30,"autonomy":366,"adoptionStage":32,"evidenceCount":367,"publicEvidenceCount":368,"organizations":369,"bestGrade":224,"headline":249,"lastVerified":193,"indexable":215},"AI assistant for employee onboarding","Employee onboarding assistant","An assistant that guides each new employee from signed contract through the first months: it answers first week questions in plain language, tracks the personal onboarding checklist, triggers the paperwork, equipment, access and training steps in the systems that own them, and keeps the manager and HR informed of what is still open.",[19,22,334,361],"healthcare",[24,363],"knowledge-management",[337,365,339],"rag-knowledge-assistant","supervised-agent",4,3,[370,371,372],"American Addiction Centers","KPMG","U.S. Department of Agriculture",{"slug":192,"title":374,"shortTitle":375,"definition":376,"status":9,"industries":377,"functions":380,"patterns":381,"audience":30,"autonomy":366,"adoptionStage":32,"evidenceCount":342,"publicEvidenceCount":367,"organizations":382,"bestGrade":224,"headline":387,"lastVerified":193,"indexable":215},"AI assistant for HR and policy questions","HR and policy assistant","An employee self service assistant that answers questions on leave, pay and tax forms, benefits, expenses, travel and conduct policies from the organization's own HR documents, personalized to the employee's country and role, and starts simple HR transactions such as leave requests or employment letters in the HR system.",[19,378,379,361],"banking","technology",[24,363],[365,337,339],[383,384,385,386],"Bank of America","IBM","Turing","Vituity",{"kpi":42,"label":388,"unit":351,"n":389,"nUpTo":311,"kind":352,"value":390,"qualifier":391,"claimant":355,"organization":384,"vendorReported":204},"Employee adoption",2,99,"exact",{"slug":393,"title":394,"shortTitle":395,"definition":396,"status":9,"industries":397,"functions":398,"patterns":399,"audience":30,"autonomy":341,"adoptionStage":32,"evidenceCount":342,"publicEvidenceCount":342,"organizations":403,"bestGrade":224,"headline":409,"lastVerified":193,"indexable":215},"training-content-generation","AI for creating employee training and eLearning content","Training content creation","Generative AI that helps learning and development teams turn source material such as procedures, product documentation and policies into training: course outlines, lesson text, quizzes, narration, avatar videos and translations, which instructional designers and subject matter experts review before publishing.",[19,22,379,20],[24,363],[400,401,402],"content-generation","translation","summarization",[404,405,406,407,408],"Carlsberg Group","Internal Revenue Service","U.S. Marshals Service","Veterans Benefits Administration","Zoom",{"kpi":349,"label":350,"unit":351,"n":315,"nUpTo":311,"kind":352,"value":353,"qualifier":391,"claimant":292,"organization":408,"vendorReported":215},{"indexable":215,"reasons":411},[],[413,418,423,429,435,441,447,454,462,469,476,482,489,496,502,507,514,520,526,532,538,544,550,555,560,567,574,579,585,592,598,604,610,615],{"id":147,"label":414,"issuer":156,"region":157,"url":415,"description":416,"useCases":417,"indexable":215},"EU AI Act","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":148,"label":419,"issuer":156,"region":157,"url":420,"description":421,"useCases":422,"indexable":215},"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":151,"label":424,"issuer":425,"region":206,"url":426,"description":427,"useCases":428,"indexable":215},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":152,"label":430,"issuer":431,"region":167,"url":432,"description":433,"useCases":434,"indexable":215},"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":436,"label":437,"issuer":156,"region":157,"url":438,"description":439,"useCases":440,"indexable":215},"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":149,"label":442,"issuer":443,"region":157,"url":444,"description":445,"useCases":446,"indexable":215},"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":448,"label":449,"issuer":450,"region":157,"url":451,"description":452,"useCases":453,"indexable":215},"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":455,"label":456,"issuer":457,"region":458,"url":459,"description":460,"useCases":461,"indexable":215},"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":463,"label":464,"issuer":465,"region":458,"url":466,"description":467,"useCases":468,"indexable":215},"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":470,"label":471,"issuer":472,"region":206,"url":473,"description":474,"useCases":475,"indexable":215},"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":477,"label":478,"issuer":479,"region":167,"url":480,"description":481,"useCases":475,"indexable":215},"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":483,"label":484,"issuer":485,"region":157,"url":486,"description":487,"useCases":488,"indexable":215},"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":490,"label":491,"issuer":492,"region":206,"url":493,"description":494,"useCases":495,"indexable":215},"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":497,"label":498,"issuer":156,"region":157,"url":499,"description":500,"useCases":501,"indexable":215},"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":503,"label":504,"issuer":156,"region":157,"url":505,"description":506,"useCases":501,"indexable":215},"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":508,"label":509,"issuer":510,"region":167,"url":511,"description":512,"useCases":513,"indexable":215},"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":515,"label":516,"issuer":156,"region":157,"url":517,"description":518,"useCases":519,"indexable":215},"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":521,"label":522,"issuer":523,"region":167,"url":524,"description":525,"useCases":519,"indexable":215},"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":527,"label":528,"issuer":529,"region":206,"url":530,"description":531,"useCases":519,"indexable":215},"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":533,"label":534,"issuer":156,"region":157,"url":535,"description":536,"useCases":537,"indexable":215},"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":539,"label":540,"issuer":541,"region":167,"url":542,"description":543,"useCases":537,"indexable":215},"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":545,"label":546,"issuer":457,"region":458,"url":547,"description":548,"useCases":549,"indexable":215},"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":551,"label":552,"issuer":156,"region":157,"url":553,"description":554,"useCases":549,"indexable":215},"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":556,"label":557,"issuer":156,"region":157,"url":558,"description":559,"useCases":549,"indexable":215},"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":561,"label":562,"issuer":563,"region":157,"url":564,"description":565,"useCases":566,"indexable":215},"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":568,"label":569,"issuer":570,"region":167,"url":571,"description":572,"useCases":573,"indexable":215},"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":575,"label":576,"issuer":156,"region":157,"url":577,"description":578,"useCases":573,"indexable":215},"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":580,"label":581,"issuer":156,"region":157,"url":582,"description":583,"useCases":584,"indexable":215},"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.",6,{"id":586,"label":587,"issuer":588,"region":589,"url":590,"description":591,"useCases":342,"indexable":215},"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":593,"label":594,"issuer":595,"region":157,"url":596,"description":597,"useCases":367,"indexable":215},"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":599,"label":600,"issuer":601,"region":157,"url":602,"description":603,"useCases":367,"indexable":215},"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":605,"label":606,"issuer":607,"region":458,"url":608,"description":609,"useCases":368,"indexable":215},"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":611,"label":612,"issuer":156,"region":157,"url":613,"description":614,"useCases":368,"indexable":215},"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":616,"label":617,"issuer":618,"region":167,"url":619,"description":620,"useCases":368,"indexable":215},"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.",1790598302371]