[{"data":1,"prerenderedAt":562},["ShallowReactive",2],{"uc-ai-candidate-sourcing-and-talent-rediscovery":3,"uc-regulations":341},{"useCase":4,"evidence":178,"blitsAiDeployments":244,"benchmarks":245,"indicative":257,"related":260,"indexability":339,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":23,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":41,"macroEstimates":76,"feasibility":77,"implementation":88,"risk":126,"blitsAi":155,"faq":157,"related":170,"datePublished":173,"dateModified":173,"lastVerified":173,"changelog":174,"slug":177},"AI agent for candidate sourcing and talent rediscovery","AI candidate sourcing and rediscovery","AI candidate sourcing software for recruiters","AI resurfaces past applicants for open roles. Box cut time to hire 16%, and Forvia lifted career site visitor to applicant conversion by 3.5x.","published","AI that builds and works the candidate pipeline before an application arrives: it matches open roles against a company's own past applicants sitting unused in the applicant tracking system, ranks and surfaces the best fits for a recruiter to approach, and optimizes career site content and outreach to attract more of the right applicants, instead of a recruiter starting each search from an empty external search or a job board.",[12,13,14,15,16],"AI sourcing agent","candidate rediscovery","talent rediscovery","passive candidate matching","AI recruiting sourcing",[18,19,20],"cross-industry","manufacturing","technology",[22],"human-resources",[24,25],"recommendation-and-personalization","prediction-and-scoring",[27],"internal-tools","employee-facing","assist","early-adopters","Most recruiting effort goes into candidates who have already applied to an open role, while a\ncompany's applicant tracking system quietly accumulates thousands of past applicants, people good\nenough to interview for a different role, who were never hired and are never looked at again. A\nrecruiting team facing a hard to fill role, such as a security engineer in a location with sparse\ntalent, defaults to posting the job and waiting, or to an external search that costs money and takes\nweeks, without first checking who the company already knows.\n\nCareer sites have the same problem from the other direction: most visitors never apply, and the\ncontent, form length and channel mix that would convert more of them into applicants is rarely\ntested or optimized, so sourcing spend goes to buying more traffic instead of converting the traffic\nalready there.",[],"1. **Build a living talent pool.** The system reads every past applicant, employee referral and\n   sourced profile already in the applicant tracking system and infers each person's skills,\n   experience and likely fit for the roles the company hires for, refreshed as new applications and\n   outcomes come in.\n2. **Match against open roles.** When a role opens, it ranks the existing pool and any newly sourced\n   candidates against the role's requirements, and surfaces the recruiter's best matches, including\n   people who applied for a different role months or years earlier.\n3. **Optimize the front door.** For inbound traffic, it tests and personalizes career site content\n   and application flow to convert more visitors into applicants, and flags where the funnel is\n   losing people.\n4. **Recruiter decides, agent drafts the approach.** The recruiter reviews the suggested matches and\n   decides who to contact; the system can draft a personalized outreach message for the recruiter to\n   send, referencing the role and the candidate's relevant background.",[35,36,37],"employee-productivity","speed","cost-to-serve",[39,40],"productivity-gain","processing-time-reduction",{"referenceOrg":42,"inputs":43,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A company that fills 500 roles a year and processes 40,000 applications a year",[44,50,57,64],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"rolesPerYear","Roles filled per year",500,"roles per year","The reference company.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"rediscoveredShare","Share of roles filled from the existing applicant pool instead of new sourcing",0.05,0.15,"fraction of roles","Editorial assumption, replace with your own applicant tracking system data.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"hoursSavedPerRediscoveredHire","Recruiter hours saved per role filled by rediscovery instead of a fresh external search",15,30,"hours per hire","Editorial assumption. Box reports a 16% reduction in time to hire after adopting AI sourcing and rediscovery, https://eightfold.ai/wp-content/uploads/3Sixty-Insights-Anatomy-of-a-Decision-Eightfold-Box.pdf, but does not publish an hours figure, so this input is not taken directly from that source.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"recruiterHourCost","Fully loaded cost of a recruiter hour",40,70,"USD per hour","Editorial assumption.","rolesPerYear * rediscoveredShare * hoursSavedPerRediscoveredHire * recruiterHourCost","USD","per year","Recruiter time cost avoided from rediscovering existing candidates","Counts only recruiter time saved on roles filled from the existing pool instead of a fresh search. It leaves out any reduction in job board or agency spend, the value of a faster time to hire itself, the software cost, and any effect on candidate quality or diversity, which the evidence describes qualitatively but does not quantify consistently.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"medium","The matching technology is available off the shelf and integrates with common applicant tracking systems. The harder part is data quality in years of historical applicant records, and getting recruiters to change from reactive sourcing (working whichever req is open) to proactively building and maintaining pipelines, which is a workflow and incentive change, not just a tool rollout.",[81,82,83],"Historical applicant records in the applicant tracking system, with enough structured data to match against new roles","Career site analytics to see where visitors drop off before applying","A policy on how long a past applicant's data is retained and how recontact consent works",[85,86,87],"Applicant tracking system, for historical applicants and open roles","Career site or job board content management","Email or messaging tool the recruiter already uses for outreach",{"steps":89,"guardrails":105,"humanInTheLoop":110,"kpisToInstrument":111,"failureModes":116},[90,93,96,99,102],{"title":91,"detail":92},"Clean and connect the historical pool first","Rediscovery is only as good as the applicant history behind it. Confirm what is in the applicant tracking system, how old it is, and whether recontact consent and retention rules allow it to be used before switching the matching on.",{"title":94,"detail":95},"Pilot on the hardest roles to fill","Roles where talent is genuinely sparse, such as security engineering in certain locations, are where an existing applicant pool has the clearest advantage over a fresh external search: fewer qualified candidates means every past applicant is worth checking. Box, for example, resurfaced a past applicant to fill an open security engineer role this way.",{"title":97,"detail":98},"Give recruiters a dashboard, not just a ranked list","Box's team built a dashboard to track hires resulting specifically from resurfacing candidates in the applicant tracking system, and separately benchmarked recruiter adoption of the tool, so return on the tool could be measured rather than assumed from general activity.",{"title":100,"detail":101},"Let AI draft outreach, keep a human sending it","A drafted message referencing the candidate's real background saves time over a blank page, but a recruiter should read and personalize it before it goes out, especially to someone who applied once and was rejected.",{"title":103,"detail":104},"Track quality, not only speed","Faster time to hire is not the only goal. Track interview to offer ratio and early tenure outcomes for rediscovered hires against newly sourced ones so speed is not gained at the cost of fit.",[106,107,108,109],"Recruiters decide who to contact and what outreach to send; the system suggests and drafts, it does not contact candidates on its own","Past applicants can request their data be removed from the pool, honored promptly","No protected characteristics or obvious proxies used as ranking features","Regular fairness testing of who gets surfaced and contacted, by group","Recruiters review every suggested match and outreach draft before contacting a candidate; hiring managers make the actual selection decision as they would for any candidate. HR or talent acquisition leadership owns the matching model's fairness testing and the data retention policy.",[112,113,114,115],"Time to hire and time to fill for roles sourced from the pool versus newly sourced roles","Share of hires that come from the existing applicant pool","Recruiter adoption of the tool as a share of the recruiting team","Candidate response rate to AI drafted versus recruiter written outreach",[117,120,123],{"title":118,"detail":119},"Stale profiles surface a candidate who is no longer available or interested","A profile from years ago may no longer reflect the person's skills, interest or availability. Confirm interest before advancing someone the system resurfaced, and refresh the pool from outcomes over time.",{"title":121,"detail":122},"Rediscovery narrows instead of widens the pool","Always pulling from the same historical pool can reduce diversity of the candidate slate over time. Track the mix of rediscovered versus newly sourced candidates and set a floor for fresh sourcing.",{"title":124,"detail":125},"Outreach feels templated","A drafted message that is not personalized reads as spam and damages the employer brand. Require a recruiter to edit, not just approve, before sending.",{"euAiAct":127,"regulations":130,"guidance":136,"controls":149,"incidents":154},{"tier":128,"basis":129},"high","Annex III point 4(a) lists AI systems intended to be used to recruit or select natural persons, including to place targeted job advertisements and to analyse and filter applications. Ranking a company's own past applicants and new candidates against an open role, and deciding who a recruiter sees first, is filtering and evaluating candidates for that purpose.",[131,132,133,134,135],"eu-ai-act","gdpr","uk-gdpr","nyc-local-law-144","iso-42001",[137,143],{"title":138,"issuer":139,"region":140,"url":141,"note":142},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4(a) covers recruitment or selection of natural persons, including analysing and filtering applications and evaluating candidates.",{"title":144,"issuer":145,"region":146,"url":147,"note":148},"Automated Employment Decision Tools, Frequently Asked Questions","New York City Department of Consumer and Worker Protection","north-america","https://www.nyc.gov/assets/dca/downloads/pdf/about/DCWP-AEDT-FAQ.pdf","Local Law 144 requires a bias audit, published results and candidate notice before an employer uses a tool that substantially assists or replaces discretionary decision making. It applies only when the tool screens a candidate who has applied for a specific position; sourcing people who have not applied for a specific role falls outside its scope.",[150,151,152,153],"Bias audit of the ranking and matching model before use in a jurisdiction that requires one","Documented data retention and recontact consent policy for historical applicants","Named recruiting operations owner for the model and its fairness testing","Human review of every suggested match and outreach draft before a candidate is contacted",[],{"howToBuild":156},"Blits.ai does not build the candidate matching model itself; that sits inside a specialist talent\nintelligence platform connected to the applicant tracking system. What Blits.ai builds is the agent\nlayer a recruiter actually works with. An **agent** inside the recruiter's existing tools takes a\nplain language description of an open role and, through a **custom function** calling the talent\nplatform's API, returns ranked matches from the existing applicant pool with the reasons for the\nmatch, instead of the recruiter reading a raw ranked list.\n\nA **knowledge base** holds the organization's own outreach templates and employer brand guidance so\ndrafted messages sound like the company, and every drafted candidate message goes through **human\nin the loop approval** before it can be sent, with a full audit trail of what was suggested and\nwhat a recruiter actually sent. **Guardrails** keep protected characteristics and their obvious\nproxies out of the matching prompt and out of drafted outreach. **Analytics** give the recruiting\nteam a dashboard of agent interactions, and a **monitor** runs a recurring health check on the\nmatching agent so a broken integration to the talent platform is caught before recruiters notice.",[158,161,164,167],{"question":159,"answer":160},"What results have companies reported from AI sourcing and rediscovery?","Box reported, in a 3Sixty Insights report published by Eightfold, a 16% reduction in time to hire; the same report separately notes that 95% of Box's recruiting team was using the tool, and that Box resurfaced a past applicant to fill a hard to fill security engineer role. In a separate Eightfold case study, Forvia reports a 3.5 times increase in visitor to applicant conversion on its career site and 30% productivity gains in sourcing.",{"question":162,"answer":163},"How is this different from resume screening software?","Screening software ranks people who have already applied to a specific open role. Sourcing and rediscovery works the other direction: it searches a company's own historical applicant pool and external sources for people who fit a role before, or instead of, waiting for them to apply.",{"question":165,"answer":166},"Does the AI contact candidates on its own?","It should not. Neither the Box nor the Forvia source describes the review workflow in detail, but as recommended practice, a recruiter reviews every suggested match and any drafted outreach message before a candidate is contacted: the AI proposes, the recruiter decides and sends.",{"question":168,"answer":169},"Is AI candidate sourcing high risk under the EU AI Act?","Yes. Annex III point 4(a) covers AI used to recruit or select people, including filtering and evaluating applications, which is what ranking and surfacing candidates for a recruiter does.",[171,172],"recruitment-screening-and-interview-scheduling","internal-talent-marketplace-matching","2026-09-29",[175],{"date":173,"note":176},"First published","ai-candidate-sourcing-and-talent-rediscovery",[179,214],{"title":180,"useCases":181,"organization":182,"vendors":186,"summary":190,"stage":191,"year":192,"channels":193,"languages":194,"metrics":196,"outcomeDisclosed":203,"sources":204,"verification":209,"grade":211,"id":212,"organizationSlug":213},"Forvia: AI sourcing lifts career site conversion",[177],{"name":183,"anonymized":184,"country":185,"region":140,"industry":19},"Forvia",false,"FR",[187],{"name":188,"role":189},"Eightfold AI","platform","Forvia, an automotive technology supplier transitioning from a traditional manufacturer, deployed Eightfold's Talent Acquisition platform to find digital talent and increase sourcing efficiency across its global recruiting organization, opening new sourcing channels and improving applicant quality and diversity.","scaled",2024,[27],[195],"en",[197],{"kpi":39,"value":61,"unit":198,"qualifier":199,"claimant":200,"quote":201,"sourceUrl":202},"percent","exact","vendor","30% productivity gains in sourcing","https://eightfold.ai/customers/customer-stories/forvia-transforms-talent-acquisition-with-award-winning-eightfold-solution/",true,[205],{"url":202,"title":206,"publisher":188,"date":207,"archivedUrl":208},"Forvia transforms talent acquisition with award-winning Eightfold solution","2024-04-16","https://web.archive.org/web/20240416042837/https://eightfold.ai/customers/customer-stories/forvia-transforms-talent-acquisition-with-award-winning-eightfold-solution/",{"level":210,"checkedAt":173},"source-verified","C","forvia-eightfold-sourcing-transformation",null,{"title":215,"useCases":216,"organization":217,"vendors":220,"summary":222,"stage":223,"year":224,"channels":225,"languages":226,"metrics":227,"outcomeDisclosed":203,"sources":237,"verification":242,"grade":211,"id":243,"organizationSlug":213},"Box: candidate rediscovery cuts time to hire",[177],{"name":218,"anonymized":184,"country":219,"region":146,"industry":20},"Box","US",[221],{"name":188,"role":189},"Box, the cloud content management company, began evaluating vendors in 2021 and adopted Eightfold's AI enabled Talent Acquisition solution to integrate with its Greenhouse applicant tracking system and resurface past applicants already in it, instead of relying only on new inbound and outbound sourcing, aiming for more speed and agility in hiring, including for hard to fill roles such as security engineers.","production",2023,[27],[195],[228,233],{"kpi":40,"value":229,"unit":198,"qualifier":199,"claimant":230,"quote":231,"sourceUrl":232},16,"organization","Box reports having reduced time-to-hire by 16 percent.","https://eightfold.ai/wp-content/uploads/3Sixty-Insights-Anatomy-of-a-Decision-Eightfold-Box.pdf",{"kpi":234,"value":235,"unit":198,"qualifier":199,"claimant":200,"quote":236,"sourceUrl":232},"employee-adoption",95,"Ninety-five percent of the recruiting team at Box is utilizing the Eightfold tool.",[238],{"url":232,"title":239,"publisher":240,"date":241},"Anatomy of a Decision: Box Elects to Leverage Eightfold's AI-Enabled Talent Acquisition Solution to Facilitate Candidate Rediscovery","3Sixty Insights (published via Eightfold AI)","2023-01-01",{"level":210,"checkedAt":173},"box-eightfold-candidate-rediscovery",0,[246,252],{"kpi":40,"label":247,"unit":198,"aggregate":203,"higherIsBetter":203,"n":248,"nUpTo":244,"median":229,"min":229,"max":229,"byClaimant":249,"vendorOnly":184,"points":250},"Cycle time reduction",1,{"organization":248,"vendor":244,"regulator":244,"independent":244},[251],{"evidenceId":243,"organization":218,"value":229,"qualifier":199,"claimant":230,"grade":211,"pooled":203},{"kpi":39,"label":253,"unit":198,"aggregate":203,"higherIsBetter":203,"n":248,"nUpTo":244,"median":61,"min":61,"max":61,"byClaimant":254,"vendorOnly":203,"points":255},"Productivity gain",{"organization":244,"vendor":248,"regulator":244,"independent":244},[256],{"evidenceId":212,"organization":183,"value":61,"qualifier":199,"claimant":200,"grade":211,"pooled":203},{"low":258,"high":259},15000,157500,[261,289,302,320],{"slug":171,"title":262,"shortTitle":263,"definition":264,"status":9,"industries":265,"functions":269,"patterns":270,"audience":274,"autonomy":275,"adoptionStage":30,"evidenceCount":276,"publicEvidenceCount":276,"organizations":277,"bestGrade":283,"headline":284,"lastVerified":288,"indexable":203},"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.",[18,266,267,268],"government","travel-and-hospitality","professional-services",[22],[271,272,25,273],"conversational-agent","classification-and-routing","agentic-workflow","customer-facing","copilot",5,[278,279,280,281,282],"Chipotle Mexican Grill","Gojob","U.S. Immigration and Customs Enforcement","Mastercard","Trace3","B",{"kpi":40,"label":247,"unit":198,"n":248,"nUpTo":244,"kind":285,"value":286,"qualifier":287,"claimant":230,"organization":281,"vendorReported":184},"reported",90,"approximately","2026-09-27",{"slug":172,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":295,"patterns":296,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":297,"publicEvidenceCount":297,"organizations":298,"bestGrade":283,"headline":213,"lastVerified":288,"indexable":203},"AI internal talent marketplace for matching employees to projects, roles and mentors","Internal talent marketplace","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.",[18,19,294,266],"payments",[22],[24,25],4,[299,281,300,301],"Federal Bureau of Prisons","Schneider Electric","Unilever",{"slug":303,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":309,"patterns":310,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":312,"publicEvidenceCount":312,"organizations":313,"bestGrade":211,"headline":316,"lastVerified":173,"indexable":203},"compensation-and-pay-equity-analysis","AI for compensation governance and pay equity analysis","Pay equity and compensation analysis","AI that statistically analyzes an organization's pay data for unexplained gaps by gender, race or other protected characteristics, and, at the point a recruiter or manager sets a starting salary, a raise or a promotion increase, checks the proposed number against pay bands and equity rules and flags a decision before it creates a new gap, instead of finding it in next year's audit.",[18,20,308],"insurance",[22],[25,311],"anomaly-detection",2,[314,315],"Elevance Health","Salesforce",{"kpi":317,"label":318,"unit":198,"n":248,"nUpTo":244,"kind":285,"value":319,"qualifier":199,"claimant":200,"organization":314,"vendorReported":203},"cost-reduction","Cost reduction",25,{"slug":321,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":326,"patterns":328,"audience":28,"autonomy":275,"adoptionStage":30,"evidenceCount":276,"publicEvidenceCount":276,"organizations":332,"bestGrade":283,"headline":338,"lastVerified":288,"indexable":203},"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.",[18,266,20,19],[22,327],"knowledge-management",[329,330,331],"content-generation","translation","summarization",[333,334,335,336,337],"Carlsberg Group","Internal Revenue Service","U.S. Marshals Service","Veterans Benefits Administration","Zoom",{"kpi":40,"label":247,"unit":198,"n":248,"nUpTo":244,"kind":285,"value":286,"qualifier":199,"claimant":200,"organization":337,"vendorReported":203},{"indexable":203,"reasons":340},[],[342,347,352,359,366,372,378,385,393,399,406,413,419,425,431,437,443,450,456,462,468,475,480,487,492,497,502,508,515,521,528,534,540,547,552,557],{"id":131,"label":343,"issuer":139,"region":140,"url":344,"description":345,"useCases":346,"indexable":203},"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.",230,{"id":132,"label":348,"issuer":139,"region":140,"url":349,"description":350,"useCases":351,"indexable":203},"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.",207,{"id":135,"label":353,"issuer":354,"region":355,"url":356,"description":357,"useCases":358,"indexable":203},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":360,"label":361,"issuer":362,"region":146,"url":363,"description":364,"useCases":365,"indexable":203},"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.",92,{"id":133,"label":367,"issuer":368,"region":140,"url":369,"description":370,"useCases":371,"indexable":203},"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.",71,{"id":373,"label":374,"issuer":139,"region":140,"url":375,"description":376,"useCases":377,"indexable":203},"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":379,"label":380,"issuer":381,"region":140,"url":382,"description":383,"useCases":384,"indexable":203},"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.",50,{"id":386,"label":387,"issuer":388,"region":389,"url":390,"description":391,"useCases":392,"indexable":203},"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.",37,{"id":394,"label":395,"issuer":396,"region":389,"url":397,"description":398,"useCases":319,"indexable":203},"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.",{"id":400,"label":401,"issuer":402,"region":146,"url":403,"description":404,"useCases":405,"indexable":203},"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.",22,{"id":407,"label":408,"issuer":409,"region":355,"url":410,"description":411,"useCases":412,"indexable":203},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":414,"label":415,"issuer":139,"region":140,"url":416,"description":417,"useCases":418,"indexable":203},"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.",17,{"id":420,"label":421,"issuer":422,"region":140,"url":423,"description":424,"useCases":418,"indexable":203},"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":426,"label":427,"issuer":428,"region":146,"url":429,"description":430,"useCases":229,"indexable":203},"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":432,"label":433,"issuer":434,"region":355,"url":435,"description":436,"useCases":60,"indexable":203},"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.",{"id":438,"label":439,"issuer":139,"region":140,"url":440,"description":441,"useCases":442,"indexable":203},"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":444,"label":445,"issuer":446,"region":146,"url":447,"description":448,"useCases":449,"indexable":203},"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":451,"label":452,"issuer":453,"region":146,"url":454,"description":455,"useCases":449,"indexable":203},"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":457,"label":458,"issuer":139,"region":140,"url":459,"description":460,"useCases":461,"indexable":203},"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":463,"label":464,"issuer":465,"region":355,"url":466,"description":467,"useCases":461,"indexable":203},"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":469,"label":470,"issuer":471,"region":146,"url":472,"description":473,"useCases":474,"indexable":203},"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.",11,{"id":476,"label":477,"issuer":139,"region":140,"url":478,"description":479,"useCases":474,"indexable":203},"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.",{"id":481,"label":482,"issuer":483,"region":140,"url":484,"description":485,"useCases":486,"indexable":203},"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.",10,{"id":488,"label":489,"issuer":388,"region":389,"url":490,"description":491,"useCases":486,"indexable":203},"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.",{"id":493,"label":494,"issuer":139,"region":140,"url":495,"description":496,"useCases":486,"indexable":203},"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":498,"label":499,"issuer":139,"region":140,"url":500,"description":501,"useCases":486,"indexable":203},"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":503,"label":504,"issuer":139,"region":140,"url":505,"description":506,"useCases":507,"indexable":203},"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.",9,{"id":509,"label":510,"issuer":511,"region":146,"url":512,"description":513,"useCases":514,"indexable":203},"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.",7,{"id":516,"label":517,"issuer":139,"region":140,"url":518,"description":519,"useCases":520,"indexable":203},"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":522,"label":523,"issuer":524,"region":525,"url":526,"description":527,"useCases":276,"indexable":203},"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":529,"label":530,"issuer":531,"region":140,"url":532,"description":533,"useCases":297,"indexable":203},"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":535,"label":536,"issuer":537,"region":140,"url":538,"description":539,"useCases":297,"indexable":203},"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":541,"label":542,"issuer":543,"region":389,"url":544,"description":545,"useCases":546,"indexable":203},"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.",3,{"id":548,"label":549,"issuer":139,"region":140,"url":550,"description":551,"useCases":546,"indexable":203},"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":553,"label":554,"issuer":139,"region":140,"url":555,"description":556,"useCases":546,"indexable":203},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":134,"label":558,"issuer":559,"region":146,"url":560,"description":561,"useCases":546,"indexable":203},"NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683487107]