[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-compensation-and-pay-equity-analysis":3,"uc-regulations":331},{"useCase":4,"evidence":175,"blitsAiDeployments":236,"benchmarks":237,"indicative":244,"related":247,"indexability":329,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":40,"macroEstimates":74,"feasibility":75,"implementation":88,"risk":126,"blitsAi":152,"faq":154,"related":167,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI for compensation governance and pay equity analysis","Pay equity and compensation analysis","AI pay equity analysis for compensation teams","AI checks each pay decision against equity rules. Syndio reports Elevance Health cut remediation costs 25%, Salesforce held them flat as headcount roughly tripled.","published","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.",[12,13,14,15],"AI pay equity software","compensation equity analysis","pay gap analysis AI","proactive pay equity",[17,18,19],"cross-industry","technology","insurance",[21],"human-resources",[23,24],"prediction-and-scoring","anomaly-detection",[26],"internal-tools","employee-facing","assist","early-adopters","A typical pay equity audit is a periodic statistical regression over the whole workforce that finds\ngaps after they exist. Between audits, new hire offers, merit increases and promotions can each\ncreate or widen a gap, and the only remedy is a remediation budget to close them after the fact.\nElevance Health's compensation team described the result before it changed its process: \"Managers\ndidn't know what they didn't know,\" so they proposed offers that were competitive with market rates\nbut not always in line with internal pay equity.\n\nRegulation is also moving from annual reporting to real time obligations. The EU Pay Transparency\nDirective requires employers to give candidates pay information before the interview or otherwise\nbefore the contract, and requires larger employers to report gender pay gaps, with a mandatory joint\npay assessment when a gap of at least 5% in any category of workers is not justified and not\nremedied within six months. A compensation team that only checks equity once a year finds out about\na breach after it has already made the disclosures the law requires to be accurate.",[],"1. **Baseline analysis.** The system runs a regression across the current workforce, grouping\n   employees doing substantially similar work, and controls for legitimate factors such as\n   experience, performance and location to isolate any unexplained gap by gender, race or other\n   protected characteristic.\n2. **Remediation modelling.** For every gap found, it models the cost and equity impact of closing\n   it in different ways (a targeted raise, phased over cycles, by level or by location) so the\n   compensation team can budget and prioritize.\n3. **Decision time check.** When a recruiter enters a candidate's proposed starting salary, or a\n   manager enters a raise or promotion increase, the system returns the equitable range for that\n   role, level and location in the same workflow, before the offer or increase is finalized.\n4. **Continuous monitoring.** Every governed decision is logged, so the next baseline analysis\n   starts from a smaller, better documented gap instead of rediscovering the same disparities.",[34,35,36],"risk-reduction","compliance","employee-productivity",[38,39],"cost-reduction","employee-adoption",{"referenceOrg":41,"inputs":42,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"An employer with 20,000 employees governing 25,000 pay decisions a year",[43,49,56,63],{"key":44,"label":45,"low":46,"high":46,"unit":47,"note":48},"payDecisions","Pay decisions governed per year (new hires, raises, promotions)",25000,"decisions per year","Editorial assumption scaled from Syndio's reported 102,000 pay decisions a year for Salesforce's roughly 80,000 employees, https://synd.io/case-study/salesforce/.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"shareWithGap","Share of decisions that would otherwise create or widen an unexplained pay gap",0.02,0.03,"fraction of decisions","Editorial assumption, replace with your own baseline audit findings.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"avgRemediationPerGap","Average later remediation cost per decision that created a gap",800,1000,"USD per decision","Editorial assumption. The high end is kept so total baseline remediation for the reference organization stays near Salesforce's reported pattern of about $3 million a year for about 80,000 employees, roughly $37 per employee per year, https://synd.io/case-study/salesforce/. Replace with your own remediation data.",{"key":64,"label":65,"low":66,"high":66,"unit":67,"note":68},"avoidedShare","Share of that later remediation avoided by catching the decision upfront",0.25,"fraction","Syndio reports that Elevance Health cut pay equity remediation costs 25% after adopting decision time pay guidance, https://synd.io/wp-content/uploads/2024/01/01_2024_Case-Study_Elevance-Main.pdf.","payDecisions * shareWithGap * avgRemediationPerGap * avoidedShare","USD","per year","Remediation cost avoided","Counts only avoided remediation spend on pay decisions. It leaves out legal and reputational risk from an unremedied gap, recruiter and compensation team time, the software cost, and any effect on offer acceptance or retention.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":83},"medium","The statistical method is well established, the harder part is data: getting clean, comparable compensation, role and demographic data across countries with different pay data rules, and getting the decision time check into the actual hiring and pay review workflow so managers use it instead of working around it.",[79,80,81,82],"Compensation, job level and location data for every employee, kept current","A job architecture that groups roles doing substantially similar work","Demographic data collected and stored under local law, with a lawful basis for equity analysis","Pay bands and a policy for how to remediate a confirmed gap",[84,85,86,87],"HR information system for compensation and job data","Applicant tracking system, to check offers before they are extended","Compensation planning and merit review tools, to check raises and promotions","Payroll, for the final governed pay figure",{"steps":89,"guardrails":105,"humanInTheLoop":110,"kpisToInstrument":111,"failureModes":116},[90,93,96,99,102],{"title":91,"detail":92},"Run the baseline analysis before anything else","Start with a full regression across the current workforce, with legal counsel involved so the analysis and its findings are handled correctly, before any decision time check goes live.",{"title":94,"detail":95},"Put the check at the point of the decision","Elevance Health's recruiters look up the equitable range in Syndio's Pay Finder before making an offer, at the point the decision is made, rather than as a separate report checked later.",{"title":97,"detail":98},"Set a remediation policy in advance","Decide how a confirmed gap gets closed, immediately, at the next cycle, over how many cycles, before the analysis finds one, so a real finding does not stall on a policy debate.",{"title":100,"detail":101},"Train managers on what the range means","A recommended equitable range is not a hiring decision. Explain to managers and recruiters why it exists and what to do when a strong candidate wants more than the range supports.",{"title":103,"detail":104},"Rerun the baseline on a fixed cycle","Reanalyze at least yearly, and whenever a new pay transparency or reporting obligation applies in a country the organization operates in, so disclosures stay accurate.",[106,107,108,109],"Every remediation decision is approved by the compensation team, not applied automatically","Protected characteristics are used only inside the statistical analysis, never as a matching feature that changes an individual's recommended range","Access to individual level results limited to compensation and legal roles","Methodology and grouping logic documented and available for audit","The compensation team owns the methodology, approves every remediation, and decides how to close a confirmed gap. Recruiters and managers see a recommended range but make the actual offer or raise decision; nothing changes an employee's pay without a person approving it.",[112,113,114,115],"Remediation cost per cycle, and per employee as headcount grows","Share of new pay decisions that fall inside the equitable range without an override","Unexplained gap size at each baseline analysis, trended over time","Time from a confirmed gap to remediation",[117,120,123],{"title":118,"detail":119},"Managers override the range without review","A recommended range that is easy to ignore gets ignored under hiring pressure. Log every override with a reason and review the pattern, not just the individual case.",{"title":121,"detail":122},"Job architecture hides real differences","Grouping roles too broadly compares people doing genuinely different work and produces a false gap, or too narrowly hides a real one. Have compensation and legal review the groupings, not just the statistics.",{"title":124,"detail":125},"Demographic data is incomplete or unlawfully collected","Analysis needs demographic data the organization may not lawfully hold everywhere. Confirm the lawful basis and data quality per country before analyzing, and disclose gaps in coverage.",{"euAiAct":127,"regulations":130,"guidance":135,"controls":146,"incidents":151},{"tier":128,"basis":129},"context-dependent","The tier depends on what the range is used for. When it materially influences an individual hiring offer, it falls under Annex III point 4(a) (recruitment); when it materially influences an individual raise or promotion decision, it falls under point 4(b) (decisions affecting the terms of a work relationship). A recommended range that a recruiter or manager can accept or override, as this use case is designed, still counts as materially influencing that decision. A separate system that only produces an aggregate regression report for the compensation team, with no individual recommendation, is either outside Annex III on that narrower use or falls under Article 6(3) as a preparatory task that detects deviations from prior decision patterns rather than a high risk use in itself. Deployers of the high risk part must inform workers' representatives and affected employees before use (Article 26).",[131,132,133,134],"eu-ai-act","gdpr","uk-gdpr","iso-42001",[136,142],{"title":137,"issuer":138,"region":139,"url":140,"note":141},"Directive (EU) 2023/970 on pay transparency","European Union","europe","https://eur-lex.europa.eu/eli/dir/2023/970/oj","Requires employers to give candidates pay information, such as in a job vacancy notice, before the interview or otherwise before the contract. Requires employers with at least 100 workers to report gender pay gaps, with a mandatory joint pay assessment when a gap of at least 5% in any category of workers is not justified by objective, gender neutral criteria and not remedied within six months. Member states had to transpose it by 7 June 2026; EUR-Lex blocks automated fetches, so this cites the Wayback copy.",{"title":143,"issuer":138,"region":139,"url":144,"note":145},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 4 covers employment, workers' management and access to self employment, including decisions affecting the terms of a work relationship.",[147,148,149,150],"Legal counsel involved in the analysis methodology and any remediation","Named compensation owner for the model, its groupings and its overrides","Country by country review of what demographic data may lawfully be collected and analyzed","Documented, auditable methodology, separate from the vendor's proprietary scoring",[],{"howToBuild":153},"Blits.ai is not a pay equity statistics engine and does not run the regression; that stays with a\nspecialist compensation platform or the organization's own model. What Blits.ai builds well is the\nworkflow around it. A **custom function** calls the compensation platform's API for the equitable\nrange at the point a recruiter or manager is about to submit an offer or raise, inside an **agent**\nembedded in the applicant tracking or HR system, and an **agentic workflow** with **human in the\nloop approval** routes anything outside the range to the compensation team instead of blocking the\nmanager outright.\n\nA **knowledge base** answers manager and recruiter questions about the policy itself (\"why does\nthis role have this range\") from the organization's own compensation and pay transparency\ndocuments, so the compensation team is not fielding the same question by email. **Guardrails** keep\nprotected characteristics out of anything the agent surfaces to a manager, and the **agentic\nworkflow's** run history and audit trail, together with **analytics**, track override rates and\ntime to remediation for the compensation team's own reporting, with **EU and UAE data residency**\nfor organizations that must keep compensation data in region.",[155,158,161,164],{"question":156,"answer":157},"What results do companies report from decision time pay equity checks?","Syndio reports that Elevance Health cut remediation costs 25% after putting Pay Finder in front of more than 200 recruiters, checking each candidate's proposed salary before an offer goes out. Syndio also reports that Salesforce held its roughly $3 million annual remediation cost steady while governing about 102,000 pay decisions a year and roughly tripling its headcount; Syndio's case study attributes this to pay staying corrected through later merit cycles and promotions, though it does not describe a check at the point of each individual offer or raise.",{"question":159,"answer":160},"Is this different from an annual pay equity audit?","An annual audit still matters for the aggregate baseline and for reporting, but it only finds gaps after new ones have already been created. A decision time check catches a specific offer or raise before it goes out, so fewer gaps reach the next audit.",{"question":162,"answer":163},"Does the AI decide who gets what raise?","No. It analyzes patterns and recommends a range; the compensation team, recruiter or manager makes the actual pay decision and can document a reason to go outside the range.",{"question":165,"answer":166},"Is pay equity analysis high risk under the EU AI Act?","It depends on the design. A range that materially influences an individual hiring offer, raise or promotion falls under Annex III point 4(a) or 4(b). A separate tool that only produces an aggregate regression report for the compensation team, with no individual recommendation, is either outside Annex III on that use or a preparatory task under Article 6(3).",[168,169],"hr-and-policy-assistant","recruitment-screening-and-interview-scheduling","2026-09-29",[172],{"date":170,"note":173},"First published","compensation-and-pay-equity-analysis",[176,206],{"title":177,"useCases":178,"organization":179,"vendors":184,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":194,"outcomeDisclosed":195,"sources":196,"verification":201,"grade":203,"id":204,"organizationSlug":205},"Salesforce: pay equity governance at scale with Syndio",[174],{"name":180,"anonymized":181,"country":182,"region":183,"industry":18},"Salesforce",false,"US","north-america",[185],{"name":186,"role":187},"Syndio","platform","Salesforce, with about 80,000 employees, governs about 102,000 pay decisions a year, and Syndio's platform manages the complexity of that analysis across more than 100 countries. Benioff first took a public stand on pay equity in 2015. Salesforce initially ran pay equity analysis in house with a model built by its own data science team, and later replaced it with Syndio's platform as the company scaled.","scaled",2026,[26],[193],"en",[],true,[197],{"url":198,"title":199,"publisher":186,"archivedUrl":200},"https://synd.io/case-study/salesforce/","Salesforce + Syndio | Pay Equity at Scale as Workforce Tripled","https://web.archive.org/web/20260609032347/https://synd.io/case-study/salesforce/",{"level":202,"checkedAt":170},"source-verified","C","salesforce-pay-equity-governance",null,{"title":207,"useCases":208,"organization":209,"vendors":211,"summary":213,"stage":189,"year":214,"channels":215,"languages":216,"metrics":217,"outcomeDisclosed":195,"sources":231,"verification":234,"grade":203,"id":235,"organizationSlug":205},"Elevance Health: Pay Finder cuts remediation costs at the point of hire",[174],{"name":210,"anonymized":181,"country":182,"region":183,"industry":19},"Elevance Health",[212],{"name":186,"role":187},"Elevance Health, a US health insurer, partnered with Syndio in 2020 for a gender and race pay equity analysis and began using Syndio's Pay Finder tool in 2021 so that more than 200 internal and external recruiters can check a candidate's proposed salary against an equitable, competitive range before an offer goes out, instead of only auditing pay equity after the fact.",2023,[26],[193],[218,225,229],{"kpi":38,"value":219,"unit":220,"qualifier":221,"claimant":222,"quote":223,"sourceUrl":224},25,"percent","exact","vendor","Pay Finder helped Elevance Health maintain pay equity with every new starting salary, resulting in a 25% reduction in remediation costs.","https://synd.io/wp-content/uploads/2024/01/01_2024_Case-Study_Elevance-Main.pdf",{"kpi":226,"value":227,"unit":220,"qualifier":221,"claimant":222,"quote":228,"sourceUrl":224},"processing-time-reduction",6,"This resulted in a 6% decrease in time to fill and 6% increase in offer acceptance rate.",{"kpi":230,"value":227,"unit":220,"qualifier":221,"claimant":222,"quote":228,"sourceUrl":224},"conversion-rate-uplift",[232],{"url":224,"title":233,"publisher":186},"Elevance Health Uses Pay Finder to Maintain Equitable Pay",{"level":202,"checkedAt":170},"elevance-health-pay-finder-remediation",0,[238],{"kpi":38,"label":239,"unit":220,"aggregate":195,"higherIsBetter":195,"n":240,"nUpTo":236,"median":219,"min":219,"max":219,"byClaimant":241,"vendorOnly":195,"points":242},"Cost reduction",1,{"organization":236,"vendor":240,"regulator":236,"independent":236},[243],{"evidenceId":235,"organization":210,"value":219,"qualifier":221,"claimant":222,"grade":203,"pooled":195},{"low":245,"high":246},100000,187500,[248,277,300,315],{"slug":168,"title":249,"shortTitle":250,"definition":251,"status":9,"industries":252,"functions":255,"patterns":257,"audience":27,"autonomy":261,"adoptionStage":29,"evidenceCount":262,"publicEvidenceCount":263,"organizations":264,"bestGrade":269,"headline":270,"lastVerified":276,"indexable":195},"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.",[17,253,18,254],"banking","healthcare",[21,256],"knowledge-management",[258,259,260],"rag-knowledge-assistant","conversational-agent","agentic-workflow","supervised-agent",5,4,[265,266,267,268],"Bank of America","IBM","Turing","Vituity","B",{"kpi":39,"label":271,"unit":220,"n":272,"nUpTo":236,"kind":273,"value":274,"qualifier":221,"claimant":275,"organization":266,"vendorReported":181},"Employee adoption",2,"reported",99,"organization","2026-09-27",{"slug":169,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":285,"patterns":286,"audience":288,"autonomy":289,"adoptionStage":29,"evidenceCount":262,"publicEvidenceCount":262,"organizations":290,"bestGrade":269,"headline":296,"lastVerified":276,"indexable":195},"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.",[17,282,283,284],"government","travel-and-hospitality","professional-services",[21],[259,287,23,260],"classification-and-routing","customer-facing","copilot",[291,292,293,294,295],"Chipotle Mexican Grill","Gojob","U.S. Immigration and Customs Enforcement","Mastercard","Trace3",{"kpi":226,"label":297,"unit":220,"n":240,"nUpTo":236,"kind":273,"value":298,"qualifier":299,"claimant":275,"organization":294,"vendorReported":181},"Cycle time reduction",90,"approximately",{"slug":301,"title":302,"shortTitle":303,"definition":304,"status":9,"industries":305,"functions":307,"patterns":308,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":272,"publicEvidenceCount":272,"organizations":310,"bestGrade":203,"headline":313,"lastVerified":170,"indexable":195},"ai-candidate-sourcing-and-talent-rediscovery","AI agent for candidate sourcing and talent rediscovery","AI candidate sourcing and rediscovery","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.",[17,306,18],"manufacturing",[21],[309,23],"recommendation-and-personalization",[311,312],"Box","Forvia",{"kpi":226,"label":297,"unit":220,"n":240,"nUpTo":236,"kind":273,"value":314,"qualifier":221,"claimant":275,"organization":311,"vendorReported":181},16,{"slug":316,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":321,"patterns":322,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":272,"publicEvidenceCount":272,"organizations":323,"bestGrade":269,"headline":325,"lastVerified":170,"indexable":195},"employee-attrition-prediction-and-retention-analytics","AI for employee attrition prediction and retention analytics","Attrition prediction and retention analytics","Machine learning that scores each employee's risk of resigning from HR, performance, engagement and compensation data, surfaces the factors driving that risk for a manager or HR business partner, and feeds an aggregate view of where attrition is concentrated, so retention effort goes to the people and teams most likely to leave instead of being spread evenly or applied only after someone resigns.",[17,18],[21],[23],[266,324],"Pitney Bowes",{"kpi":326,"label":327,"unit":220,"n":240,"nUpTo":236,"kind":273,"value":328,"qualifier":299,"claimant":275,"organization":266,"vendorReported":181},"accuracy","Accuracy",95,{"indexable":195,"reasons":330},[],[332,337,342,349,356,362,368,375,383,389,396,403,409,415,421,428,434,441,447,453,459,466,471,478,483,488,493,499,506,511,518,524,530,537,542,547],{"id":131,"label":333,"issuer":138,"region":139,"url":334,"description":335,"useCases":336,"indexable":195},"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":338,"issuer":138,"region":139,"url":339,"description":340,"useCases":341,"indexable":195},"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":134,"label":343,"issuer":344,"region":345,"url":346,"description":347,"useCases":348,"indexable":195},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":350,"label":351,"issuer":352,"region":183,"url":353,"description":354,"useCases":355,"indexable":195},"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":357,"issuer":358,"region":139,"url":359,"description":360,"useCases":361,"indexable":195},"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":363,"label":364,"issuer":138,"region":139,"url":365,"description":366,"useCases":367,"indexable":195},"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":369,"label":370,"issuer":371,"region":139,"url":372,"description":373,"useCases":374,"indexable":195},"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":376,"label":377,"issuer":378,"region":379,"url":380,"description":381,"useCases":382,"indexable":195},"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":384,"label":385,"issuer":386,"region":379,"url":387,"description":388,"useCases":219,"indexable":195},"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":390,"label":391,"issuer":392,"region":183,"url":393,"description":394,"useCases":395,"indexable":195},"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":397,"label":398,"issuer":399,"region":345,"url":400,"description":401,"useCases":402,"indexable":195},"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":404,"label":405,"issuer":138,"region":139,"url":406,"description":407,"useCases":408,"indexable":195},"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":410,"label":411,"issuer":412,"region":139,"url":413,"description":414,"useCases":408,"indexable":195},"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":416,"label":417,"issuer":418,"region":183,"url":419,"description":420,"useCases":314,"indexable":195},"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":422,"label":423,"issuer":424,"region":345,"url":425,"description":426,"useCases":427,"indexable":195},"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":429,"label":430,"issuer":138,"region":139,"url":431,"description":432,"useCases":433,"indexable":195},"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":435,"label":436,"issuer":437,"region":183,"url":438,"description":439,"useCases":440,"indexable":195},"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":442,"label":443,"issuer":444,"region":183,"url":445,"description":446,"useCases":440,"indexable":195},"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":448,"label":449,"issuer":138,"region":139,"url":450,"description":451,"useCases":452,"indexable":195},"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":454,"label":455,"issuer":456,"region":345,"url":457,"description":458,"useCases":452,"indexable":195},"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":460,"label":461,"issuer":462,"region":183,"url":463,"description":464,"useCases":465,"indexable":195},"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":467,"label":468,"issuer":138,"region":139,"url":469,"description":470,"useCases":465,"indexable":195},"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":472,"label":473,"issuer":474,"region":139,"url":475,"description":476,"useCases":477,"indexable":195},"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":479,"label":480,"issuer":378,"region":379,"url":481,"description":482,"useCases":477,"indexable":195},"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":484,"label":485,"issuer":138,"region":139,"url":486,"description":487,"useCases":477,"indexable":195},"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":489,"label":490,"issuer":138,"region":139,"url":491,"description":492,"useCases":477,"indexable":195},"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":494,"label":495,"issuer":138,"region":139,"url":496,"description":497,"useCases":498,"indexable":195},"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":500,"label":501,"issuer":502,"region":183,"url":503,"description":504,"useCases":505,"indexable":195},"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":507,"label":508,"issuer":138,"region":139,"url":509,"description":510,"useCases":227,"indexable":195},"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":512,"label":513,"issuer":514,"region":515,"url":516,"description":517,"useCases":262,"indexable":195},"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":519,"label":520,"issuer":521,"region":139,"url":522,"description":523,"useCases":263,"indexable":195},"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":525,"label":526,"issuer":527,"region":139,"url":528,"description":529,"useCases":263,"indexable":195},"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":531,"label":532,"issuer":533,"region":379,"url":534,"description":535,"useCases":536,"indexable":195},"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":538,"label":539,"issuer":138,"region":139,"url":540,"description":541,"useCases":536,"indexable":195},"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":543,"label":544,"issuer":138,"region":139,"url":545,"description":546,"useCases":536,"indexable":195},"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":548,"label":549,"issuer":550,"region":183,"url":551,"description":552,"useCases":536,"indexable":195},"nyc-local-law-144","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.",1790683490299]