[{"data":1,"prerenderedAt":565},["ShallowReactive",2],{"uc-employee-attrition-prediction-and-retention-analytics":3,"uc-regulations":342},{"useCase":4,"evidence":177,"blitsAiDeployments":247,"benchmarks":248,"indicative":260,"related":263,"indexability":340,"includeUnpublished":183},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":35,"indicativeValue":38,"macroEstimates":71,"feasibility":72,"implementation":85,"risk":126,"blitsAi":153,"faq":155,"related":168,"datePublished":172,"dateModified":172,"lastVerified":172,"changelog":173,"slug":176},"AI for employee attrition prediction and retention analytics","Attrition prediction and retention analytics","AI employee attrition prediction software","AI flags employees at risk of quitting before they resign. IBM says its model is about 95% accurate and saved it nearly $300 million in retention costs.","published","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.",[12,13,14,15],"employee churn prediction","flight risk model","predictive attrition","turnover prediction AI",[17,18],"cross-industry","technology",[20],"human-resources",[22],"prediction-and-scoring",[24],"internal-tools","employee-facing","assist","early-adopters","By the time an employee formally resigns, the decision is often close to final, and the manager\nfinds out at the point retention options are narrowest. Exit interviews describe why people left,\nbut only after they are gone, so the same pattern repeats with the next person on the team. HR\nteams add engagement surveys and stay interviews, but neither reaches every employee often enough\nto catch a change in risk as it happens.\n\nReplacing an employee is also expensive in ways that rarely show up in one line of a budget:\nrecruiting cost, the vacancy itself, ramp time for a replacement, and the knowledge that leaves with\nthe person. A model that scores risk continuously, from data the organization already has, turns\nretention from a reaction into something a manager can act on before the resignation letter.",[],"1. **Assemble the signal.** The model draws on tenure, role, compensation relative to the market and\n   to peers, manager changes, performance ratings, promotion history, engagement survey responses\n   and, where available, internal mobility activity.\n2. **Score risk.** Each employee gets a risk score and the factors behind it (for example, below\n   market pay for the role, a recent change of manager, or a stalled promotion), refreshed on a\n   regular cycle rather than once a year.\n3. **Route to a person, not a dashboard alone.** The manager or HR business partner sees which\n   specific employees are at risk and why, so a stay conversation or a compensation review can happen\n   before the person is already interviewing elsewhere.\n4. **Validate and adjust.** The model's predictions are checked against who actually leaves, stays or\n   is promoted, and retrained; workforce planning uses the aggregate pattern to see where attrition\n   risk is concentrated by team, level or location.",[32,33,34],"risk-reduction","cost-to-serve","employee-productivity",[36,37],"cost-savings","accuracy",{"referenceOrg":39,"inputs":40,"formula":66,"currency":67,"period":68,"resultLabel":69,"caveat":70},"A company with 10,000 employees and 12% annual voluntary turnover",[41,46,52,59],{"key":42,"label":43,"low":44,"high":44,"unit":42,"note":45},"employees","Employees",10000,"The reference company.",{"key":47,"label":48,"low":49,"high":49,"unit":50,"note":51},"baselineTurnover","Baseline annual voluntary turnover rate",0.12,"fraction of employees","Editorial assumption, replace with your own turnover rate.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"reducibleShare","Share of voluntary departures a timely intervention can prevent",0.05,0.1,"fraction of voluntary departures","Editorial assumption, conservative against Visier's reported 10% reduction in truck driver turnover at Pitney Bowes, https://www.visier.com/customers/pitney-bowes/, since that result is for one role from a broader people analytics rollout, not a per employee risk score applied to the whole workforce.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"costPerDeparture","Fully loaded cost of an avoidable voluntary departure (recruiting, vacancy, ramp time)",15000,40000,"USD per departure","Editorial assumption, varies widely by role; replace with your own cost model.","employees * baselineTurnover * reducibleShare * costPerDeparture","USD","per year","Avoidable turnover cost prevented","Assumes a share of voluntary departures are genuinely preventable with a timely, well targeted intervention; many departures are not (relocation, retirement, a role that no longer exists). It leaves out the cost of running the model, the risk of employees reacting badly to being scored, and any effect of retention actions on people who were never actually at risk.",[],{"complexity":73,"complexityNote":74,"dataPrerequisites":75,"integrations":80},"medium","The modelling itself is standard machine learning on structured HR data. The harder parts are data quality across systems that were never built to talk to each other, and change management: managers have to act on the score, and the organization needs a policy for how the score is shown and used so it does not feel like it decided who gets fired.",[76,77,78,79],"Clean, joined HR, payroll, performance and engagement survey data over enough history to model against actual departures","A defined population and time horizon for what counts as a resignation the model should predict","Manager and HR business partner training on what the score means and does not mean","A retention playbook describing what a manager is expected to do with a high risk score",[81,82,83,84],"HR information system for tenure, role, compensation and performance data","Engagement or pulse survey platform","Payroll, for compensation relative to market and peers","Manager facing dashboard or existing people analytics tool",{"steps":86,"guardrails":102,"humanInTheLoop":107,"kpisToInstrument":108,"failureModes":113},[87,90,93,96,99],{"title":88,"detail":89},"Define what the model predicts and for whom","Decide the population, the horizon (for example resignation risk in the next six months) and whether the model runs company wide from the start or is piloted in one function first.",{"title":91,"detail":92},"Build from data the organization already has","Do not launch a new mandatory survey to feed the model. Start with HR, payroll and performance data that already exists, and add engagement signals the organization already collects.",{"title":94,"detail":95},"Give managers the reason, not just the score","A bare risk number invites either panic or dismissal. Connect the score to specific, addressable drivers, such as below market pay for the role, a recent change of manager or a stalled promotion, so the manager has something concrete to raise in a stay conversation.",{"title":97,"detail":98},"Validate before it drives action at scale","Check the model's predictions against actual departures, promotions and stays for at least one full cycle before managers are expected to act on it, and keep validating after launch.",{"title":100,"detail":101},"Decide how employees are told, if at all","Decide, before launch, whether attrition scoring is disclosed as a workforce planning tool and whether an individual employee is ever told their own score. Make this a deliberate policy, with legal and works council input where required, rather than a default.",[103,104,105,106],"The model never triggers an employment action (termination, non promotion) on its own; a person always decides what, if anything, to do with a risk score","No protected characteristics or obvious proxies for them used as model features","Regular fairness testing of scores and any resulting actions across employee groups","Retention offers and stay conversations documented, so an unusual pattern can be reviewed","HR and the manager decide whether and how to act on a risk score; the model surfaces risk and drivers, it does not decide pay, promotion or termination. HR owns model validation, fairness testing and the policy on what employees are told.",[109,110,111,112],"Precision and recall of the model against actual departures, tracked over time","Voluntary turnover rate for flagged versus unflagged employees after an intervention","Manager action rate on flagged employees (did a conversation actually happen)","Fairness metrics across employee groups for both scores and outcomes",[114,117,120,123],{"title":115,"detail":116},"The model is accurate but nobody acts on it","A dashboard nobody opens changes nothing. Route flagged employees to a specific person with a specific expected action and a deadline, and track whether it happened.",{"title":118,"detail":119},"Employees learn they are being scored and disengage further","A leaked or mishandled disclosure that scoring exists can itself damage trust. Decide the communication policy deliberately, with legal and employee representative input.",{"title":121,"detail":122},"Compensation ends up as the only lever","It is easiest to retain someone with a raise, but a raise driven by a flight risk score can create its own pay equity problem. Coordinate flight risk retention actions with the organization's pay equity process rather than running them separately.",{"title":124,"detail":125},"The model drifts as the business changes","A model trained before a reorganization, a new competitor or a change in the labour market can quietly stop predicting well. Revalidate on a fixed schedule, not only when someone notices it is wrong.",{"euAiAct":127,"regulations":130,"guidance":136,"controls":147,"incidents":152},{"tier":128,"basis":129},"high","Annex III point 4(b) lists AI systems intended to monitor and evaluate the performance and behaviour of workers as high risk, and scoring an employee's likelihood of leaving from their performance, compensation and engagement data is that kind of evaluation, even though the output feeds a retention conversation rather than a punitive action. Deployers must inform workers' representatives and affected employees before use (Article 26) and keep human oversight over any action taken on the score.",[131,132,133,134,135],"eu-ai-act","gdpr","uk-gdpr","iso-42001","nist-ai-rmf",[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 covers employment, workers' management and access to self employment, including monitoring and evaluating performance and behaviour.",{"title":144,"issuer":139,"region":140,"url":145,"note":146},"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.",[148,149,150,151],"Named HR owner for the model, its validation and its fairness testing","Documented policy on whether and how employees are told scoring exists","Retention actions and stay conversations logged for review","Coordination with the pay equity process before compensation is used as a retention lever",[],{"howToBuild":154},"Blits.ai does not build the predictive model itself; that stays with the organization's people\nanalytics or HR platform, which already holds the tenure, performance and compensation history the\nscore needs. What Blits.ai builds is the layer that gets the score to a manager and turns it into\naction: an **agent** inside **Microsoft Teams** or the manager's own tools that explains, in plain\nlanguage, why an employee is flagged, using a **custom function** that reads the risk factors from\nthe analytics platform's API rather than the raw score alone.\n\nAn **agentic workflow** with **human in the loop approval** creates a tracked stay conversation\ntask for the manager with a follow up reminder, instead of leaving the flag to sit in a dashboard,\nand a **knowledge base** gives managers the organization's own retention playbook for the specific\ndriver flagged (pay, workload, career progression). **Role based access control** and **per bot\npermissions** restrict who can see an individual score to the manager and HR business partner it is\nmeant for, and the workflow's **run history and analytics** track how often a flagged conversation\nactually happens, for HR's own reporting.",[156,159,162,165],{"question":157,"answer":158},"What results have companies reported from attrition prediction?","IBM's then CEO Ginni Rometty said in 2019 that the company's AI could predict which employees were about to leave with about 95% accuracy and had saved IBM nearly $300 million in retention costs. Vendors of broader people analytics platforms report retention gains too: Visier says Pitney Bowes cut truck driver turnover by 10% after using its platform to understand what was driving departures, though that result is about people analytics generally rather than an individual attrition score.",{"question":160,"answer":161},"Does the model decide who gets a raise or is let go?","No. It scores risk and surfaces the likely drivers; a manager or HR business partner decides whether and how to act, whether that is a stay conversation, a role change or nothing at all.",{"question":163,"answer":164},"Is attrition scoring high risk under the EU AI Act?","Generally yes. Scoring an employee's likelihood of leaving from their performance, pay and engagement data is a form of monitoring and evaluating worker behaviour under Annex III point 4(b), which brings the usual obligations: human oversight, logging and informing workers' representatives before use.",{"question":166,"answer":167},"Should employees be told their own risk score?","There is no single right answer. Decide it deliberately, as a policy on whether the score is an internal workforce planning signal or something an employee can see about themselves, with legal and, where applicable, works council input before launch, rather than leaving it to default.",[169,170,171],"hr-and-policy-assistant","internal-talent-marketplace-matching","performance-review-drafting-agent","2026-09-29",[174],{"date":172,"note":175},"First published","employee-attrition-prediction-and-retention-analytics",[178,222],{"title":179,"useCases":180,"organization":181,"vendors":186,"summary":189,"stage":190,"year":191,"channels":192,"languages":193,"metrics":195,"outcomeDisclosed":208,"sources":209,"verification":217,"grade":219,"id":220,"organizationSlug":221},"IBM: predictive attrition program saves retention costs",[176],{"name":182,"anonymized":183,"country":184,"region":185,"industry":18},"IBM",false,"US","north-america",[187],{"name":182,"role":188},"in-house","IBM built its own \"predictive attrition program\" using AI on internal HR data to flag employees at risk of resigning. Then CEO Ginni Rometty described the results publicly at a CNBC @Work conference in April 2019: the model was in the 95% accuracy range at identifying workers planning to leave, and the program had saved IBM nearly $300 million in retention costs.","production",2019,[24],[194],"en",[196,203],{"kpi":37,"value":197,"unit":198,"qualifier":199,"claimant":200,"quote":201,"sourceUrl":202},95,"percent","approximately","organization","IBM artificial intelligence technology is now 95 percent accurate in predicting workers who are planning to leave their jobs, said Rometty.","https://www.cnbc.com/2019/04/03/ibm-ai-can-predict-with-95-percent-accuracy-which-employees-will-quit.html",{"kpi":36,"value":204,"unit":205,"currency":67,"qualifier":199,"claimant":200,"quote":206,"sourceUrl":207},300000000,"currency","AI has so far saved IBM nearly $300 million in retention costs.","https://www.cnbc.com/2019/04/05/excerpts-from-work-talent-hr-conference.html",true,[210,214],{"url":202,"title":211,"publisher":212,"date":213},"IBM AI can predict with 95 percent accuracy which employees will quit","CNBC","2019-04-03",{"url":207,"title":215,"publisher":212,"date":216},"Excerpts from @Work Talent + HR Conference","2019-04-05",{"level":218,"checkedAt":172},"source-verified","B","ibm-watson-predictive-attrition","ibm",{"title":223,"useCases":224,"organization":225,"vendors":227,"summary":231,"stage":190,"year":191,"channels":232,"languages":233,"metrics":234,"outcomeDisclosed":208,"sources":235,"verification":243,"grade":244,"id":245,"organizationSlug":246},"Pitney Bowes: Visier people analytics cuts driver turnover",[176],{"name":226,"anonymized":183,"country":184,"region":185,"industry":18},"Pitney Bowes",[228],{"name":229,"role":230},"Visier","platform","Pitney Bowes, a global shipping and mailing technology company, adopted Visier's people analytics platform to analyze, identify and predict issues across the employee lifecycle. Visier reports a 10% reduction in truck driver turnover and over 400 self service people analytics users, and Pitney Bowes' VP of Total Rewards and HR Technology said the platform let the company change how it onboards and engages new hires to improve retention. The platform gives a general view of retention drivers across the workforce; neither Visier nor Pitney Bowes describes it scoring an individual employee's risk of leaving.",[24],[194],[],[236,239],{"url":237,"title":238,"publisher":229},"https://www.visier.com/customers/pitney-bowes/","Pitney Bowes | Visier",{"url":240,"title":241,"publisher":229,"date":242},"https://www.visier.com/company/news/pitney-bowes-improves-retention-with-visier-people/","Pitney Bowes Improves Retention with Visier People","2019-01-15",{"level":218,"checkedAt":172},"C","pitney-bowes-visier-turnover-reduction",null,0,[249,255],{"kpi":37,"label":250,"unit":198,"aggregate":208,"higherIsBetter":208,"n":251,"nUpTo":247,"median":197,"min":197,"max":197,"byClaimant":252,"vendorOnly":183,"points":253},"Accuracy",1,{"organization":251,"vendor":247,"regulator":247,"independent":247},[254],{"evidenceId":220,"organization":182,"value":197,"qualifier":199,"claimant":200,"grade":219,"pooled":208},{"kpi":36,"label":256,"unit":205,"currency":67,"aggregate":183,"higherIsBetter":208,"n":251,"nUpTo":247,"median":204,"min":204,"max":204,"byClaimant":257,"vendorOnly":183,"points":258},"Cost savings",{"organization":251,"vendor":247,"regulator":247,"independent":247},[259],{"evidenceId":220,"organization":182,"value":204,"qualifier":199,"claimant":200,"grade":219,"pooled":208},{"low":261,"high":262},900000,4800000,[264,292,308,327],{"slug":169,"title":265,"shortTitle":266,"definition":267,"status":9,"industries":268,"functions":271,"patterns":273,"audience":25,"autonomy":277,"adoptionStage":27,"evidenceCount":278,"publicEvidenceCount":279,"organizations":280,"bestGrade":219,"headline":284,"lastVerified":291,"indexable":208},"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.",[17,269,18,270],"banking","healthcare",[20,272],"knowledge-management",[274,275,276],"rag-knowledge-assistant","conversational-agent","agentic-workflow","supervised-agent",5,4,[281,182,282,283],"Bank of America","Turing","Vituity",{"kpi":285,"label":286,"unit":198,"n":287,"nUpTo":247,"kind":288,"value":289,"qualifier":290,"claimant":200,"organization":182,"vendorReported":183},"employee-adoption","Employee adoption",2,"reported",99,"exact","2026-09-27",{"slug":170,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":300,"patterns":301,"audience":25,"autonomy":26,"adoptionStage":27,"evidenceCount":279,"publicEvidenceCount":279,"organizations":303,"bestGrade":219,"headline":246,"lastVerified":291,"indexable":208},"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.",[17,297,298,299],"manufacturing","payments","government",[20],[302,22],"recommendation-and-personalization",[304,305,306,307],"Federal Bureau of Prisons","Mastercard","Schneider Electric","Unilever",{"slug":171,"title":309,"shortTitle":310,"definition":311,"status":9,"industries":312,"functions":313,"patterns":314,"audience":25,"autonomy":317,"adoptionStage":27,"evidenceCount":287,"publicEvidenceCount":287,"organizations":318,"bestGrade":244,"headline":321,"lastVerified":326,"indexable":208},"AI agent for drafting employee performance reviews","Performance review drafting","An assistant that gathers an employee's work history, goals and peer feedback from the systems a manager already uses, and drafts a first version of the performance review for the manager to edit, rewrite or reject, so the manager starts from a grounded summary instead of a blank form and a stack of six months of context to recall from memory.",[17,18],[20],[315,316,276],"content-generation","summarization","copilot",[319,320],"Case Status","Rho",{"kpi":322,"label":323,"unit":198,"n":251,"nUpTo":247,"kind":288,"value":324,"qualifier":290,"claimant":325,"organization":319,"vendorReported":208},"processing-time-reduction","Cycle time reduction",84,"vendor","2026-09-28",{"slug":328,"title":329,"shortTitle":330,"definition":331,"status":9,"industries":332,"functions":333,"patterns":334,"audience":25,"autonomy":26,"adoptionStage":27,"evidenceCount":287,"publicEvidenceCount":287,"organizations":335,"bestGrade":244,"headline":338,"lastVerified":172,"indexable":208},"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.",[17,297,18],[20],[302,22],[336,337],"Box","Forvia",{"kpi":322,"label":323,"unit":198,"n":251,"nUpTo":247,"kind":288,"value":339,"qualifier":290,"claimant":200,"organization":336,"vendorReported":183},16,{"indexable":208,"reasons":341},[],[343,348,353,360,366,372,378,385,393,400,407,414,420,426,432,439,445,452,458,464,470,477,482,489,494,499,504,510,517,523,530,536,542,549,554,559],{"id":131,"label":344,"issuer":139,"region":140,"url":345,"description":346,"useCases":347,"indexable":208},"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":349,"issuer":139,"region":140,"url":350,"description":351,"useCases":352,"indexable":208},"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":354,"issuer":355,"region":356,"url":357,"description":358,"useCases":359,"indexable":208},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":135,"label":361,"issuer":362,"region":185,"url":363,"description":364,"useCases":365,"indexable":208},"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":208},"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":208},"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":208},"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":208},"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":399,"indexable":208},"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":401,"label":402,"issuer":403,"region":185,"url":404,"description":405,"useCases":406,"indexable":208},"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":408,"label":409,"issuer":410,"region":356,"url":411,"description":412,"useCases":413,"indexable":208},"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":415,"label":416,"issuer":139,"region":140,"url":417,"description":418,"useCases":419,"indexable":208},"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":421,"label":422,"issuer":423,"region":140,"url":424,"description":425,"useCases":419,"indexable":208},"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":427,"label":428,"issuer":429,"region":185,"url":430,"description":431,"useCases":339,"indexable":208},"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":433,"label":434,"issuer":435,"region":356,"url":436,"description":437,"useCases":438,"indexable":208},"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":440,"label":441,"issuer":139,"region":140,"url":442,"description":443,"useCases":444,"indexable":208},"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":446,"label":447,"issuer":448,"region":185,"url":449,"description":450,"useCases":451,"indexable":208},"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":453,"label":454,"issuer":455,"region":185,"url":456,"description":457,"useCases":451,"indexable":208},"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":459,"label":460,"issuer":139,"region":140,"url":461,"description":462,"useCases":463,"indexable":208},"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":465,"label":466,"issuer":467,"region":356,"url":468,"description":469,"useCases":463,"indexable":208},"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":471,"label":472,"issuer":473,"region":185,"url":474,"description":475,"useCases":476,"indexable":208},"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":478,"label":479,"issuer":139,"region":140,"url":480,"description":481,"useCases":476,"indexable":208},"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":483,"label":484,"issuer":485,"region":140,"url":486,"description":487,"useCases":488,"indexable":208},"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":490,"label":491,"issuer":388,"region":389,"url":492,"description":493,"useCases":488,"indexable":208},"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":495,"label":496,"issuer":139,"region":140,"url":497,"description":498,"useCases":488,"indexable":208},"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":500,"label":501,"issuer":139,"region":140,"url":502,"description":503,"useCases":488,"indexable":208},"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":505,"label":506,"issuer":139,"region":140,"url":507,"description":508,"useCases":509,"indexable":208},"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":511,"label":512,"issuer":513,"region":185,"url":514,"description":515,"useCases":516,"indexable":208},"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":518,"label":519,"issuer":139,"region":140,"url":520,"description":521,"useCases":522,"indexable":208},"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":524,"label":525,"issuer":526,"region":527,"url":528,"description":529,"useCases":278,"indexable":208},"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":531,"label":532,"issuer":533,"region":140,"url":534,"description":535,"useCases":279,"indexable":208},"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":537,"label":538,"issuer":539,"region":140,"url":540,"description":541,"useCases":279,"indexable":208},"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":543,"label":544,"issuer":545,"region":389,"url":546,"description":547,"useCases":548,"indexable":208},"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":550,"label":551,"issuer":139,"region":140,"url":552,"description":553,"useCases":548,"indexable":208},"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":555,"label":556,"issuer":139,"region":140,"url":557,"description":558,"useCases":548,"indexable":208},"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":560,"label":561,"issuer":562,"region":185,"url":563,"description":564,"useCases":548,"indexable":208},"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.",1790683490510]