[{"data":1,"prerenderedAt":541},["ShallowReactive",2],{"uc-production-scheduling-and-sequencing":3,"uc-regulations":319},{"useCase":4,"evidence":164,"blitsAiDeployments":230,"benchmarks":231,"indicative":237,"related":240,"indexability":317,"includeUnpublished":170},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":41,"macroEstimates":69,"feasibility":70,"implementation":83,"risk":117,"blitsAi":143,"faq":145,"related":158,"datePublished":159,"dateModified":159,"lastVerified":159,"changelog":160,"slug":163},"AI copilots for production scheduling and sequencing","Production scheduling and sequencing","AI production scheduling for factories","Celonis says Mercedes-Benz uses AI copilots to optimize sequencing; Hyundai's AI cart sequencing cut unnecessary production downtime by about 86%.","published","AI that continuously resequences production orders and the material flow that feeds them, from the routing of parts and transfer carts between lines to the order list at a line itself, and flags conflicts as real conditions change: a machine goes down, a rush order lands, a part arrives late. Instead of a planner or operator working out the new sequence by hand, the AI proposes the change for a human to approve.",[12,13,14,15],"AI production scheduling","vehicle sequencing AI","dynamic factory scheduling","enterprise scheduling AI",[17,18],"automotive","manufacturing",[20],"operations",[22,23],"agentic-workflow","prediction-and-scoring",[25],"internal-tools","employee-facing","copilot","early-adopters","production-scheduling","Planning and scheduling usually run in parallel but rarely in sync. Plans are typically created\nat a strategic level while schedules are built on the plant floor, and the two drift apart as\nsoon as reality departs from the plan. That gap widens whenever a machine breaks down, a part is\nlate or an order changes: a schedule that is not resequenced in time means a line runs the wrong\nsequence, changeovers take longer than they need to, and the plan nobody updated becomes the plan\neverybody works around.\n\nMixed model lines make this harder still. Product variety commonly means more frequent\nchangeovers, and a schedule that looked feasible in the morning can be wrong by the afternoon\nonce a machine, a supplier or a rush order changes the picture.\n\nThe same gap reaches upstream, into the material flow that feeds the line. When a machine fault\ndisrupts the route that parts or transfer carts take between stations, someone has to work out a\nnew path by hand before parts keep moving: the intralogistics version of resequencing the\nproduction schedule itself.",[],"1. **Bring planning and scheduling onto one picture.** Order backlog, machine capacity, material\n   availability and shift constraints sit in one place instead of a plan handed to a separate\n   scheduling tool.\n2. **Forecast and flag.** The AI forecasts delivery timelines for orders already in the system\n   and flags where the current sequence will miss a date, waste a changeover or create a\n   downstream bottleneck.\n3. **Recommend a resequence.** When a machine goes down, a rush order lands or material arrives\n   late, the AI proposes an updated sequence, whether that is the order list at a line or the\n   routing of parts and transfer carts feeding it, that respects the written constraints, with\n   the reasoning behind the change.\n4. **A scheduler decides.** A human scheduler reviews the recommendation, adjusts it if it misses\n   something the AI could not see, and approves it before it reaches the floor.\n5. **Simulate before committing.** Schedulers can test a what if scenario, an extra shift, a\n   different changeover order, before it becomes the live plan.\n6. **Feed outcomes back.** What actually happened on the floor updates the constraints and the\n   forecast, so the next recommendation starts from a more accurate picture.",[34,35,36],"speed","cost-to-serve","employee-productivity",[38,39,40],"processing-time-reduction","productivity-gain","cost-savings",{"referenceOrg":42,"inputs":43,"formula":64,"currency":65,"period":66,"resultLabel":67,"caveat":68},"A consumer goods plant running three packaging lines, 300 days a year",[44,51,58],{"key":45,"label":46,"low":47,"high":48,"unit":49,"note":50},"changeoversPerYear","Product changeovers across the plant per year",500,1500,"changeovers per year","Editorial assumption, replace with your own changeover log; varies widely with product mix.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"hoursSavedPerChangeover","Extra line hours unlocked per changeover from tighter sequencing",0.25,1,"hours per changeover","Editorial assumption. C3 AI states 100% production capacity utilization with dynamic scheduling for an anonymized contract manufacturer, with no changeover time figure specifically, so this range is a conservative, editorial estimate in the same direction rather than a number taken from either source on this page.",{"key":59,"label":60,"low":47,"high":61,"unit":62,"note":63},"valuePerLineHour","Value of one extra hour of line capacity",3000,"USD per hour","Editorial assumption, replace with your own contribution margin per line hour.","changeoversPerYear * hoursSavedPerChangeover * valuePerLineHour","USD","per year","Extra production capacity unlocked per year","Counts unlocked capacity at its contribution value only. It leaves out the platform's own cost, the planning team's time, and any change to inventory or service levels from running tighter schedules.",[],{"complexity":71,"complexityNote":72,"dataPrerequisites":73,"integrations":78},"high","The hard part is rarely the optimization itself; it is connecting order, capacity and material data from enterprise resource planning, manufacturing execution and planning systems that were not built to share a live picture, and writing down the constraints schedulers already carry in their heads.",[74,75,76,77],"Order backlog, machine capacity and a changeover matrix per line","Material and component availability by location","A record of past disruptions and how the schedule was changed in response","A written escalation path for conflicts the AI cannot resolve on its own",[79,80,81,82],"Enterprise resource planning and manufacturing execution systems for orders and machine status","An advanced planning and scheduling or process intelligence platform","Inbound material and logistics visibility systems","Shift and workforce planning systems, where scheduling affects staffing",{"steps":84,"guardrails":100,"humanInTheLoop":104,"kpisToInstrument":105,"failureModes":110},[85,88,91,94,97],{"title":86,"detail":87},"Prove it on one line before the network","Pick one line or plant with a clear pain point, frequent changeovers or frequent disruption, and get the recommendation to approval loop working there first.",{"title":89,"detail":90},"Write down the constraints planners actually use","Capture changeover sequences, shift patterns and the informal rules experienced schedulers apply, so the AI's recommendations start from the same limits a person would respect.",{"title":92,"detail":93},"Let it draft, not decide, the first schedule","Have the AI propose the resequence and have the scheduler approve, edit or reject it, and track how often each happens before increasing its scope.",{"title":95,"detail":96},"Track disruptions, not only adherence","Measure how fast a disruption turns into an updated, approved schedule, not only whether the final schedule matched the plan.",{"title":98,"detail":99},"Scale plant by plant","Reuse the constraint model and the integration pattern for the next line or plant, and keep a written record of what changed at each site.",[101,102,103],"Every recommended schedule change shows the constraint, forecast or event behind it, so a scheduler can check it before approving","A human approves any resequence before it changes the live shop floor plan","Safety critical and certified process steps stay outside the AI's authority to resequence or reorder","Production planners and schedulers review every recommended change before it reaches the floor, and adjust the constraints the AI works from whenever a recommendation misses something only they could see.",[106,107,108,109],"Schedule adherence and the number of manual rebuilds needed per week","Changeover time and overall equipment effectiveness, before and after","Lead time between a disruption and an approved, updated schedule","Planner time spent building schedules from scratch versus reviewing recommendations",[111,114],{"title":112,"detail":113},"Recommendations with no visible reasoning","A scheduler who cannot see why a change is proposed will ignore it or approve it blindly. Show the constraint, forecast or event behind every recommendation.",{"title":115,"detail":116},"Optimizing one line while starving the next","A sequence that looks efficient for one line can create a bottleneck downstream. Model the flow across connected lines, not one line in isolation.",{"euAiAct":118,"regulations":121,"guidance":125,"controls":138,"incidents":142},{"tier":119,"basis":120},"context-dependent","Sequencing machines and orders is usually minimal risk. The design decides the tier if the same system also allocates tasks to individual workers based on their individual behaviour or personal traits or characteristics, or monitors and evaluates their performance and behaviour in a work relationship; that use falls under Annex III point 4(b) of the EU AI Act. Keep order and machine sequencing separate from any such worker task allocation or performance evaluation to stay outside that category.",[122,123,124],"eu-ai-act","iso-42001","nist-ai-rmf",[126,132],{"title":127,"issuer":128,"region":129,"url":130,"note":131},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers AI used to allocate tasks based on individual behaviour or personal traits or characteristics, or to monitor and evaluate the performance and behaviour of persons in a work relationship, which a production or machine scheduling system must stay separate from.",{"title":133,"issuer":134,"region":135,"url":136,"note":137},"AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary framework for mapping, measuring and managing the risks of an AI system, useful for a copilot whose recommendations a human always approves.",[139,140,141],"Inventory entry for the scheduling AI with an owner and the lines or plants it covers","Written boundary between order and machine sequencing and any worker task allocation or performance evaluation, kept as a separate, human owned process","Change control and a rollback plan for every new constraint, line or plant added",[],{"howToBuild":144},"On Blits.ai this sits alongside the plant's planning and scheduling system rather than\nreplacing it. A SQL knowledge base holds a live or daily copy of the order backlog, machine\nstatus and changeover data, so an AI agent can answer a scheduler's question about why a\nchange is recommended or what a line's capacity looks like this week, in plain language. A\ncondition triggered agentic task, \"draft a resequencing recommendation when a machine down\nsignal, a new rush order or a late delivery arrives,\" with scheduled rechecks, drafts the\nrecommended change and routes it to the named scheduler for human in the loop approval, with\napprove and reject controls, before anything reaches the shop floor system.\n\nGuardrails keep the agent inside answering questions and drafting recommendations; it never\nwrites a schedule change to the manufacturing execution system without that approval step. A\ncustom function submits the approved change once a scheduler confirms it. Schedulers reach the\nagent through internal tools or a channel such as Microsoft Teams, test suites check that a\nsample of past disruptions still produce a sensible recommendation after every change, monitors\nrun scheduled health checks against the agent and alert by email or webhook if it stops\nresponding, and analytics track how often recommendations are approved, edited or rejected. The\nplatform is model agnostic and can run with EU or UAE data residency for teams that need it.",[146,149,152,155],{"question":147,"answer":148},"Can AI fully automate production scheduling?","Not based on the evidence on this page. Celonis says Mercedes-Benz uses AI copilots to forecast delivery timelines and optimize sequencing, C3 AI describes an anonymized contract manufacturer whose AI evaluates up to 130,000 possible sequences per run so planners can review and adjust the schedule, and Hyundai Motor Group reports that its own reinforcement learning based Transfer Cart Sequencing Optimization Technology calculates optimal cart routing automatically, work employees previously did by hand when a disruption occurred. None of the three sources states that a person must approve every change before it reaches the floor. This use case's own implementation playbook recommends a human scheduler review every recommendation before it goes live, as a guardrail, not because any deployment on this page documents that step.",{"question":150,"answer":151},"How is this different from predictive maintenance?","Predictive maintenance flags when a machine is likely to fail so a repair can be planned. Production scheduling and sequencing decides the order work runs in given the machines, materials and orders available right now. A predictive maintenance alert is one of the events that can trigger a resequence.",{"question":153,"answer":154},"Does the AI decide which worker does which task?","It should not, and keeping that separate matters for risk classification. This use case covers order and machine sequencing; allocating tasks to individual workers based on their behaviour or personal traits, or monitoring and evaluating their performance and behaviour, is a different, higher risk category under the EU AI Act (Annex III point 4(b)) and belongs to a separate, human owned process.",{"question":156,"answer":157},"How fast can a schedule change when something breaks?","As fast as the event reaches the platform and a scheduler can review the recommendation. None of the sources on this page publishes a time figure for that specific step: Hyundai reports a cut of about 86% in unnecessary production downtime from its own cart sequencing AI, but not the time it takes to calculate a new route. Treat any speed claim as directional until you have your own numbers from a pilot.",[],"2026-09-29",[161],{"date":159,"note":162},"First published","production-scheduling-and-sequencing",[165,201],{"title":166,"useCases":167,"organization":168,"vendors":173,"summary":176,"stage":177,"year":178,"channels":179,"languages":180,"metrics":183,"outcomeDisclosed":191,"sources":192,"verification":196,"grade":198,"id":199,"organizationSlug":200},"Hyundai Motor Group: Transfer Cart Sequencing Optimization for production line logistics",[163],{"name":169,"anonymized":170,"country":171,"region":172,"industry":17},"Hyundai Motor Group",false,"KR","asia-pacific",[174],{"name":169,"role":175},"in-house","Hyundai Motor Group built Transfer Cart Sequencing Optimization Technology, reinforcement learning based AI that automatically calculates optimal routing for parts transfer carts moving across its production lines. Previously, when an equipment malfunction disrupted the sequence, employees had to work out and restore the movement routes by hand; the system now combines reinforcement learning with existing optimization algorithms to identify the most efficient routes from many possible scenarios. Hyundai presented the technology alongside other AI manufacturing tools, including its E-FOREST: POLARIS agent platform, at its AX Achievement Showcase in Seoul on August 12, 2026.","production",2026,[25],[181,182],"en","ko",[184],{"kpi":39,"value":185,"unit":186,"qualifier":187,"claimant":188,"quote":189,"sourceUrl":190},86,"percent","approximately","organization","As a result, unnecessary production downtime has been reduced by approximately 86 percent, improving overall operational efficiency.","https://www.hyundai.com/worldwide/en/newsroom/detail/0000001257",true,[193],{"url":190,"title":194,"publisher":169,"date":195},"Hyundai Motor Group Accelerates AI Transformation Across Its Business, Advancing Toward the Physical AI Era","2026-08-12",{"level":197,"checkedAt":159},"source-verified","B","hyundai-motor-group-transfer-cart-sequencing",null,{"title":202,"useCases":203,"organization":204,"vendors":207,"summary":211,"stage":212,"year":213,"channels":214,"languages":215,"metrics":216,"outcomeDisclosed":191,"sources":217,"verification":227,"grade":228,"id":229,"organizationSlug":200},"Mercedes-Benz: AI copilots for order to delivery sequencing across production plants",[163],{"name":205,"anonymized":170,"country":206,"region":129,"industry":17},"Mercedes-Benz Group AG","DE",[208],{"name":209,"role":210},"Celonis","platform","Mercedes-Benz connected data from its major production and logistics systems on the Celonis Process Intelligence Platform, on top of its own MO360 manufacturing platform, giving the company visibility across every order, part and process. Celonis reports that, across more than 30 global production plants, this has helped Mercedes-Benz improve on time delivery, speed up decisions and make efficiency gains. Celonis describes AI copilots that, in order to delivery operations, forecast delivery timelines, optimize sequencing and reduce delays; the same platform also flags bottlenecks in service parts logistics and anomalies in quality data, according to Celonis.","scaled",2025,[25],[181],[],[218,222],{"url":219,"title":220,"publisher":209,"date":221},"https://www.celonis.com/news/press/mercedes-benz-accelerates-ai-driven-transformation-with-celonis","Mercedes-Benz accelerates AI driven transformation with Celonis","2025-11-04",{"url":223,"title":224,"publisher":225,"date":226},"https://www.automotivelogistics.media/digitalisation/mercedesbenz-boosts-production-and-logistics-visibility-with-celonis/2129820","Mercedes-Benz boosts production and logistics visibility with Celonis","Automotive Logistics and Supply Chain","2025-11-05",{"level":197,"checkedAt":159},"C","mercedes-benz-order-to-delivery-sequencing",0,[232],{"kpi":39,"label":233,"unit":186,"aggregate":191,"higherIsBetter":191,"n":55,"nUpTo":230,"median":185,"min":185,"max":185,"byClaimant":234,"vendorOnly":170,"points":235},"Productivity gain",{"organization":55,"vendor":230,"regulator":230,"independent":230},[236],{"evidenceId":199,"organization":169,"value":185,"qualifier":187,"claimant":188,"grade":198,"pooled":191},{"low":238,"high":239},62500,4500000,[241,258,282,301],{"slug":242,"title":243,"shortTitle":244,"definition":245,"status":9,"industries":246,"functions":247,"patterns":249,"audience":251,"autonomy":252,"adoptionStage":28,"segment":253,"evidenceCount":254,"publicEvidenceCount":254,"organizations":255,"bestGrade":228,"headline":200,"lastVerified":159,"indexable":191},"dealer-aftersales-retention-agent","AI agent for dealer service retention and aftersales outreach","Dealer service retention agent","An AI agent that continuously mines a dealer's own service records for each vehicle's factory intervals, declined work, open recalls and lapsed visits, reaches the owner by text or email at the right moment, answers what is due, and books the appointment in the same conversation, so a lean service team keeps more of the visits it would otherwise lose to time or a competitor.",[17],[248,20],"customer-service",[250,22,23],"conversational-agent","customer-facing","supervised-agent","aftersales",2,[256,257],"Fred Anderson Toyota","Murfreesboro Nissan",{"slug":259,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":264,"patterns":265,"audience":26,"autonomy":252,"adoptionStage":28,"segment":177,"evidenceCount":269,"publicEvidenceCount":269,"organizations":270,"bestGrade":198,"headline":274,"lastVerified":281,"indexable":191},"production-line-quality-inspection","AI quality inspection on the production line","Production quality inspection","AI that inspects every unit on a production line, from camera images, sound or machine process data, to find defects, missing parts and wrong variants in real time, and routes the few anomalies it flags to a quality inspector instead of relying on manual sampling at the end of the line.",[18,17],[20],[266,267,268,22],"computer-vision","anomaly-detection","synthetic-data-generation",3,[271,272,273],"Audi","BMW Group","Pegatron",{"kpi":275,"label":276,"unit":186,"n":55,"nUpTo":230,"kind":277,"value":278,"qualifier":279,"claimant":280,"organization":273,"vendorReported":191},"cost-reduction","Cost reduction","reported",7,"exact","vendor","2026-09-27",{"slug":283,"title":284,"shortTitle":285,"definition":286,"status":9,"industries":287,"functions":288,"patterns":291,"audience":26,"autonomy":293,"adoptionStage":28,"segment":294,"evidenceCount":254,"publicEvidenceCount":254,"organizations":295,"bestGrade":228,"headline":298,"lastVerified":159,"indexable":191},"supply-chain-disruption-monitoring","AI supply chain risk and disruption monitoring","Supply chain risk monitoring","AI that watches transport lanes, weather, strikes, ports and the suppliers behind a manufacturer's own suppliers for signs of a coming disruption, turns scattered news and sensor signals into one validated alert per event, and gives planners enough lead time to reroute, expedite or substitute before the disruption reaches production or the customer.",[17,18],[289,20,290],"procurement","risk-management",[267,23,292],"summarization","assist","supply-chain-risk",[296,297],"Schaeffler","Schneider Electric",{"kpi":39,"label":233,"unit":186,"n":55,"nUpTo":230,"kind":277,"value":299,"qualifier":300,"claimant":280,"organization":296,"vendorReported":191},50,"at-least",{"slug":302,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":309,"patterns":311,"audience":26,"autonomy":252,"adoptionStage":28,"segment":313,"evidenceCount":269,"publicEvidenceCount":254,"organizations":314,"bestGrade":228,"headline":200,"lastVerified":281,"indexable":191},"fraud-alert-triage","AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[307,308],"banking","payments",[310,20],"fraud-prevention",[22,312,292,23],"classification-and-routing","middle-office",[315,316],"Coast","SEB",{"indexable":191,"reasons":318},[],[320,325,331,338,342,349,355,361,368,375,382,389,395,401,408,415,421,428,434,440,446,453,458,465,470,475,480,486,492,498,506,513,519,525,530,535],{"id":122,"label":321,"issuer":128,"region":129,"url":322,"description":323,"useCases":324,"indexable":191},"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":326,"label":327,"issuer":128,"region":129,"url":328,"description":329,"useCases":330,"indexable":191},"gdpr","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":123,"label":332,"issuer":333,"region":334,"url":335,"description":336,"useCases":337,"indexable":191},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":124,"label":339,"issuer":134,"region":135,"url":136,"description":340,"useCases":341,"indexable":191},"NIST AI Risk Management Framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",92,{"id":343,"label":344,"issuer":345,"region":129,"url":346,"description":347,"useCases":348,"indexable":191},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":350,"label":351,"issuer":128,"region":129,"url":352,"description":353,"useCases":354,"indexable":191},"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":356,"label":357,"issuer":358,"region":129,"url":359,"description":360,"useCases":299,"indexable":191},"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.",{"id":362,"label":363,"issuer":364,"region":172,"url":365,"description":366,"useCases":367,"indexable":191},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":369,"label":370,"issuer":371,"region":172,"url":372,"description":373,"useCases":374,"indexable":191},"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":376,"label":377,"issuer":378,"region":135,"url":379,"description":380,"useCases":381,"indexable":191},"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":383,"label":384,"issuer":385,"region":334,"url":386,"description":387,"useCases":388,"indexable":191},"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":390,"label":391,"issuer":128,"region":129,"url":392,"description":393,"useCases":394,"indexable":191},"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":396,"label":397,"issuer":398,"region":129,"url":399,"description":400,"useCases":394,"indexable":191},"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":402,"label":403,"issuer":404,"region":135,"url":405,"description":406,"useCases":407,"indexable":191},"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.",16,{"id":409,"label":410,"issuer":411,"region":334,"url":412,"description":413,"useCases":414,"indexable":191},"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":416,"label":417,"issuer":128,"region":129,"url":418,"description":419,"useCases":420,"indexable":191},"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":422,"label":423,"issuer":424,"region":135,"url":425,"description":426,"useCases":427,"indexable":191},"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":429,"label":430,"issuer":431,"region":135,"url":432,"description":433,"useCases":427,"indexable":191},"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":435,"label":436,"issuer":128,"region":129,"url":437,"description":438,"useCases":439,"indexable":191},"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":441,"label":442,"issuer":443,"region":334,"url":444,"description":445,"useCases":439,"indexable":191},"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":447,"label":448,"issuer":449,"region":135,"url":450,"description":451,"useCases":452,"indexable":191},"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":454,"label":455,"issuer":128,"region":129,"url":456,"description":457,"useCases":452,"indexable":191},"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":459,"label":460,"issuer":461,"region":129,"url":462,"description":463,"useCases":464,"indexable":191},"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":466,"label":467,"issuer":364,"region":172,"url":468,"description":469,"useCases":464,"indexable":191},"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":471,"label":472,"issuer":128,"region":129,"url":473,"description":474,"useCases":464,"indexable":191},"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":476,"label":477,"issuer":128,"region":129,"url":478,"description":479,"useCases":464,"indexable":191},"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":481,"label":482,"issuer":128,"region":129,"url":483,"description":484,"useCases":485,"indexable":191},"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":487,"label":488,"issuer":489,"region":135,"url":490,"description":491,"useCases":278,"indexable":191},"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.",{"id":493,"label":494,"issuer":128,"region":129,"url":495,"description":496,"useCases":497,"indexable":191},"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":499,"label":500,"issuer":501,"region":502,"url":503,"description":504,"useCases":505,"indexable":191},"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.",5,{"id":507,"label":508,"issuer":509,"region":129,"url":510,"description":511,"useCases":512,"indexable":191},"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.",4,{"id":514,"label":515,"issuer":516,"region":129,"url":517,"description":518,"useCases":512,"indexable":191},"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":520,"label":521,"issuer":522,"region":172,"url":523,"description":524,"useCases":269,"indexable":191},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":526,"label":527,"issuer":128,"region":129,"url":528,"description":529,"useCases":269,"indexable":191},"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":531,"label":532,"issuer":128,"region":129,"url":533,"description":534,"useCases":269,"indexable":191},"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":536,"label":537,"issuer":538,"region":135,"url":539,"description":540,"useCases":269,"indexable":191},"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.",1790683489541]