[{"data":1,"prerenderedAt":541},["ShallowReactive",2],{"uc-port-call-and-vessel-operations-optimization":3,"uc-regulations":313},{"useCase":4,"evidence":144,"blitsAiDeployments":223,"benchmarks":224,"indicative":231,"related":234,"indexability":311,"includeUnpublished":150},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":22,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":35,"indicativeValue":38,"macroEstimates":67,"feasibility":68,"implementation":81,"risk":114,"blitsAi":126,"faq":128,"related":138,"datePublished":139,"dateModified":139,"lastVerified":139,"changelog":140,"slug":143},"AI port call optimization for vessel arrival, berth and turnaround planning","Port call and vessel operations optimization","AI port call optimization for vessels","PortXchange's Synchronizer platform predicts vessel arrival times so ships sail just in time. Shell reports up to 20 percent less departure waiting time.","published","AI that predicts a vessel's arrival time and how long each service during a port call, from pilotage to loading, will take, then shares one timeline with the ship, the terminal and nautical service providers so a vessel can sail at the speed that gets it there just in time instead of arriving early and anchoring.",[12,13,14,15],"port call optimization","just in time vessel arrival","vessel arrival forecasting","port call synchronization",[17],"logistics-and-transportation",[19],"operations",[21],"prediction-and-scoring",[23,24],"internal-tools","api","employee-facing","assist","early-adopters","maritime","A vessel that sails at full speed to arrive as scheduled can still end up waiting, because berth,\npilot or nautical service capacity is not ready the moment it arrives. Robin de Puij, Ocean Network\nExpress's Head of Operations in Rotterdam, describes the cost this way: vessels that need to wait\nand end up anchoring cost a lot of money, and sailing too fast to arrive early results in both that\nwaiting time and unnecessarily high fuel consumption. At Shell's Europoort Terminal in Rotterdam the\nproblem showed up differently: a baseline measurement found vessels waited an average of 210 minutes\nafter finishing cargo operations before they could depart, because the terminal and the shipping\nagent each planned from their own view of the schedule. More broadly, every party involved in a\nport call, the shipping line, the terminal, the pilots, tugs and bunkering companies, has\ntraditionally planned from its own view of the schedule, so no one party can see, or fix, the\ndelay building up across the whole call.",[],"1. **Build one shared timeline per port call.** Public vessel tracking data is combined with data\n   shared directly by the shipping line, the terminal and nautical service providers into a single\n   timeline for that vessel's visit.\n2. **Predict arrival and service times.** PortXchange, the platform behind both deployments on this\n   page, describes its Synchronizer product's arrival predictions as built on a tree based machine\n   learning model trained on vessel position and route data.\n3. **Recommend a just in time speed.** The vessel gets a recommended arrival window so it can sail\n   at a slower, more fuel efficient speed instead of rushing to arrive early and then anchoring.\n4. **Coordinate the terminal and services around the same timeline.** Berth planners, pilots, tugs\n   and bunkering companies work from the same shared timeline so capacity is ready when the vessel\n   actually arrives.\n5. **Measure waiting and turnaround time.** Anchoring time, berth turnaround time and schedule\n   adherence are tracked per port call against the period before the platform was used.",[33,34],"cost-to-serve","speed",[36,37],"processing-time-reduction","energy-savings",{"referenceOrg":39,"inputs":40,"formula":62,"currency":63,"period":64,"resultLabel":65,"caveat":66},"A shipping line with 350 port calls a year at a major container port",[41,48,55],{"key":42,"label":43,"low":44,"high":45,"unit":46,"note":47},"portCalls","Port calls per year at the optimized port",200,500,"port calls per year","Editorial assumption, set around the roughly 350 calls a year Ocean Network Express reports at the Port of Rotterdam.",{"key":49,"label":50,"low":51,"high":52,"unit":53,"note":54},"costPerHourWaiting","Cost of a vessel waiting or anchoring per hour, including charter and fuel",1000,4000,"USD per hour","Editorial assumption for a mid sized container or tanker vessel; replace with your own charter and fuel cost.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"hoursSavedPerCall","Departure idle time avoided per port call",0.25,0.7,"hours per port call","Based on the only baseline this page has: a measurement of vessels waiting an average of 210 minutes after cargo operations at Shell's Europoort Terminal before Synchronizer, and Shell's own reported reduction of up to 20% in departure waiting time. 20% of 210 minutes is about 42 minutes, 0.7 hours, so that is the high end; the low end allows for a smaller reduction at other terminals or vessel types, and for the fact that the only quantified result on this page is from a liquid bulk terminal while the reference organization here is a container line.","portCalls * costPerHourWaiting * hoursSavedPerCall","USD","per year","Waiting and anchoring cost avoided across a year of port calls","Waiting and anchoring cost only. It leaves out the platform and integration cost, the value of fuel saved by sailing at a more efficient speed, and any benefit to the terminal or port itself; neither deployment on this page reports a single dollar figure that this calculation could be checked against.",[],{"complexity":69,"complexityNote":70,"dataPrerequisites":71,"integrations":76},"medium","The prediction models are a well understood forecasting problem once vessel tracking data is available; the harder part is getting the terminal, pilots, tugs, bunkering companies and the shipping line to all share their own timing data into the same platform, since the forecast only improves as more of them do.",[72,73,74,75],"Vessel positions and estimated arrival times from an automatic identification system feed","Terminal, berth and nautical service schedules shared by the parties involved in each call","Historical port call data by vessel type and terminal","Weather and tidal data affecting the vessel's approach",[77,78,79,80],"Port community system or a dedicated port call data platform","Terminal operating system","Vessel traffic and automatic identification system data feed","Shipping line voyage planning system",{"steps":82,"guardrails":98,"humanInTheLoop":102,"kpisToInstrument":103,"failureModes":107},[83,86,89,92,95],{"title":84,"detail":85},"Start with one shipping line and one terminal relationship","PortXchange's account of Ocean Network Express's adoption in Rotterdam describes its Rotterdam operations team working alongside ONE's London scheduling team on the platform, while the company's Singapore head office followed the work with interest; neither source says ONE rolled the platform out across its wider network.",{"title":87,"detail":88},"Get vessel tracking and terminal timing into one timeline first","A shared, continuously updated timeline for arrival, pilotage, berthing and departure is the foundation; the just in time speed recommendation only becomes trustworthy once that timeline is reliable.",{"title":90,"detail":91},"Bring nautical service providers into the same platform","Bunkering, towage and waste collection times shape when a vessel can actually leave a berth; a timeline that only covers the terminal misses a real source of departure delay.",{"title":93,"detail":94},"Recommend a speed, do not set one","Give the vessel's master and the shipping line's planners a recommended arrival window and let them decide the actual sailing speed and route, since safety and weather judgment stay with the ship.",{"title":96,"detail":97},"Measure at the level of the whole port, not one vessel","Track anchoring time, berth turnaround and schedule adherence across all participating vessels, since optimizing one ship's arrival can simply move a delay onto another one if the wider port picture is ignored.",[99,100,101],"Nautical safety rules and pilotage requirements always take precedence over a recommended arrival window","A vessel's master and the port authority confirm the final approach plan; the platform only recommends it","Data shared by one party in the timeline is used only for coordinating that port call, under agreed data sharing terms","Vessel planners, port agents and the vessel's master review the recommended arrival window and service schedule, and pilots and terminal planners confirm the final berth and service plan before the vessel changes speed or course.",[104,105,106],"Anchoring or waiting time per port call, before and after joining the platform","Berth turnaround time and schedule adherence","Fuel burned or emissions per port call",[108,111],{"title":109,"detail":110},"A timeline only one party keeps updated","The forecast is only as good as the data shared into it; a terminal or service provider that does not update its own timing breaks the plan for every other party relying on it.",{"title":112,"detail":113},"Optimizing one vessel at the cost of the queue","Bringing one vessel in earlier can crowd a berth or a pilot slot another vessel needs; plan and measure at the level of the whole port, not one ship at a time.",{"euAiAct":115,"regulations":118,"guidance":121,"controls":122,"incidents":125},{"tier":116,"basis":117},"minimal","Annex III point 2 lists critical infrastructure sectors that carry specific obligations: digital infrastructure, road traffic, and the supply of water, gas, heating or electricity. Port call and vessel arrival scheduling is not one of the sectors listed there, and the system does not decide on a natural person's access to an essential service, so it carries no specific EU AI Act obligation beyond the Article 4 AI literacy duty that applies to all providers and deployers. A port that later applies the same kind of model to safety critical vessel traffic management should reassess the tier for that specific use.",[119,120],"eu-ai-act","nis2",[],[123,124],"Data sharing agreements that state which timeline data each party may see and use, and for how long","Annual review of forecast accuracy against actual port call outcomes, shared with the participating parties",[],{"howToBuild":127},"The vessel arrival and service time forecasting model is a specialist maritime data problem;\nBlits.ai is not where you build that forecasting engine. What Blits.ai adds is the layer the\npeople coordinating a port call use around it: an **AI agent** with a **SQL knowledge base** over\nthe shared port call timeline lets a port agent or a shipping line's planner ask, in plain\nlanguage, when a specific vessel is expected, or which of its services are running behind, instead\nof checking several systems by hand.\n\nAn **agentic workflow** can draft a berth confirmation or a change notice to a terminal or\nnautical service provider once a timeline update crosses a threshold, with **human in the loop**\nconfirmation before anything is sent. A **voice, web chat and WhatsApp** agent can answer routine\narrival and berth questions from truckers, agents or nautical service providers from the same\napproved data, with **human handover** to a port agent for anything outside the script, and the\nplatform's **MCP** and **custom function** integrations connect to the port community system or\nterminal operating system that already holds the timeline.",[129,132,135],{"question":130,"answer":131},"Does the AI control the vessel's speed?","No. Design it so the platform surfaces predicted timing and a recommended arrival window, and lets the vessel's master, the shipping line's own planners and the port's nautical service providers decide the actual speed, route and berth plan. Safety and pilotage judgment should always stay with the people responsible for the vessel, not the software.",{"question":133,"answer":134},"What results have shipping companies reported?","Shell reported reducing the waiting time for its departing vessels by up to 20% after a pilot at its Europoort Terminal, where vessels had waited an average of 210 minutes after cargo operations before departing. Ocean Network Express, which adopted the same underlying platform for its Rotterdam port calls, reports early improvements in waiting time without giving a checked figure, so treat that second result as directionally positive rather than quantified.",{"question":136,"answer":137},"Does this only work for one port?","No, but the quantified result on this page is from Rotterdam. PortXchange reports that since the pilot, Shell has been using the Synchronizer platform across different terminals, geographies and business units internally, though no number has been published for that wider use. The underlying platform began as Pronto, built by the Port of Rotterdam Authority, which spun it out into the separate company PortXchange in 2019 specifically to offer it to ports worldwide, naming Shell as one of its first partners for that expansion.",[],"2026-09-29",[141],{"date":139,"note":142},"First published","port-call-and-vessel-operations-optimization",[145,179],{"title":146,"useCases":147,"organization":148,"vendors":153,"summary":157,"stage":158,"year":159,"channels":160,"languages":161,"metrics":163,"outcomeDisclosed":150,"sources":164,"verification":174,"grade":176,"id":177,"organizationSlug":178},"Ocean Network Express: port call optimization with PortXchange Synchronizer in Rotterdam",[143],{"name":149,"anonymized":150,"country":151,"region":152,"industry":17},"Ocean Network Express (ONE)",false,"SG","asia-pacific",[154],{"name":155,"role":156},"PortXchange (Port of Rotterdam Authority)","platform","Ocean Network Express, one of the world's largest container shipping lines with around 350 calls a year at the Port of Rotterdam, ran a two month trial of the PortXchange platform (formerly known as Pronto, a development of the Port of Rotterdam Authority) in 2019 to monitor, analyse and optimize the approach and handling of its vessels there, then continued using it afterwards. Its Rotterdam operations team, led by Robin de Puij, worked alongside ONE's London vessel scheduling team on the platform's shared timeline to plan just in time arrival and to track the time between the end of terminal operations and a vessel's departure, aiming to reduce both anchoring time and the time spent in port after cargo work finishes.","production",2019,[23,24],[162],"en",[],[165,169],{"url":166,"title":167,"publisher":168},"https://port-xchange.com/pilots/improve-just-in-time-sailing-reduce-waiting-time","ONE aims to reduce waiting time with Synchronizer","PortXchange",{"url":170,"title":171,"publisher":172,"archivedUrl":173},"https://www.porttechnology.org/news/one-continues-using-portxchange-after-successful-trial-in-rotterdam/","ONE continues using PortXchange after successful trial in Rotterdam","Port Technology International","http://web.archive.org/web/20250515003509/https://www.porttechnology.org/news/one-continues-using-portxchange-after-successful-trial-in-rotterdam/",{"level":175,"checkedAt":139},"source-verified","C","one-port-call-optimization-rotterdam",null,{"title":180,"useCases":181,"organization":182,"vendors":187,"summary":190,"stage":191,"year":192,"channels":193,"languages":194,"metrics":195,"outcomeDisclosed":204,"sources":205,"verification":221,"grade":176,"id":222,"organizationSlug":178},"Shell: reduced departure waiting time with PortXchange Synchronizer",[143],{"name":183,"anonymized":150,"country":184,"region":185,"industry":186},"Shell","GB","europe","energy-and-utilities",[188],{"name":189,"role":156},"PortXchange Synchronizer (originally Pronto, Port of Rotterdam Authority)","Shell's shipping and maritime business piloted PortXchange Synchronizer, the platform that began as Pronto, built by the Port of Rotterdam Authority and spun out as the separate company PortXchange in 2019, to close the gap between the end of cargo operations and departure at its Europoort Terminal in Rotterdam, where a baseline measurement found vessels waiting an average of 210 minutes before leaving. Shell Europoort Terminal, the Loodswezen pilots and the Vopak agency shared their planning data on the platform, which PortXchange describes as using a machine learning model to predict vessel arrival times and which sends notifications and warnings of planning conflicts before they occur. PortXchange reports that since the pilot, Shell has been using the Synchronizer platform across different terminals, geographies and business units internally.","scaled",2018,[23,24],[162],[196],{"kpi":36,"value":197,"unit":198,"qualifier":199,"period":200,"claimant":201,"quote":202,"sourceUrl":203},20,"percent","up-to","pilot phase, reported 18 April 2018","organization","We have reduced the waiting time up to 20% for departing ships.","https://port-xchange.com/case-studies/reduce-idle-time-on-departure-liquid-bulk/",true,[206,208,211,216],{"url":203,"title":207,"publisher":168},"How Shell has reduced idle time on departure",{"url":209,"title":210,"publisher":168},"https://port-xchange.com/blog/ai-revolutionizes-the-maritime-industry/","AI Revolutionizes the Maritime Industry: Navigating the Future with Data and Technology",{"url":212,"title":213,"publisher":214,"date":215},"https://www.maritimeprofessional.com/news/port-rotterdam-authority-launches-pronto-316497","Port of Rotterdam Authority Launches New Pronto Application","Maritime Professional","2018-04-18",{"url":217,"title":218,"publisher":219,"date":220},"https://www.portofrotterdam.com/en/news-and-press-releases/port-rotterdam-authority-launches-new-company-portxchange-make-digital","Port of Rotterdam Authority launches new company PortXchange to make digital shipping app Pronto available to ports worldwide","Port of Rotterdam Authority","2019-08-08",{"level":175,"checkedAt":139},"shell-port-call-idle-time-reduction",0,[225],{"kpi":36,"label":226,"unit":198,"aggregate":204,"higherIsBetter":204,"n":223,"nUpTo":227,"median":178,"min":178,"max":178,"byClaimant":228,"vendorOnly":150,"points":229},"Cycle time reduction",1,{"organization":223,"vendor":223,"regulator":223,"independent":223},[230],{"evidenceId":222,"organization":183,"value":197,"qualifier":199,"claimant":201,"grade":176,"pooled":150},{"low":232,"high":233},50000,1400000,[235,261,277,293],{"slug":236,"title":237,"shortTitle":238,"definition":239,"status":9,"industries":240,"functions":241,"patterns":242,"audience":25,"autonomy":246,"adoptionStage":27,"segment":247,"evidenceCount":248,"publicEvidenceCount":248,"organizations":249,"bestGrade":253,"headline":254,"lastVerified":260,"indexable":204},"freight-dispatch-and-load-matching-agent","AI agent for freight dispatch and load matching","Freight dispatch and load matching","An AI agent that does the coordination work behind moving a truckload: reading an emailed quote request and replying with a price, ranking which loads to show which carriers, matching pickup and delivery details to an open dock appointment slot, and chasing the exceptions, so a broker's or carrier's own staff plan lanes and handle disputes instead of typing quotes and making appointment calls.",[17],[19],[243,244,245,21],"agentic-workflow","recommendation-and-personalization","document-processing","supervised-agent","freight-brokerage",3,[250,251,252],"C.H. Robinson","J.B. Hunt Transport Services","Uber Freight","B",{"kpi":255,"label":256,"unit":198,"n":227,"nUpTo":223,"kind":257,"value":258,"qualifier":259,"claimant":201,"organization":252,"vendorReported":150},"conversion-rate-uplift","Conversion uplift","reported",12,"exact","2026-09-28",{"slug":262,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":267,"patterns":269,"audience":25,"autonomy":26,"adoptionStage":27,"segment":272,"evidenceCount":273,"publicEvidenceCount":273,"organizations":274,"bestGrade":253,"headline":178,"lastVerified":260,"indexable":204},"freight-rail-rolling-stock-predictive-maintenance","AI predictive maintenance for freight rail rolling stock","Rail rolling stock predictive maintenance","Machine vision and machine learning that inspect freight railcar wheels, bearings and other running gear as trains pass wayside sensors and camera portals at track speed, learn what a healthy wheel or a healthy reading looks like, and flag the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.",[17],[19,268],"field-service",[270,271,21],"computer-vision","anomaly-detection","mechanical-and-safety",2,[275,276],"BNSF Railway","Norfolk Southern",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":284,"patterns":286,"audience":288,"autonomy":246,"adoptionStage":27,"segment":289,"evidenceCount":273,"publicEvidenceCount":273,"organizations":290,"bestGrade":176,"headline":178,"lastVerified":139,"indexable":204},"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.",[283],"automotive",[285,19],"customer-service",[287,243,21],"conversational-agent","customer-facing","aftersales",[291,292],"Fred Anderson Toyota","Murfreesboro Nissan",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":301,"patterns":303,"audience":25,"autonomy":246,"adoptionStage":27,"segment":306,"evidenceCount":248,"publicEvidenceCount":273,"organizations":307,"bestGrade":176,"headline":178,"lastVerified":310,"indexable":204},"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.",[299,300],"banking","payments",[302,19],"fraud-prevention",[243,304,305,21],"classification-and-routing","summarization","middle-office",[308,309],"Coast","SEB","2026-09-27",{"indexable":204,"reasons":312},[],[314,320,326,334,342,349,355,362,369,376,383,390,395,401,408,415,421,428,434,439,445,452,457,464,469,474,479,485,492,498,506,513,519,525,530,535],{"id":119,"label":315,"issuer":316,"region":185,"url":317,"description":318,"useCases":319,"indexable":204},"EU AI Act","European Union","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":321,"label":322,"issuer":316,"region":185,"url":323,"description":324,"useCases":325,"indexable":204},"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":327,"label":328,"issuer":329,"region":330,"url":331,"description":332,"useCases":333,"indexable":204},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":335,"label":336,"issuer":337,"region":338,"url":339,"description":340,"useCases":341,"indexable":204},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":343,"label":344,"issuer":345,"region":185,"url":346,"description":347,"useCases":348,"indexable":204},"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":316,"region":185,"url":352,"description":353,"useCases":354,"indexable":204},"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":185,"url":359,"description":360,"useCases":361,"indexable":204},"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":363,"label":364,"issuer":365,"region":152,"url":366,"description":367,"useCases":368,"indexable":204},"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":370,"label":371,"issuer":372,"region":152,"url":373,"description":374,"useCases":375,"indexable":204},"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":377,"label":378,"issuer":379,"region":338,"url":380,"description":381,"useCases":382,"indexable":204},"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":384,"label":385,"issuer":386,"region":330,"url":387,"description":388,"useCases":389,"indexable":204},"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":120,"label":391,"issuer":316,"region":185,"url":392,"description":393,"useCases":394,"indexable":204},"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":185,"url":399,"description":400,"useCases":394,"indexable":204},"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":338,"url":405,"description":406,"useCases":407,"indexable":204},"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":330,"url":412,"description":413,"useCases":414,"indexable":204},"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":316,"region":185,"url":418,"description":419,"useCases":420,"indexable":204},"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":338,"url":425,"description":426,"useCases":427,"indexable":204},"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":338,"url":432,"description":433,"useCases":427,"indexable":204},"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":316,"region":185,"url":437,"description":438,"useCases":258,"indexable":204},"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.",{"id":440,"label":441,"issuer":442,"region":330,"url":443,"description":444,"useCases":258,"indexable":204},"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":446,"label":447,"issuer":448,"region":338,"url":449,"description":450,"useCases":451,"indexable":204},"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":453,"label":454,"issuer":316,"region":185,"url":455,"description":456,"useCases":451,"indexable":204},"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":458,"label":459,"issuer":460,"region":185,"url":461,"description":462,"useCases":463,"indexable":204},"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":465,"label":466,"issuer":365,"region":152,"url":467,"description":468,"useCases":463,"indexable":204},"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":470,"label":471,"issuer":316,"region":185,"url":472,"description":473,"useCases":463,"indexable":204},"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":475,"label":476,"issuer":316,"region":185,"url":477,"description":478,"useCases":463,"indexable":204},"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":480,"label":481,"issuer":316,"region":185,"url":482,"description":483,"useCases":484,"indexable":204},"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":486,"label":487,"issuer":488,"region":338,"url":489,"description":490,"useCases":491,"indexable":204},"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":493,"label":494,"issuer":316,"region":185,"url":495,"description":496,"useCases":497,"indexable":204},"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":204},"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":185,"url":510,"description":511,"useCases":512,"indexable":204},"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":185,"url":517,"description":518,"useCases":512,"indexable":204},"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":152,"url":523,"description":524,"useCases":248,"indexable":204},"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":316,"region":185,"url":528,"description":529,"useCases":248,"indexable":204},"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":316,"region":185,"url":533,"description":534,"useCases":248,"indexable":204},"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":338,"url":539,"description":540,"useCases":248,"indexable":204},"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.",1790683492507]