[{"data":1,"prerenderedAt":556},["ShallowReactive",2],{"uc-drive-through-vehicle-inspection":3,"uc-regulations":318},{"useCase":4,"evidence":132,"blitsAiDeployments":221,"benchmarks":222,"indicative":166,"related":229,"indexability":316,"includeUnpublished":138},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"macroEstimates":40,"feasibility":41,"implementation":54,"risk":95,"blitsAi":113,"faq":115,"related":125,"datePublished":127,"dateModified":127,"lastVerified":127,"changelog":128,"slug":131},"AI drive through vehicle inspection for rental and delivery fleets","Drive through vehicle inspection","AI drive through vehicle inspection for fleets","Camera gates scan rental and delivery vehicles in seconds. Amazon says 35% of issues found are tires; Sixt says staff review flags before any damage claim.","published","AI drive through vehicle inspection uses cameras and sensors in a gate or archway that a vehicle drives through to scan its body, glass, tires and undercarriage in seconds. Computer vision detects defects and damage, rates their severity and compares the scan with an earlier one, so a fleet or rental operator knows what to repair or charge for.",[12,13,14,15],"automated vehicle inspection","AI rental car damage scanner","AVI archway","fleet inspection scanner",[17,18],"logistics-and-transportation","automotive",[20,21],"operations","field-service",[23,24],"computer-vision","anomaly-detection",[26],"internal-tools","employee-facing","supervised-agent","early-adopters","Fleet operators have long relied on a driver or an agent walking around a vehicle. Inspections are\ndone in varying light and weather and miss small defects such as a sidewall\ntear or a nail in a tread. Rental companies also need a record of the condition before and after each\nrental to decide who pays for damage.\n\nAmazon says its fleet managers previously had to rely solely on the human eye and manual inspections for daily safety rounds, and\nHertz says rental assessments have always relied heavily on manual inspections in varying conditions.",[],"1. **Drive through the gate.** The vehicle rolls through an archway, over sensor plates at low speed\n   (about 5 mph in Amazon's archways), at pickup, return or the end of a work shift.\n2. **Capture images.** Cameras and sensors capture the body, glass, tires and undercarriage, often in 3D.\n3. **Detect and classify.** A vision model finds dents, scratches, tire wear and mechanical defects and\n   classifies them by severity.\n4. **Compare with the baseline.** For rentals the scan at return is compared with the scan at pickup, so\n   only new damage is flagged.\n5. **Route the result.** Results go to a fleet portal or the rental system with photos and repair\n   suggestions. A person decides on repairs and on any charge to a customer.",[34,35,36],"cost-to-serve","risk-reduction","speed",[38,39],"accuracy","detection-rate-improvement",[],{"complexity":42,"complexityNote":43,"dataPrerequisites":44,"integrations":49},"medium","The scanner is physical equipment that has to be installed at each site and tuned to the fleet, for example Amazon trained the models on its own roadworthy standards and van types. The scan has to be linked to the vehicle record and the rental contract, and the process around disputes has to be ready before any charge is made.",[45,46,47,48],"Vehicle identification and contract or driver assignment at scan time","Baseline scans from the previous checkout or inspection","Repair standards that define what counts as damage or a defect","Photos and scan history kept for the retention period",[50,51,52,53],"Scanner vendor platform and cloud storage","Fleet management or rental system","Maintenance and work order system","Customer billing and dispute handling",{"steps":55,"guardrails":71,"humanInTheLoop":76,"kpisToInstrument":77,"failureModes":82},[56,59,62,65,68],{"title":57,"detail":58},"Start at a single site","Install at one airport branch or delivery station and run it beside the manual inspection until you have compared both on the same vehicles.",{"title":60,"detail":61},"Define damage rules before billing","Decide thresholds for chargeable damage and the wear that is normal, and write them down for customers and staff.",{"title":63,"detail":64},"Put a person before any charge","Have a trained staff member review every flagged item before a customer is charged. Sixt says it does this, yet The Drive reported two 2025 Sixt invoices that used the pickup scan photo as proof of return damage. The review must check the date and scan of every photo, not only the flag.",{"title":66,"detail":67},"Feed repairs from the results","Link detected defects such as tire wear to the work order system so the scan becomes maintenance, as Amazon does through its fleet portal.",{"title":69,"detail":70},"Handle disputes with the evidence","Give customers the dated photos from pickup and return on request and track dispute outcomes.",[72,73,74,75],"Human review of every flagged item before any charge to a customer","Dated before and after images available to the customer on request","Baseline scan must exist before a damage claim is raised","Retention and access rules for vehicle images and any visible personal data","Staff review flagged damage, decide on repairs and approve customer charges. Operations leads review dispute outcomes and adjust the rules and thresholds.",[78,79,80,81],"Defects found per scan against manual inspection on the same vehicles","Share of flagged items overturned on review or dispute","Inspection time per vehicle","Repairs triggered before a breakdown",[83,86,89,92],{"title":84,"detail":85},"Wrong photo as evidence","A damage claim uses a photo from the wrong date or scan. Match each claim to the pickup and return scans by timestamp.",{"title":87,"detail":88},"Automatic charging","Customers are billed for flagged damage without review. Require staff review before a charge.",{"title":90,"detail":91},"Site variance","Weather and light at the gate change detection quality. Test at each site and track false positive rates.",{"title":93,"detail":94},"Customer backlash","Charges that look automated damage trust and reach the press. Publish the process and handle disputes quickly.",{"euAiAct":96,"regulations":99,"guidance":102,"controls":103,"incidents":108},{"tier":97,"basis":98},"context-dependent","Scanning vehicles for damage and defects is not an Annex III use, and Article 50 does not apply because no person interacts with the system, so it is normally minimal risk. If scan results are used to attribute damage to individual drivers and to monitor, evaluate or discipline them, as could happen in a delivery fleet, Annex III point 4(b) can make it high risk, and under Article 26(7) employers must inform workers' representatives and the affected workers first. Charges to rental customers remain a consumer law matter.",[100,101],"eu-ai-act","gdpr",[],[104,105,106,107],"Human review before every customer charge","Dated baseline and return images retained and shared on request","Written damage thresholds and a dispute process","Image retention and access limits",[109],{"title":110,"url":111,"note":112},"Sixt customers charged for damage present before their rental","https:\u002F\u002Fwww.thedrive.com\u002Fnews\u002Fits-not-just-hertz-more-rental-car-brands-are-using-ai-scanners-to-nickel-and-dime-you","The Drive reported in July 2025 that two Sixt customers were invoiced for damage that predated their rental, in both cases with photos from the pickup scan. Sixt apologized in one case, said discrepancies are reviewed, and dropped both claims.",{"howToBuild":114},"Blits.ai does not supply the scanner, the vision model or the comparison between scans; those come\nfrom the hardware vendor. Blits.ai builds the layer around the scan result. An **agentic workflow**\nreceives the vendor's damage report through an API, matches it to the rental contract or vehicle\nrecord through **custom functions**, and drafts the repair order or the damage claim, with **human in\nthe loop** approval before any customer is charged.\n\nA **voice** or **web chat** agent handles customer disputes with **custom functions** that fetch the\ndated pickup and return photos, and **human handover** passes contested cases to staff. **Guardrails**\nkeep the agent from confirming a charge, **PII masking** protects customer data, and **analytics**\ntrack the share of flagged items overturned.\n\nRun Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private\ncloud, or fully on premises.",[116,119,122],{"question":117,"answer":118},"How accurate is AI vehicle inspection?","UVeye says its system identifies defects in Amazon delivery vans with 96% accuracy, against 24% manually. This is a vendor claim without a stated method, so run both on the same vehicles before trusting it.",{"question":120,"answer":121},"Do rental companies charge customers automatically?","Practice differs. Sixt says a staff member reviews flagged damage before a claim, which The Drive contrasted with Hertz, and The Drive still reported two Sixt invoices for damage that predated the rental. Enterprise told The Drive it does not use digital damage scanners and Avis said its damage assessment remains human led.",{"question":123,"answer":124},"What do fleets learn beyond damage?","Amazon reports that 35% of all issues found by its scanners involve tires, and Delivery Service Partners are notified to replace tires before a blowout or flat.",[126],"photo-based-damage-assessment","2026-11-11",[129],{"date":127,"note":130},"First published","drive-through-vehicle-inspection",[133,167,185],{"title":134,"useCases":135,"organization":136,"vendors":142,"summary":146,"stage":147,"year":148,"channels":149,"languages":150,"metrics":152,"outcomeDisclosed":138,"sources":153,"verification":162,"grade":164,"id":165,"organizationSlug":166},"Hertz: UVeye AI vehicle inspection at US airport locations",[131],{"name":137,"anonymized":138,"country":139,"region":140,"industry":17,"wikidata":141},"Hertz",false,"US","north-america","Q1543874",[143],{"name":144,"role":145},"UVeye","platform","Hertz announced on 16 April 2025 that it is adding UVeye's AI inspection systems, which scan a vehicle's body, glass, tires and undercarriage in seconds, to its US operations, starting at Atlanta Hartsfield-Jackson airport and rolling out to major airports. The release gives no result figures. The rollout drew customer disputes over damage charges, reported by The Drive.","production",2025,[26],[151],"en",[],[154,158],{"url":155,"title":156,"publisher":137,"date":157},"https:\u002F\u002Fnewsroom.hertz.com\u002Farticles\u002Farticle-details\u002Fhertz-and-uveye-partner-to-modernize-vehicle-maintenance-with-ai-technology","Hertz and UVeye Partner to Modernize Vehicle Maintenance with AI Technology","2025-04-16",{"url":111,"title":159,"publisher":160,"date":161},"It's Not Just Hertz: AI Damage Scanners Are Spreading to More Rental Car Companies","The Drive","2025-07-24",{"level":163,"checkedAt":127},"source-verified","B","hertz-uveye-ai-vehicle-inspection-airports",null,{"title":168,"useCases":169,"organization":170,"vendors":175,"summary":176,"stage":147,"year":148,"channels":177,"languages":178,"metrics":179,"outcomeDisclosed":138,"sources":180,"verification":182,"grade":183,"id":184,"organizationSlug":166},"Sixt: Car Gate AI scanning at rental pickup and return",[131],{"name":171,"anonymized":138,"country":172,"region":173,"industry":17,"wikidata":174},"Sixt","DE","global","Q705664",[],"Sixt scans rental cars with its Car Gate system when a customer drives away and again on return, and says it issues damage claims only after a staff member has reviewed what the computer flagged. The Drive reported in July 2025 that two US customers were billed for damage that was already present at pickup, and Sixt apologized and said discrepancies are reviewed and resolved. No result figures are published.",[26],[151],[],[181],{"url":111,"title":159,"publisher":160,"date":161},{"level":163,"checkedAt":127},"C","sixt-car-gate-ai-damage-scanning",{"title":186,"useCases":187,"organization":188,"vendors":191,"summary":195,"stage":196,"year":197,"channels":198,"languages":199,"metrics":200,"outcomeDisclosed":209,"sources":210,"verification":218,"grade":183,"id":219,"organizationSlug":220},"Amazon: UVeye automated vehicle inspection for delivery vans",[131],{"name":189,"anonymized":138,"country":139,"region":173,"industry":17,"wikidata":190},"Amazon","Q3884",[192,193],{"name":144,"role":145},{"name":194,"role":145},"Amazon Web Services","Amazon and UVeye built an Automated Vehicle Inspection archway that delivery drivers pass through at the end of each workday, replacing manual driver vehicle inspection reports for vans operated by Delivery Service Partners in the US, Canada, Germany and the UK. The scan takes seconds, sorts issues by severity and shows photos and repair suggestions in Amazon's Fleet Portal. UVeye reports 96% defect detection against 24% for manual inspection.","scaled",2023,[26],[151],[201],{"kpi":38,"value":202,"unit":203,"qualifier":204,"baseline":205,"claimant":206,"quote":207,"sourceUrl":208},96,"percent","exact","24% for manual inspection, as reported by UVeye","vendor","AI-driven accuracy: Identifies defects with 96% accuracy vs. 24% manually, spotting even the smallest anomalies in Amazon delivery vans.","https:\u002F\u002Fuveye.com\u002Fuse-case\u002Famazon\u002F",true,[211,215],{"url":212,"title":213,"publisher":189,"date":214},"https:\u002F\u002Fwww.aboutamazon.com\u002Fnews\u002Ftransportation\u002Famazon-automated-vehicle-inspection","Amazon unveils new AI-powered technology that will inspect delivery vans and help keep drivers safe","2023-10-18",{"url":208,"title":216,"publisher":144,"date":217},"UVeye and Amazon","2025-09-29",{"level":163,"checkedAt":127},"amazon-uveye-automated-vehicle-inspection-delivery-vans","amazon",0,[223],{"kpi":38,"label":224,"unit":203,"aggregate":209,"higherIsBetter":209,"n":225,"nUpTo":221,"median":202,"min":202,"max":202,"byClaimant":226,"vendorOnly":209,"points":227},"Accuracy",1,{"organization":221,"vendor":225,"regulator":221,"independent":221},[228],{"evidenceId":219,"organization":189,"value":202,"qualifier":204,"claimant":206,"grade":183,"pooled":209},[230,261,277,293],{"slug":126,"title":231,"shortTitle":232,"definition":233,"status":9,"industries":234,"functions":236,"patterns":238,"audience":241,"autonomy":28,"adoptionStage":29,"segment":237,"evidenceCount":242,"publicEvidenceCount":242,"organizations":243,"bestGrade":164,"headline":256,"lastVerified":260,"indexable":209},"AI for photo based damage assessment in insurance claims","Photo damage assessment","Photo based damage assessment is computer vision that assesses vehicle or property damage from photos or video for an insurance claim. The photos or video can come from the policyholder, a repairer or an adjuster. The AI identifies the damaged parts and the repair or replace decision, produces or checks the repair estimate, and flags total losses and inconsistencies for a person to review.",[235],"insurance",[237],"claims",[23,239,240],"prediction-and-scoring","agentic-workflow","customer-facing",12,[244,245,246,247,248,249,250,251,252,253,254,255],"Admiral Seguros","Ageas UK","Covéa","Foyer","MAPFRE","Ping An Insurance (Group) Company of China","Porto Seguro","PZU","Sompo Japan","Tokio Marine & Nichido Fire Insurance","Warta","ZhongAn Online P&C Insurance",{"kpi":38,"label":224,"unit":203,"n":225,"nUpTo":221,"kind":257,"value":258,"qualifier":204,"claimant":259,"organization":249,"vendorReported":138},"reported",95,"organization","2026-09-27",{"slug":262,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":267,"patterns":268,"audience":27,"autonomy":269,"adoptionStage":29,"segment":270,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":164,"headline":166,"lastVerified":276,"indexable":209},"freight-rail-rolling-stock-predictive-maintenance","AI predictive maintenance for freight rail rolling stock","Rail rolling stock predictive maintenance","AI predictive maintenance for freight rail rolling stock is 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, it learns what a healthy wheel or reading looks like and flags the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.",[17],[20,21],[23,24,239],"assist","mechanical-and-safety",3,[273,274,275],"BNSF Railway","Canadian Pacific Kansas City","Norfolk Southern","2026-09-28",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":285,"patterns":287,"audience":27,"autonomy":269,"adoptionStage":288,"evidenceCount":271,"publicEvidenceCount":271,"organizations":289,"bestGrade":183,"headline":166,"lastVerified":292,"indexable":209},"crowd-density-and-flow-monitoring","AI crowd density and flow monitoring for venues, transport hubs and pilgrimage sites","Crowd density and flow monitoring","Crowd density and flow monitoring is AI that counts people on camera feeds and flags crowding, movement against the flow and congestion points so operators can act early. It estimates how many people are in a zone and which way they move, and leaves every decision, such as opening a gate, redirecting a stream or sending staff, to the operators in the control room.",[283,17,284],"government","media-and-entertainment",[286,20],"security-operations",[23,24,239],"emerging",[290,291],"Ministry of the Interior (France)","Saudi Data and Artificial Intelligence Authority (SDAIA)","2026-10-14",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":300,"patterns":302,"audience":303,"autonomy":28,"adoptionStage":29,"segment":304,"evidenceCount":305,"publicEvidenceCount":305,"organizations":306,"bestGrade":164,"headline":166,"lastVerified":260,"indexable":209},"predictive-network-maintenance","AI for predictive network maintenance in telecom","Predictive network maintenance","Predictive network maintenance is machine learning that spots early signs of network failure and triggers a fix before customers lose service, for telecom operators. It watches for degrading cells, faulty customer equipment, aging hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention.",[299],"telecommunications",[301,21,20],"network-operations",[24,239,240],"back-office","network",9,[307,308,309,310,311,312,313,314,315],"Grameenphone","KDDI","Orange","Telefónica España","Telkomsel","Telstra","TIM Brasil","Verizon","Vodafone",{"indexable":209,"reasons":317},[],[319,326,331,338,345,352,358,365,373,380,386,392,398,405,412,419,425,431,437,443,450,455,460,466,471,478,483,489,496,501,509,515,521,528,533,539,544,550],{"id":100,"label":320,"issuer":321,"region":322,"url":323,"description":324,"useCases":325,"indexable":209},"EU AI Act","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",306,{"id":101,"label":327,"issuer":321,"region":322,"url":328,"description":329,"useCases":330,"indexable":209},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",276,{"id":332,"label":333,"issuer":334,"region":173,"url":335,"description":336,"useCases":337,"indexable":209},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",150,{"id":339,"label":340,"issuer":341,"region":140,"url":342,"description":343,"useCases":344,"indexable":209},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":346,"label":347,"issuer":348,"region":322,"url":349,"description":350,"useCases":351,"indexable":209},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",86,{"id":353,"label":354,"issuer":321,"region":322,"url":355,"description":356,"useCases":357,"indexable":209},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":359,"label":360,"issuer":361,"region":322,"url":362,"description":363,"useCases":364,"indexable":209},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":366,"label":367,"issuer":368,"region":369,"url":370,"description":371,"useCases":372,"indexable":209},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":374,"label":375,"issuer":376,"region":369,"url":377,"description":378,"useCases":379,"indexable":209},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":381,"label":382,"issuer":321,"region":322,"url":383,"description":384,"useCases":385,"indexable":209},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":387,"label":388,"issuer":389,"region":173,"url":390,"description":391,"useCases":385,"indexable":209},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":393,"label":394,"issuer":395,"region":140,"url":396,"description":397,"useCases":385,"indexable":209},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":399,"label":400,"issuer":401,"region":140,"url":402,"description":403,"useCases":404,"indexable":209},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",21,{"id":406,"label":407,"issuer":408,"region":322,"url":409,"description":410,"useCases":411,"indexable":209},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":413,"label":414,"issuer":415,"region":140,"url":416,"description":417,"useCases":418,"indexable":209},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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.",17,{"id":420,"label":421,"issuer":321,"region":322,"url":422,"description":423,"useCases":424,"indexable":209},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",15,{"id":426,"label":427,"issuer":428,"region":173,"url":429,"description":430,"useCases":424,"indexable":209},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":432,"label":433,"issuer":321,"region":322,"url":434,"description":435,"useCases":436,"indexable":209},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":438,"label":439,"issuer":440,"region":173,"url":441,"description":442,"useCases":436,"indexable":209},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":444,"label":445,"issuer":446,"region":140,"url":447,"description":448,"useCases":449,"indexable":209},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":451,"label":452,"issuer":321,"region":322,"url":453,"description":454,"useCases":449,"indexable":209},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":456,"label":457,"issuer":321,"region":322,"url":458,"description":459,"useCases":449,"indexable":209},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":461,"label":462,"issuer":463,"region":140,"url":464,"description":465,"useCases":242,"indexable":209},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":467,"label":468,"issuer":321,"region":322,"url":469,"description":470,"useCases":242,"indexable":209},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":472,"label":473,"issuer":474,"region":322,"url":475,"description":476,"useCases":477,"indexable":209},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":479,"label":480,"issuer":321,"region":322,"url":481,"description":482,"useCases":477,"indexable":209},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":484,"label":485,"issuer":368,"region":369,"url":486,"description":487,"useCases":488,"indexable":209},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":490,"label":491,"issuer":492,"region":140,"url":493,"description":494,"useCases":495,"indexable":209},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":497,"label":498,"issuer":321,"region":322,"url":499,"description":500,"useCases":495,"indexable":209},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":502,"label":503,"issuer":504,"region":505,"url":506,"description":507,"useCases":508,"indexable":209},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":510,"label":511,"issuer":321,"region":322,"url":512,"description":513,"useCases":514,"indexable":209},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",5,{"id":516,"label":517,"issuer":518,"region":322,"url":519,"description":520,"useCases":514,"indexable":209},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":522,"label":523,"issuer":524,"region":369,"url":525,"description":526,"useCases":527,"indexable":209},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",4,{"id":529,"label":530,"issuer":368,"region":369,"url":531,"description":532,"useCases":527,"indexable":209},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":534,"label":535,"issuer":536,"region":322,"url":537,"description":538,"useCases":527,"indexable":209},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":540,"label":541,"issuer":321,"region":322,"url":542,"description":543,"useCases":271,"indexable":209},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":545,"label":546,"issuer":547,"region":140,"url":548,"description":549,"useCases":271,"indexable":209},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",{"id":551,"label":552,"issuer":553,"region":505,"url":554,"description":555,"useCases":271,"indexable":209},"sdaia-ai-ethics","SDAIA AI ethics principles","Saudi Data and AI Authority","https:\u002F\u002Fsdaia.gov.sa\u002Fen\u002FSDAIA\u002Fabout\u002FDocuments\u002Fai-principles.pdf","Saudi Arabia's national AI ethics principles, applied across sectors including finance and government.",1794496888227]