[{"data":1,"prerenderedAt":609},["ShallowReactive",2],{"uc-photo-based-damage-assessment":3,"uc-regulations":397},{"useCase":4,"evidence":175,"blitsAiDeployments":308,"benchmarks":309,"indicative":319,"related":322,"indexability":395,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":25,"audience":29,"autonomy":30,"adoptionStage":31,"segment":20,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":47,"macroEstimates":74,"feasibility":75,"implementation":88,"risk":131,"blitsAi":153,"faq":155,"related":165,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI for photo based damage assessment in insurance claims","Photo damage assessment","AI photo damage assessment for insurance claims","AI assesses damage from claim photos and drafts or checks the repair estimate. Tractable reports 90% of Admiral Seguros estimates in 2021 needed no human appraiser.","published","Computer vision that assesses damage from photos or video of a vehicle or property taken by the policyholder, a repairer or an adjuster, 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.",[12,13,14,15,16],"AI damage estimation","computer vision claims assessment","photo estimating","virtual vehicle inspection","AI estimate review",[18],"insurance",[20],"claims",[22,23,24],"computer-vision","prediction-and-scoring","agentic-workflow",[26,27,28],"mobile-app","web-chat","api","customer-facing","supervised-agent","early-adopters","For motor and many property claims, the size of the loss is decided by an expert looking at the\ndamage: a field appraiser drives to the car or house, or a desk assessor reviews a repairer's\nestimate and photos. Appraiser capacity is limited, the visit can add days to the claim, and after\na hail storm, flood or other large event the number of claims surges and the backlog grows.\nCustomers wait without a car or with a damaged home, and repairers wait for approval before they\ncan start.\n\nDesk review has its own problem: assessors check large numbers of estimates line by line, and the\nconsistency of the decision depends on who looks at it. Inflated or duplicated items can slip\nthrough, while honest estimates wait in the same queue.",[],"1. **Capture the images.** The policyholder receives a link at first notice of loss and is guided\n   to take the right photos, or the repairer uploads them with the estimate.\n2. **Check the images.** The model confirms the images show the insured vehicle or property, are\n   usable and have not been reused from another claim.\n3. **Assess the damage.** Computer vision identifies the damaged parts, the severity and the repair\n   or replace decision per part, and estimates labour and parts cost from repair data.\n4. **Decide the path.** A clear, low value case gets an estimate or cash settlement offer within\n   minutes; a likely total loss goes to the total loss process; everything else goes to an\n   assessor with the AI findings.\n5. **Review estimates from repairers.** For repairer estimates, the AI compares each line with the\n   photos and flags items that are not supported, so assessors review exceptions only.",[36,37,38,39],"speed","cost-to-serve","customer-experience","risk-reduction",[41,42,43,44,45,46],"automation-rate","interactions-handled","processing-time-reduction","cycle-time-days","accuracy","cost-reduction",{"referenceOrg":48,"inputs":49,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"A motor insurer handling 100,000 repairable vehicle damage claims a year",[50,55,62],{"key":20,"label":51,"low":52,"high":52,"unit":53,"note":54},"Repairable vehicle damage claims per year",100000,"claims per year","The reference insurer.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"eligibleShare","Share of claims assessed from photos instead of a physical inspection",0.3,0.6,"fraction of claims","Conservative against the evidence on this page (Tractable reports that 90% of Admiral Seguros claim estimates were processed without human appraisers in 2021), because eligibility depends on the claim mix and customer uptake.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"inspectionCost","Cost of a physical or detailed desk assessment avoided",60,150,"USD per claim","Editorial assumption covering appraiser time and travel; replace with your own appraisal costs.","claims * eligibleShare * inspectionCost","USD","per year","Appraisal cost avoided","Appraisal cost only. It leaves out shorter rental car periods, better estimate accuracy and leakage control, customer satisfaction, catastrophe surge capacity and the cost of the service and the integration.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":82},"medium","Specialised vendor models exist for vehicle damage, and the motor deployments on this page use one (Tractable) rather than a model built in house; Tokio Marine & Nichido Fire uses Shift Technology's claims platform to review damage photos and estimates. The work is in the customer photo journey, integration with estimating and claims systems, repair cost data for the local market, and the rules that decide when a claim may settle on the AI estimate.",[79,80,81],"Local repair cost, labour rate and parts data, or a vendor that holds it","Historical claims with photos and final repair costs to calibrate and test","Rules for photo settlement per claim type, value and customer situation",[83,84,85,86,87],"Claims management system for the claim, reserve and payment","Customer photo capture link or app, sent at first notice of loss","Estimating platform and repairer network systems","Total loss valuation process","Fraud detection for image reuse and manipulation checks",{"steps":89,"guardrails":105,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":118},[90,93,96,99,102],{"title":91,"detail":92},"Choose the journey","Decide whether the AI starts with the policyholder's photos at first notice of loss, with repairer estimates, or both. Repairer estimate review can be the faster first step, because repairers already send photos with every estimate.",{"title":94,"detail":95},"Calibrate on your own claims","Run the model over a few thousand closed claims with known repair costs and compare estimates, repair or replace decisions and total loss calls with what happened.",{"title":97,"detail":98},"Design the photo capture for customers","Guided capture with examples and live checks for blur and angle decides how many claims can be assessed at all. Test it with real customers, not staff.",{"title":100,"detail":101},"Set settlement rules","Define the value limits, claim types and confidence thresholds under which an AI estimate may be offered or approved without an assessor.",{"title":103,"detail":104},"Close the loop with repair outcomes","Feed final invoices, supplements and reinspections back into monitoring so estimate accuracy is measured on real repairs.",[106,107,108,109,110],"Cash settlement offers only inside value limits and confidence thresholds per claim type","The customer can always ask for a human assessment","Image checks for reuse, manipulation and the wrong vehicle or property before any offer","Supplements and reinspection results monitored against AI estimates per repairer","Total loss and injury indications always go to a person","Assessors review every claim outside the settlement rules and every flagged estimate line, and sample AI settled claims each week. Customers who disagree with an estimate get a human assessment.",[113,114,115,116,117],"Share of claims assessed from photos, per claim type","Days from first notice of loss to estimate and to repair authorisation","Difference between AI estimate and final repair cost, including supplements","Share of customers who complete the photo journey","Complaints and disputes about AI based estimates",[119,122,125,128],{"title":120,"detail":121},"Customers do not finish the photo journey","Poor guidance leads to unusable photos and a fallback inspection, which is slower than before. Invest in guided capture and measure completion.",{"title":123,"detail":124},"Estimates that miss hidden damage","Photos show the outside, not the structural damage behind it. Allow supplements and track them against AI estimates.",{"title":126,"detail":127},"Low offers that damage trust","A fast but low settlement offer creates complaints and regulatory risk. Monitor disputes and offer a human assessment by default.",{"title":129,"detail":130},"Reused or manipulated images","Fraudsters submit old or edited photos. Check image metadata, similarity across claims and signs of manipulation before any payment.",{"euAiAct":132,"regulations":135,"guidance":140,"controls":147,"incidents":152},{"tier":133,"basis":134},"limited","Assessing damage to vehicles or property for property and casualty claims is not listed in Annex III, which covers insurance only for risk assessment and pricing of natural persons in life and health insurance (point 5(c)). Article 50(1) transparency duties apply when the customer interacts directly with the AI, for example a guided photo journey that returns an AI estimate or offer, or a chat agent. A purely internal repairer estimate review with no customer interaction is minimal. A settlement or refusal decided solely by automated processing can fall under GDPR Article 22.",[136,137,138,139],"eu-ai-act","gdpr","uk-consumer-duty","iso-42001",[141],{"title":142,"issuer":143,"region":144,"url":145,"note":146},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Addressed to national supervisors (August 2025). Sets out how insurers should govern AI systems across the value chain, including claims, with measures proportionate to their risk and impact on customers, such as data governance, explainability and human oversight.",[148,149,150,151],"Documented settlement rules and thresholds under change control","Estimate accuracy monitored against final repair costs per model version","Customer disclosure that AI assesses the photos, with a right to a human assessment","Image integrity checks logged per claim",[],{"howToBuild":154},"Blits.ai does not supply the damage recognition model itself; the insurer uses a specialised\ncomputer vision vendor or its own model and connects it through **custom functions** (REST\ncalls). Blits.ai carries the customer journey around it: an **AI agent** on **web chat, WhatsApp\nor the insurer's own app through the API channel** that guides the policyholder through the\nphotos with **receive attachment** blocks, explains the estimate and next steps, and hands over\nto an assessor.\n\n**Guardrails** keep the agent from promising amounts outside the settlement rules, **human in the\nloop approval** in an **agentic workflow** holds offers above a threshold for an assessor, and\n**human handover** passes the case with photos and findings. **Test suites** evaluate the\nconversation around the photo journey before each release, **monitors** run scheduled health\nchecks, and **analytics** with flow statistics show how many customers complete the photo\njourney.",[156,159,162],{"question":157,"answer":158},"How many claims can be assessed from photos without an appraiser?","It depends on the claim mix and on how many customers finish the photo journey. Tractable reports that 90% of Admiral Seguros claim estimates in 2021 were processed without human appraisers, with 98% of claims completed in less than 15 minutes, and that 70 to 75% of customers who receive the link complete the claim. Complex damage, injuries and likely total losses are best kept with a person.",{"question":160,"answer":161},"Is this only for customers' own photos?","No. Photo AI can also serve repairers and assessors who review estimates: Covéa's partner repairers receive instant assessments based on photos of the damage, and at Tokio Marine & Nichido Fire the AI highlights points to check for consistency across estimates, damage photos and claim statements.",{"question":163,"answer":164},"Does photo AI increase fraud risk?","It changes it. When the estimate rests on photos alone, reused, edited or AI generated images become a risk to manage, so image integrity checks and similarity search across claims should run before any payment.",[166,167,168,169],"claims-first-notice-of-loss-agent","claims-triage-and-straight-through-processing","claims-fraud-detection","subrogation-opportunity-detection","2026-09-27",[172],{"date":170,"note":173},"First published","photo-based-damage-assessment",[176,212,233,260,281],{"title":177,"useCases":178,"organization":179,"vendors":183,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":202,"sources":203,"verification":207,"grade":209,"id":210,"organizationSlug":211},"Covéa: AI photo assessment of motor damage for its repairer network",[174],{"name":180,"anonymized":181,"country":182,"region":144,"industry":18},"Covéa",false,"FR",[184],{"name":185,"role":186},"Tractable","platform","Covéa, the French mutual group behind the MAAF, MMA and GMF brands, has used Tractable's AI since 2018 to analyse photos of vehicle damage in real time, so that its partner repairers receive instant assessments instead of waiting on administrative steps after an accident. In April 2026 the two renewed the partnership for three years. Covéa's head of the auto networks division says more than 160,000 claims have been finalized since the collaboration began.","production",2026,[],[192],"fr",[194],{"kpi":42,"value":195,"unit":196,"qualifier":197,"period":198,"claimant":199,"quote":200,"sourceUrl":201},160000,"count","at-least","Claims finalized since the partnership began in 2018","organization","Since the beginning of our collaboration, more than 160,000 claims have been finalized.","https://tractable.ai/covea-renewal-tractable/",true,[204],{"url":201,"title":205,"publisher":185,"date":206},"Covéa and Tractable renew their partnership to leverage AI in auto claims management in France","2026-04-01",{"level":208,"checkedAt":170},"source-verified","C","covea-ai-photo-damage-assessment",null,{"title":213,"useCases":214,"organization":215,"vendors":218,"summary":220,"stage":221,"year":189,"channels":222,"languages":223,"metrics":224,"outcomeDisclosed":181,"sources":225,"verification":230,"grade":209,"id":232,"organizationSlug":211},"Foyer: AI analysis of damage photos for minor motor claims",[174],{"name":216,"anonymized":181,"country":217,"region":144,"industry":18},"Foyer","LU",[219],{"name":185,"role":186},"Foyer, a Luxembourg insurer, announced in May 2026 that it has formalised a partnership with Tractable to identify damage automatically from photographs for minor motor incidents. The aim is immediate analysis of the damage the policyholder reports, faster handling of the simplest claims and fewer visits to the garage. No outcome figures were published.","announced",[],[],[],[226],{"url":227,"title":228,"publisher":185,"date":229},"https://tractable.ai/foyer-and-tractable/","Foyer and Tractable: AI for motor claims management","2026-05-06",{"level":208,"checkedAt":231},"2026-09-26","foyer-ai-motor-claims-photos",{"title":234,"useCases":235,"organization":236,"vendors":240,"summary":243,"stage":188,"year":244,"channels":245,"languages":247,"metrics":249,"outcomeDisclosed":181,"sources":250,"verification":258,"grade":209,"id":259,"organizationSlug":211},"Tokio Marine & Nichido Fire: AI review of damage photos, estimates and suspicious claims",[167,168,174],{"name":237,"anonymized":181,"country":238,"region":239,"industry":18},"Tokio Marine & Nichido Fire Insurance","JP","asia-pacific",[241],{"name":242,"role":186},"Shift Technology","Tokio Marine & Nichido Fire Insurance uses Shift Technology's claims intake and claims fraud detection solutions, extended with generative AI that extracts data from structured and unstructured sources such as images and documents. The system highlights the points handlers should check for consistency across estimates, damage photos and claim statements, which makes reviews more efficient and more standardized, including during the surge of claims after large disasters, and it helps detect suspicious claims, which tend to rise after such events. No figures were published.",2025,[246],"internal-tools",[248],"ja",[],[251,254],{"url":252,"title":253,"publisher":242},"https://www.shift-technology.com/resources/case-studies/tokiomarine_casestudy","Case Study: Tokyo Marine & Nichido Fire Insurance Co., Ltd.",{"url":255,"title":256,"publisher":242,"date":257},"https://www.shift-technology.com/resources/press/tokio-marine-deploys-shift-technologys-gen-ai-for-claims-fraud-detection","Tokio Marine Deploys Shift Technology’s Gen AI for Claims, Fraud Detection","2025-09-04",{"level":208,"checkedAt":231},"tokio-marine-nichido-shift-claims-review",{"title":261,"useCases":262,"organization":263,"vendors":266,"summary":268,"stage":188,"year":269,"channels":270,"languages":271,"metrics":273,"outcomeDisclosed":181,"sources":274,"verification":279,"grade":209,"id":280,"organizationSlug":211},"PZU: AI assessment of car damage from policyholder photos",[174],{"name":264,"anonymized":181,"country":265,"region":144,"industry":18},"PZU","PL",[267],{"name":185,"role":186},"In March 2022 Tractable and PZU, Poland's largest insurer, announced that policyholders can submit smartphone photos of car damage when they report an accident. Tractable's AI assesses the damage and calculates the repair cost, the claim handler can share the result within minutes, and the policyholder can accept a cash settlement instead of waiting days. PZU had worked with Tractable since 2017 and already used its AI to check how body shops carry out repairs, processing several hundred thousand claims with its AI based tools. No outcome figures for the photo journey were published.",2022,[],[272],"pl",[],[275],{"url":276,"title":277,"publisher":185,"date":278},"https://tractable.ai/pzu-is-first-polish-insurer-to-enable-its-customers-to-use-ai-to-assess-car-damage-and-settle-claims-in-minutes/","PZU is first Polish insurer to enable its customers to use AI to assess car damage and settle claims in minutes","2022-03-07",{"level":208,"checkedAt":231},"pzu-ai-car-damage-assessment",{"title":282,"useCases":283,"organization":284,"vendors":287,"summary":289,"stage":188,"year":290,"channels":291,"languages":292,"metrics":294,"outcomeDisclosed":202,"sources":302,"verification":306,"grade":209,"id":307,"organizationSlug":211},"Admiral Seguros: touchless motor claims with AI damage estimates",[174,167],{"name":285,"anonymized":181,"country":286,"region":144,"industry":18},"Admiral Seguros","ES",[288],{"name":185,"role":186},"Admiral Seguros, the Spanish business of Admiral Group, sends motor claimants a link to a web app in which they photograph the damage, and Tractable's AI produces the repair estimate within minutes. Tractable reports that Admiral Seguros processed 12,000 touchless claims this way in 2021, that 90% of claim estimates were processed without human appraisers and that 98% of claims were completed in less than 15 minutes.",2021,[],[293],"es",[295],{"kpi":42,"value":296,"unit":196,"qualifier":297,"period":298,"claimant":299,"quote":300,"sourceUrl":301},12000,"exact","Touchless claims in 2021","vendor","In 2021, Admiral Seguros processed 12,000 touchless claims using Tractable AI.","https://tractable.ai/case-studies/admiral-seguros/",[303],{"url":301,"title":304,"publisher":185,"date":305},"Admiral Seguros: Tractable delivers outstanding customer services through touchless claims","2023-04-21",{"level":208,"checkedAt":231},"admiral-seguros-ai-vehicle-damage-estimates",0,[310],{"kpi":42,"label":311,"unit":196,"aggregate":181,"higherIsBetter":202,"n":312,"nUpTo":308,"median":313,"min":296,"max":195,"byClaimant":314,"vendorOnly":181,"points":316},"Interactions handled",2,86000,{"organization":315,"vendor":315,"regulator":308,"independent":308},1,[317,318],{"evidenceId":210,"organization":180,"value":195,"qualifier":197,"claimant":199,"grade":209,"pooled":202},{"evidenceId":307,"organization":285,"value":296,"qualifier":297,"claimant":299,"grade":209,"pooled":202},{"low":320,"high":321},1800000,9000000,[323,347,368,383],{"slug":166,"title":324,"shortTitle":325,"definition":326,"status":9,"industries":327,"functions":328,"patterns":330,"audience":29,"autonomy":30,"adoptionStage":31,"segment":20,"evidenceCount":334,"publicEvidenceCount":334,"organizations":335,"bestGrade":341,"headline":342,"lastVerified":170,"indexable":202},"AI agent for first notice of loss claims intake","First notice of loss agent","An AI agent that takes the first notice of loss from a policyholder by phone, chat or app, identifies the policy, collects the facts of the incident and the evidence the claim type needs, opens the claim in the claims system and tells the customer what happens next, handing complex, injured or vulnerable claimants to a human handler.",[18],[20,329],"customer-service",[331,332,24,333],"conversational-agent","voice-agent","document-processing",5,[336,337,338,339,340],"DOMCURA","Hippo","Lemonade","Progressive","Travelers","B",{"kpi":45,"label":343,"unit":344,"n":315,"nUpTo":308,"kind":345,"value":346,"qualifier":297,"claimant":299,"organization":336,"vendorReported":202},"Accuracy","percent","reported",90,{"slug":167,"title":348,"shortTitle":349,"definition":350,"status":9,"industries":351,"functions":352,"patterns":354,"audience":357,"autonomy":30,"adoptionStage":31,"segment":20,"evidenceCount":358,"publicEvidenceCount":359,"organizations":360,"bestGrade":341,"headline":364,"lastVerified":231,"indexable":202},"AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[18],[20,353],"operations",[355,23,333,24,356],"classification-and-routing","summarization","back-office",9,7,[285,361,362,338,363,237,340],"Allianz Partners","Hiscox","Sedgwick",{"kpi":41,"label":365,"unit":344,"n":315,"nUpTo":315,"kind":345,"value":366,"qualifier":367,"claimant":199,"organization":338,"vendorReported":181},"Automation rate",55,"approximately",{"slug":168,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":373,"patterns":375,"audience":357,"autonomy":377,"adoptionStage":378,"segment":20,"evidenceCount":334,"publicEvidenceCount":334,"organizations":379,"bestGrade":341,"headline":211,"lastVerified":170,"indexable":202},"AI for insurance claims fraud detection","Claims fraud detection","AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.",[18],[20,374],"fraud-prevention",[376,23,333,22,355],"anomaly-detection","assist","mainstream",[380,381,382,338,237],"Assurant","AXA Switzerland","General Insurance Association of Singapore",{"slug":169,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":388,"patterns":390,"audience":357,"autonomy":377,"adoptionStage":31,"segment":20,"evidenceCount":391,"publicEvidenceCount":312,"organizations":392,"bestGrade":341,"headline":211,"lastVerified":170,"indexable":202},"AI for subrogation opportunity detection","Subrogation detection","AI that reads open and closed claims to find cases where a third party is wholly or partly liable, estimates liability and the recoverable amount under the applicable negligence and recovery rules, and sends scored recovery opportunities with their reasons to the subrogation team.",[18],[20,389],"collections-and-recovery",[355,23,333,356],4,[393,394],"Central Insurance","Elephant Insurance",{"indexable":202,"reasons":396},[],[398,404,409,416,424,430,437,443,450,457,464,470,477,484,490,495,502,508,514,520,526,532,538,543,548,554,561,566,572,579,585,591,598,603],{"id":136,"label":399,"issuer":400,"region":144,"url":401,"description":402,"useCases":403,"indexable":202},"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.",197,{"id":137,"label":405,"issuer":400,"region":144,"url":406,"description":407,"useCases":408,"indexable":202},"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.",180,{"id":139,"label":410,"issuer":411,"region":412,"url":413,"description":414,"useCases":415,"indexable":202},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":417,"label":418,"issuer":419,"region":420,"url":421,"description":422,"useCases":423,"indexable":202},"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.",83,{"id":425,"label":426,"issuer":400,"region":144,"url":427,"description":428,"useCases":429,"indexable":202},"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":431,"label":432,"issuer":433,"region":144,"url":434,"description":435,"useCases":436,"indexable":202},"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.",64,{"id":138,"label":438,"issuer":439,"region":144,"url":440,"description":441,"useCases":442,"indexable":202},"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.",47,{"id":444,"label":445,"issuer":446,"region":239,"url":447,"description":448,"useCases":449,"indexable":202},"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.",36,{"id":451,"label":452,"issuer":453,"region":239,"url":454,"description":455,"useCases":456,"indexable":202},"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":458,"label":459,"issuer":460,"region":412,"url":461,"description":462,"useCases":463,"indexable":202},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":465,"label":466,"issuer":467,"region":420,"url":468,"description":469,"useCases":463,"indexable":202},"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.",{"id":471,"label":472,"issuer":473,"region":144,"url":474,"description":475,"useCases":476,"indexable":202},"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.",16,{"id":478,"label":479,"issuer":480,"region":412,"url":481,"description":482,"useCases":483,"indexable":202},"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":485,"label":486,"issuer":400,"region":144,"url":487,"description":488,"useCases":489,"indexable":202},"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":491,"label":492,"issuer":400,"region":144,"url":493,"description":494,"useCases":489,"indexable":202},"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.",{"id":496,"label":497,"issuer":498,"region":420,"url":499,"description":500,"useCases":501,"indexable":202},"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":503,"label":504,"issuer":400,"region":144,"url":505,"description":506,"useCases":507,"indexable":202},"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":509,"label":510,"issuer":511,"region":420,"url":512,"description":513,"useCases":507,"indexable":202},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":515,"label":516,"issuer":517,"region":412,"url":518,"description":519,"useCases":507,"indexable":202},"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":521,"label":522,"issuer":400,"region":144,"url":523,"description":524,"useCases":525,"indexable":202},"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.",11,{"id":527,"label":528,"issuer":529,"region":420,"url":530,"description":531,"useCases":525,"indexable":202},"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":533,"label":534,"issuer":446,"region":239,"url":535,"description":536,"useCases":537,"indexable":202},"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.",10,{"id":539,"label":540,"issuer":400,"region":144,"url":541,"description":542,"useCases":537,"indexable":202},"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":544,"label":545,"issuer":400,"region":144,"url":546,"description":547,"useCases":537,"indexable":202},"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":549,"label":550,"issuer":551,"region":144,"url":552,"description":553,"useCases":358,"indexable":202},"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.",{"id":555,"label":556,"issuer":557,"region":420,"url":558,"description":559,"useCases":560,"indexable":202},"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.",8,{"id":562,"label":563,"issuer":400,"region":144,"url":564,"description":565,"useCases":560,"indexable":202},"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.",{"id":567,"label":568,"issuer":400,"region":144,"url":569,"description":570,"useCases":571,"indexable":202},"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":573,"label":574,"issuer":575,"region":576,"url":577,"description":578,"useCases":334,"indexable":202},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":580,"label":581,"issuer":582,"region":144,"url":583,"description":584,"useCases":391,"indexable":202},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":586,"label":587,"issuer":588,"region":144,"url":589,"description":590,"useCases":391,"indexable":202},"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":592,"label":593,"issuer":594,"region":239,"url":595,"description":596,"useCases":597,"indexable":202},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":599,"label":600,"issuer":400,"region":144,"url":601,"description":602,"useCases":597,"indexable":202},"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":604,"label":605,"issuer":606,"region":420,"url":607,"description":608,"useCases":597,"indexable":202},"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.",1790598300999]