[{"data":1,"prerenderedAt":530},["ShallowReactive",2],{"uc-subrogation-opportunity-detection":3,"uc-regulations":317},{"useCase":4,"evidence":173,"blitsAiDeployments":231,"benchmarks":232,"indicative":233,"related":236,"indexability":315,"includeUnpublished":179},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":19,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":43,"macroEstimates":78,"feasibility":79,"implementation":91,"risk":130,"blitsAi":151,"faq":153,"related":163,"datePublished":168,"dateModified":168,"lastVerified":168,"changelog":169,"slug":172},"AI for subrogation opportunity detection","Subrogation detection","AI subrogation detection for insurance claims","AI flags claims a third party caused and scores the recovery. Central Insurance uses it; Shift reports over USD 1 million a month recovered at a top 25 US insurer.","published","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.",[12,13,14,15],"subrogation AI","recovery opportunity detection","claims recovery analytics","AI subrogation referral",[17],"insurance",[19,20],"claims","collections-and-recovery",[22,23,24,25],"classification-and-routing","prediction-and-scoring","document-processing","summarization",[27,28],"internal-tools","api","back-office","assist","early-adopters","When an insurer pays a claim that someone else caused, it can recover the money from that party or\nits insurer. In practice many of those recoveries are never pursued. Handlers focus on settling the\nclaim for the customer, the signs of third party liability sit in free text notes, police reports\nand photos, and the rules on comparative negligence and recovery differ by state or country.\n\nReferrals to the subrogation team therefore depend on individual handlers spotting the opportunity,\noften too late, when evidence is gone or deadlines have passed. Recovery teams in turn spend time on\nreferrals with little chance of success. Missed subrogation is a quiet form of claims leakage: no\ncustomer complains about it, so it rarely surfaces on its own.",[],"1. **Read every claim early.** The AI reads the claim notes, statements, police reports and other\n   documents from the first days of the claim, not only when a handler refers it.\n2. **Identify who else is responsible.** It extracts the parties and facts and assesses whether a\n   third party, product, contractor or other insurer may be liable.\n3. **Apply the rules.** It checks the applicable comparative negligence, recovery and limitation\n   rules for the jurisdiction and line of business.\n4. **Estimate and score.** It estimates liability shares and the recoverable amount and scores the\n   opportunity by expected recovery.\n5. **Refer with reasons.** Scored alerts with the supporting facts and rules go to the subrogation\n   team, which decides whether to pursue, and outcomes feed back into the model.",[36,37,38],"risk-reduction","employee-productivity","speed",[40,41,42],"revenue-recovered","handling-time-reduction","productivity-gain",{"referenceOrg":44,"inputs":45,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"An auto and property insurer paying USD 500 million in claims a year",[46,52,59,66],{"key":47,"label":48,"low":49,"high":49,"unit":50,"note":51},"claimsPaid","Claims paid per year",500000000,"USD per year","The reference insurer.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"recoverableShare","Share of claims paid that is recoverable from third parties",0.03,0.06,"fraction of claims paid","Editorial assumption; depends heavily on the lines of business and jurisdictions. Replace with your own recovery history.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"missedShare","Share of recoverable amounts not pursued today",0.1,0.25,"fraction of recoverable amounts","Editorial assumption; estimate it by auditing a sample of closed claims.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"collectedShare","Share of newly found opportunities actually collected",0.4,0.7,"fraction of opportunities","Editorial assumption; not every liable party pays in full.","claimsPaid * recoverableShare * missedShare * collectedShare","USD","per year","Additional recoveries collected","Gross recoveries only. It leaves out the cost of pursuing recoveries (staff, legal, arbitration fees), the time value of money, the reduction in customer excess where recoveries are shared with the policyholder, and the cost of the platform.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":86},"medium","The model needs claim notes and documents plus machine readable recovery rules per jurisdiction. Integration is mostly read only on the claims system plus a referral into the recovery workflow, so the operational risk is modest; the effort is in the rules content and in feedback from recovery outcomes.",[83,84,85],"Claim notes, statements, police reports and photos linked to each claim","Historical subrogation referrals with outcomes and amounts recovered","Comparative negligence, recovery and limitation rules per jurisdiction and line",[87,88,89,90],"Claims management system (read access to claims, notes and payments)","Subrogation or recovery workflow and case management","Document storage for police reports and evidence","Inter company arbitration or recovery platforms where used",{"steps":92,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":120},[93,96,99,102,105],{"title":94,"detail":95},"Audit a sample of closed claims","Have experienced recovery staff review a few hundred closed claims to estimate how much was missed and why. It sizes the prize and creates the first labelled data.",{"title":97,"detail":98},"Start with one line and one jurisdiction set","Auto physical damage and the personal injury protection and medical payment rules of a few states or countries are typical starting points, because volume is high and the recovery rules for these exposures are written down per state. The top 25 US insurer on this page started with its auto subrogation team and added property later.",{"title":100,"detail":101},"Run next to handler referrals","Keep manual referrals and let the AI add its own, so you can measure how many opportunities it finds that people missed and how early.",{"title":103,"detail":104},"Tune to what the team accepts","Track which alerts the recovery team accepts and what is collected, and set thresholds to the team's capacity rather than the model's recall.",{"title":106,"detail":107},"Keep the rules current","Recovery law and limitation periods change. Give the rules an owner and a review cycle, and version them with the model.",[109,110,111,112],"The AI refers opportunities; people decide whether to pursue and what to demand","Every alert shows the facts, liability reasoning and the rule applied","Limitation deadlines tracked from the alert so late referrals are visible","The policyholder's claim is never delayed or reduced because of a recovery opportunity","The subrogation team reviews every alert, decides on pursuit and negotiates recoveries. Claims handlers can still refer claims manually, and recovery leads approve changes to rules and thresholds.",[115,116,117,118,119],"Share of alerts accepted by the recovery team","Amount recovered from AI raised opportunities per month","Days from first notice of loss to subrogation referral","Opportunities found by the AI that handlers had not referred","Recoveries lost to limitation deadlines",[121,124,127],{"title":122,"detail":123},"Alerts nobody pursues","The model raises more opportunities than the team can work, and value is lost anyway. Match thresholds to capacity and prioritise by expected recovery.",{"title":125,"detail":126},"Wrong law, wrong demand","Outdated or misapplied negligence rules lead to demands that fail. Version the rules and review them on a schedule.",{"title":128,"detail":129},"Handlers stop referring","Teams assume the AI will catch everything. Keep manual referral and measure both sources.",{"euAiAct":131,"regulations":134,"guidance":139,"controls":146,"incidents":150},{"tier":132,"basis":133},"minimal","Detecting recovery opportunities against third parties and other insurers is not listed in Annex III: point 5(c) covers only risk assessment and pricing of natural persons in life and health insurance, and the system does not decide on a natural person's access to a service. It is an internal tool that does not converse with the public or publish generated content, so the deployer transparency duties of Article 50 do not apply. Personal data in claim files, including data about the third party, is still subject to GDPR.",[135,136,137,138],"eu-ai-act","gdpr","iso-42001","nist-ai-rmf",[140],{"title":141,"issuer":142,"region":143,"url":144,"note":145},"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","Sets risk based, proportionate expectations (data governance, explainability, human oversight) for insurers' AI systems that are neither prohibited nor high risk under the AI Act, which includes back office claims models like this one.",[147,148,149],"Versioned recovery rules with an owner and review dates","Log of alerts, decisions and recovery outcomes for model monitoring","Access controls on claim files used by the model",[],{"howToBuild":152},"On Blits.ai this is an **agentic workflow** that runs on a schedule or on claim events through the\nAPI. An **AI agent** reads the claim notes and documents ingested into the **knowledge base**,\nretrieves the applicable recovery rules with **hybrid retrieval**, and returns a **structured\noutput** with parties, liability reasoning, the rule applied and an estimated recovery. **Custom\nfunctions** read the claim from the claims system and create the referral in the recovery workflow.\n\n**Human in the loop approval** keeps the decision to pursue with the recovery team, the **audit\ntrail** records every run, and **test suites** grade the agent against claims where the recovery\noutcome is known. The platform is model agnostic, so the insurer can choose the model per task.",[154,157,160],{"question":155,"answer":156},"How much can AI add to subrogation recoveries?","Published results are vendor figures for unnamed insurers. Shift Technology reports a recurring average recovery of over USD 1 million per month for a top 25 US property and casualty insurer, and an acceptance rate of 60% or more for a small regional insurer. Size it on your own book by auditing closed claims first.",{"question":158,"answer":159},"Does AI replace handler referrals to subrogation?","No, it adds to them. Central Insurance still asks its handlers to refer claims to its recovery team and uses the AI to catch the ones they miss, often on the first day of the claim.",{"question":161,"answer":162},"Which lines of business are the best starting point?","Auto claims, including personal injury protection and medical payments recoveries, because volumes are high and the state recovery rules can be encoded. The top 25 US insurer on this page started with auto and later extended the system to property; the system also draws on external data such as product recall lists.",[164,165,166,167],"claims-triage-and-straight-through-processing","claims-fraud-detection","photo-based-damage-assessment","claims-first-notice-of-loss-agent","2026-09-27",[170],{"date":168,"note":171},"First published","subrogation-opportunity-detection",[174,207],{"title":175,"useCases":176,"organization":177,"vendors":182,"summary":186,"stage":187,"year":188,"channels":189,"languages":190,"metrics":192,"outcomeDisclosed":179,"sources":193,"verification":202,"grade":204,"id":205,"organizationSlug":206},"Elephant Insurance: AI subrogation detection",[172],{"name":178,"anonymized":179,"country":180,"region":181,"industry":17},"Elephant Insurance",false,"US","north-america",[183],{"name":184,"role":185},"Shift Technology","platform","Elephant Insurance, a Virginia based auto insurer owned by Admiral Group, added Shift Subrogation Detection in November 2024 after using Shift for underwriting fraud since 2021 and claims fraud since 2020. Elephant's head of claims says the model helps the insurer find subrogation opportunities at scale and recommends handler actions to improve recovery, alongside the fraud detection already in place. No recovery figures were published.","production",2024,[27],[191],"en",[],[194,199],{"url":195,"title":196,"publisher":178,"date":197,"archivedUrl":198},"https://www.elephant.com/newsroom/press-releases/elephant-insurance-to-expand-relationship-with-shift-technology","Elephant Insurance to Expand Relationship with Shift Technology","2024-11-13","https://web.archive.org/web/20241206005506/https://www.elephant.com/newsroom/press-releases/elephant-insurance-to-expand-relationship-with-shift-technology",{"url":200,"title":201,"publisher":184},"https://www.shift-technology.com/resources/news/elephant-insurance-expands-relationship-with-shift-technology","Elephant Insurance Expands Relationship with Shift Technology",{"level":203,"checkedAt":168},"source-verified","B","elephant-insurance-subrogation-detection",null,{"title":208,"useCases":209,"organization":210,"vendors":212,"summary":214,"stage":187,"year":215,"channels":216,"languages":217,"metrics":218,"outcomeDisclosed":179,"sources":219,"verification":227,"grade":229,"id":230,"organizationSlug":206},"Central Insurance: AI subrogation detection",[172],{"name":211,"anonymized":179,"country":180,"region":181,"industry":17},"Central Insurance",[213],{"name":184,"role":185},"Central Insurance, a US insurer, added Shift Subrogation Detection in 2023 after three years of using Shift to detect claims fraud. The system reviews claims for signs that a third party is wholly or partly responsible, often on the first day of the claim, and gives the recovery team alerts with the facts, comparative negligence rules and state recovery laws for PIP and medical payments. Handlers still refer claims to subrogation; the AI catches the ones they miss. Its claims recovery supervisor reports more referrals and hours saved every week, but no figures were published.",2023,[27],[191],[],[220,224],{"url":221,"title":222,"publisher":184,"date":223},"https://www.shift-technology.com/resources/news/central-insurance-expands-relationship-with-shift-technology","Central Insurance Expands Relationship with Shift Technology","2023-06-28",{"url":225,"title":226,"publisher":184},"https://www.shift-technology.com/resources/reports-and-insights/subrogation-central-insurance","Central Insurance: The benefits of AI in subrogation",{"level":203,"checkedAt":228},"2026-09-26","C","central-insurance-subrogation-detection",0,[],{"low":234,"high":235},600000,5250000,[237,267,283,295],{"slug":164,"title":238,"shortTitle":239,"definition":240,"status":9,"industries":241,"functions":242,"patterns":244,"audience":29,"autonomy":246,"adoptionStage":31,"segment":19,"evidenceCount":247,"publicEvidenceCount":248,"organizations":249,"bestGrade":204,"headline":257,"lastVerified":228,"indexable":266},"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.",[17],[19,243],"operations",[22,23,24,245,25],"agentic-workflow","supervised-agent",9,7,[250,251,252,253,254,255,256],"Admiral Seguros","Allianz Partners","Hiscox","Lemonade","Sedgwick","Tokio Marine & Nichido Fire Insurance","Travelers",{"kpi":258,"label":259,"unit":260,"n":261,"nUpTo":261,"kind":262,"value":263,"qualifier":264,"claimant":265,"organization":253,"vendorReported":179},"automation-rate","Automation rate","percent",1,"reported",55,"approximately","organization",true,{"slug":165,"title":268,"shortTitle":269,"definition":270,"status":9,"industries":271,"functions":272,"patterns":274,"audience":29,"autonomy":30,"adoptionStage":277,"segment":19,"evidenceCount":278,"publicEvidenceCount":278,"organizations":279,"bestGrade":204,"headline":206,"lastVerified":168,"indexable":266},"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.",[17],[19,273],"fraud-prevention",[275,23,24,276,22],"anomaly-detection","computer-vision","mainstream",5,[280,281,282,253,255],"Assurant","AXA Switzerland","General Insurance Association of Singapore",{"slug":166,"title":284,"shortTitle":285,"definition":286,"status":9,"industries":287,"functions":288,"patterns":289,"audience":290,"autonomy":246,"adoptionStage":31,"segment":19,"evidenceCount":278,"publicEvidenceCount":278,"organizations":291,"bestGrade":229,"headline":206,"lastVerified":168,"indexable":266},"AI for photo based damage assessment in insurance claims","Photo damage assessment","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.",[17],[19],[276,23,245],"customer-facing",[250,292,293,294,255],"Covéa","Foyer","PZU",{"slug":167,"title":296,"shortTitle":297,"definition":298,"status":9,"industries":299,"functions":300,"patterns":302,"audience":290,"autonomy":246,"adoptionStage":31,"segment":19,"evidenceCount":278,"publicEvidenceCount":278,"organizations":305,"bestGrade":204,"headline":309,"lastVerified":168,"indexable":266},"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.",[17],[19,301],"customer-service",[303,304,245,24],"conversational-agent","voice-agent",[306,307,253,308,256],"DOMCURA","Hippo","Progressive",{"kpi":310,"label":311,"unit":260,"n":261,"nUpTo":231,"kind":262,"value":312,"qualifier":313,"claimant":314,"organization":306,"vendorReported":266},"accuracy","Accuracy",90,"exact","vendor",{"indexable":266,"reasons":316},[],[318,324,329,336,342,348,355,362,370,377,384,390,397,404,410,415,422,428,434,440,446,452,458,463,468,474,481,486,492,499,506,512,519,524],{"id":135,"label":319,"issuer":320,"region":143,"url":321,"description":322,"useCases":323,"indexable":266},"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":136,"label":325,"issuer":320,"region":143,"url":326,"description":327,"useCases":328,"indexable":266},"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":137,"label":330,"issuer":331,"region":332,"url":333,"description":334,"useCases":335,"indexable":266},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":138,"label":337,"issuer":338,"region":181,"url":339,"description":340,"useCases":341,"indexable":266},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":343,"label":344,"issuer":320,"region":143,"url":345,"description":346,"useCases":347,"indexable":266},"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":349,"label":350,"issuer":351,"region":143,"url":352,"description":353,"useCases":354,"indexable":266},"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":356,"label":357,"issuer":358,"region":143,"url":359,"description":360,"useCases":361,"indexable":266},"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.",47,{"id":363,"label":364,"issuer":365,"region":366,"url":367,"description":368,"useCases":369,"indexable":266},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":371,"label":372,"issuer":373,"region":366,"url":374,"description":375,"useCases":376,"indexable":266},"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":378,"label":379,"issuer":380,"region":332,"url":381,"description":382,"useCases":383,"indexable":266},"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":385,"label":386,"issuer":387,"region":181,"url":388,"description":389,"useCases":383,"indexable":266},"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":391,"label":392,"issuer":393,"region":143,"url":394,"description":395,"useCases":396,"indexable":266},"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":398,"label":399,"issuer":400,"region":332,"url":401,"description":402,"useCases":403,"indexable":266},"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":405,"label":406,"issuer":320,"region":143,"url":407,"description":408,"useCases":409,"indexable":266},"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":411,"label":412,"issuer":320,"region":143,"url":413,"description":414,"useCases":409,"indexable":266},"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":416,"label":417,"issuer":418,"region":181,"url":419,"description":420,"useCases":421,"indexable":266},"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":423,"label":424,"issuer":320,"region":143,"url":425,"description":426,"useCases":427,"indexable":266},"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":429,"label":430,"issuer":431,"region":181,"url":432,"description":433,"useCases":427,"indexable":266},"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":435,"label":436,"issuer":437,"region":332,"url":438,"description":439,"useCases":427,"indexable":266},"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":441,"label":442,"issuer":320,"region":143,"url":443,"description":444,"useCases":445,"indexable":266},"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":447,"label":448,"issuer":449,"region":181,"url":450,"description":451,"useCases":445,"indexable":266},"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":453,"label":454,"issuer":365,"region":366,"url":455,"description":456,"useCases":457,"indexable":266},"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":459,"label":460,"issuer":320,"region":143,"url":461,"description":462,"useCases":457,"indexable":266},"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":464,"label":465,"issuer":320,"region":143,"url":466,"description":467,"useCases":457,"indexable":266},"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":469,"label":470,"issuer":471,"region":143,"url":472,"description":473,"useCases":247,"indexable":266},"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":475,"label":476,"issuer":477,"region":181,"url":478,"description":479,"useCases":480,"indexable":266},"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":482,"label":483,"issuer":320,"region":143,"url":484,"description":485,"useCases":480,"indexable":266},"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":487,"label":488,"issuer":320,"region":143,"url":489,"description":490,"useCases":491,"indexable":266},"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":493,"label":494,"issuer":495,"region":496,"url":497,"description":498,"useCases":278,"indexable":266},"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":500,"label":501,"issuer":502,"region":143,"url":503,"description":504,"useCases":505,"indexable":266},"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":507,"label":508,"issuer":509,"region":143,"url":510,"description":511,"useCases":505,"indexable":266},"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":513,"label":514,"issuer":515,"region":366,"url":516,"description":517,"useCases":518,"indexable":266},"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":520,"label":521,"issuer":320,"region":143,"url":522,"description":523,"useCases":518,"indexable":266},"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":525,"label":526,"issuer":527,"region":181,"url":528,"description":529,"useCases":518,"indexable":266},"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.",1790598301723]