[{"data":1,"prerenderedAt":659},["ShallowReactive",2],{"uc-claims-fraud-detection":3,"uc-regulations":452},{"useCase":4,"evidence":193,"blitsAiDeployments":341,"benchmarks":342,"indicative":354,"related":356,"indexability":450,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":19,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":54,"macroEstimates":82,"feasibility":83,"implementation":97,"risk":140,"blitsAi":170,"faq":172,"related":182,"datePublished":188,"dateModified":188,"lastVerified":188,"changelog":189,"slug":192},"AI for insurance claims fraud detection","Claims fraud detection","Insurance claims fraud detection with AI","AI scores insurance claims for fraud at first notice of loss. Shift reports AXA Switzerland analysed over 1 million claims and stopped over EUR 12 million in fraud.","published","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.",[12,13,14,15],"insurance fraud detection","claims fraud scoring","SIU referral scoring","fraud network analytics for claims",[17],"insurance",[19,20],"claims","fraud-prevention",[22,23,24,25,26],"anomaly-detection","prediction-and-scoring","document-processing","computer-vision","classification-and-routing",[28,29],"api","internal-tools","back-office","assist","mainstream","Fraud hides among honest claims. Most suspicious claims look normal when viewed alone: a slightly\ninflated invoice, a staged accident with credible witnesses, a repair shop or clinic that appears\nin too many claims, a photo reused from another insurer. Traditional detection relies on business\nrules and on handlers noticing something odd, which produces many false alerts and misses the\norganised schemes that span claims and insurers.\n\nThe pressure grows as insurers speed claims up. Faster payment and straight through processing are\nwhat customers want, and exactly what fraudsters exploit. Special investigations units are small,\nso the quality of each referral matters more than the number of alerts.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"The Coalition Against Insurance Fraud states that insurance fraud steals at least USD 308.6 billion every year from American consumers and that fraud occurs in about 10% of property and casualty insurance losses.","Insurance Fraud Statistics: $308.6B Stolen Every Year","https://insurancefraud.org/fraud-stats/",2026,"1. **Score at first notice of loss.** Each new claim is scored in real time so honest claims can\n   go straight to processing and suspicious ones are held before payment.\n2. **Combine many signals.** Models use claim and policy history, the text of notes and\n   documents, images, and external data such as industry databases and public records.\n3. **Look across claims.** Network analysis links people, vehicles, addresses, repairers and\n   providers across claims and, through industry initiatives, across insurers.\n4. **Explain every alert.** Each alert states the scenario and the facts behind it, so a handler\n   or investigator can decide quickly whether to refer, investigate or clear it.\n5. **Keep watching.** The score is recalculated as new information arrives, and investigation\n   outcomes feed back into the models.",[42,43,44,45],"risk-reduction","cost-to-serve","speed","employee-productivity",[47,48,49,50,51,52,53],"fraud-losses-prevented","fraud-loss-reduction","detection-rate-improvement","false-positive-reduction","interactions-handled","cost-savings","accuracy",{"referenceOrg":55,"inputs":56,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A property and casualty insurer paying USD 1 billion in claims a year",[57,63,70],{"key":58,"label":59,"low":60,"high":60,"unit":61,"note":62},"claimsPaid","Claims paid per year",1000000000,"USD per year","The reference insurer.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69,"sourceUrl":38},"fraudShare","Share of claims losses affected by fraud",0.05,0.1,"fraction of claims paid","The high value follows the Coalition Against Insurance Fraud statement (a US figure) that fraud occurs in about 10% of property and casualty losses; the low value is an editorial assumption.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"additionalStopped","Share of that fraud additionally stopped thanks to AI detection",0.02,0.06,"fraction of fraudulent losses","Editorial assumption; replace with results from a controlled pilot on your own book.","claimsPaid * fraudShare * additionalStopped","USD","per year","Additional fraudulent payments avoided","Avoided fraudulent payments only. It leaves out investigator time saved by better referrals, the faster payment of honest claims, the deterrent effect, the cost of investigations and the cost of the platform. The share of fraud stopped varies widely by line and market.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"high","Scoring itself is well established, often through specialised vendors. The effort is in data: joining claims, policy, payment, document and external data, labelling past investigation outcomes, and fitting alerts into the handler and investigator workflow without flooding it.",[87,88,89,90],"Claims, policy and payment history with investigation outcomes as labels","Claim notes, documents and images linked to each claim","External data such as industry fraud databases, public records and sanctions lists where lawful","Scenario definitions agreed with the special investigations unit",[92,93,94,95,96],"Claims management system for real time scoring and holds on payment","Special investigations unit case management","Industry fraud data sharing schemes and databases","Document and image analysis services","Subrogation and triage models in the same claims flow",{"steps":98,"guardrails":114,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[99,102,105,108,111],{"title":100,"detail":101},"Agree what a good referral is","With the investigators, define the scenarios that matter per line of business and what an accepted referral looks like; that becomes the target and the main quality measure.",{"title":103,"detail":104},"Back test on closed claims","Score several years of closed claims and compare alerts with known fraud and with the current rules, at the same number of alerts investigators can handle.",{"title":106,"detail":107},"Score at first notice of loss","Move scoring to the start of the claim so honest claims are not slowed down and suspicious ones are held before payment.",{"title":109,"detail":110},"Put reasons in front of people","Show each alert with its scenario and facts inside the handler's screen and track the decision taken, so feedback is captured for every alert.",{"title":112,"detail":113},"Join industry data sharing","Organised fraud crosses insurers. Industry schemes that share claims data under clear legal bases find rings that no insurer sees alone.",[115,116,117,118,119],"The AI raises alerts; people decide on refusal, investigation and any report to authorities","Every alert carries its scenario and supporting facts, and alerts without reasons are not actioned","Protected characteristics and close proxies excluded from features, with fairness testing across customer groups","Honest customers are not delayed beyond a set time by a pending alert without a human decision","Data sharing with other insurers only under documented legal bases and agreements","Handlers and investigators review every alert and decide on the next step; no claim is refused on a score alone. Investigation outcomes are recorded and feed the models, and the special investigations unit approves changes to scenarios and thresholds.",[122,123,124,125,126],"Share of alerts accepted for investigation and share confirmed as fraud","Fraud stopped per period, compared with the pre AI baseline on the same lines","Alerts per investigator and time to decision per alert","Payment delay caused to claims that turned out to be honest","Complaints and appeals linked to fraud holds",[128,131,134,137],{"title":129,"detail":130},"Alert floods","Too many low quality alerts teach handlers to ignore them. Tune to investigator capacity and measure acceptance, not volume.",{"title":132,"detail":133},"Bias against groups of customers","Features such as postcode that stand in for ethnicity or age treat honest customers as suspects. Test outcomes across groups and remove proxies.",{"title":135,"detail":136},"Honest customers punished for speed","Suspicious claims are held but nobody looks at them, so honest customers wait. Set a service level for every held claim.",{"title":138,"detail":139},"Models that fall behind fraudsters","Schemes change quickly, for example with AI generated documents and images. Retrain on recent outcomes and add image and document integrity checks.",{"euAiAct":141,"regulations":144,"guidance":151,"controls":163,"incidents":169},{"tier":142,"basis":143},"context-dependent","Claims fraud detection by an insurer is not listed in Annex III, and point 5(b) explicitly excludes AI systems used to detect financial fraud from the credit scoring category. Point 5(c) covers only risk assessment and pricing in life and health insurance, so a fraud model becomes high risk when it also feeds those decisions, or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim public assistance benefits (point 5(a)). Profiling and automated decisions remain subject to GDPR, including Article 22 where a claim is refused on a decision based solely on automated processing.",[145,146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","iso-42001","nist-ai-rmf",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"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","Calls for fairness, data governance, explainability and human oversight of AI systems used by insurers, proportionate to their impact on customers.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Shows which insurance and public benefit uses are high risk and that fraud detection is carved out of the credit scoring category.",[164,165,166,167,168],"Documented scenarios and thresholds, approved by the special investigations unit","Fairness testing of alert rates and outcomes across customer groups","Audit log of every alert, its reasons and the human decision taken","Service level for claims on hold because of a fraud alert","Legal basis and data sharing agreements for external and industry data",[],{"howToBuild":171},"Blits.ai is not a fraud scoring engine; insurers use their own models or a specialised vendor and\nconnect them through **custom functions**. Blits.ai adds the investigation workflow around the\nscore: an **agentic workflow** that, when an alert fires, gathers the claim file, documents and\nearlier claims through **custom functions** and **SQL knowledge bases**, and has an **AI agent**\nwrite a structured alert summary with the reasons and open questions for the investigator.\n\n**Human in the loop approval** steps put holds, referrals and letters to the customer in front of a\nperson before they happen, the **audit trail** records every step of each run, and a **tool\nexecution policy** limits which functions the agent may call. **Test suites** check summaries\nagainst known cases, and the platform runs in EU or UAE regions where claims data must stay local.",[173,176,179],{"question":174,"answer":175},"How much fraud can AI detection stop?","Among the deployments on this page, the only quantified result is a cumulative total, not a rate: Shift Technology reports that AXA Switzerland has analysed more than 1 million claims with its real time detection and stopped over EUR 12 million in fraud. Lemonade says only that its fraud system has helped it avoid millions of dollars of potential losses. Results depend on the line, the market and how well alerts fit the investigators' workflow.",{"question":177,"answer":178},"Why score at first notice of loss rather than later in the claim?","Early scoring lets honest claims move straight to processing while suspicious ones are held before money leaves the insurer. AXA Switzerland chose detection at first notice of loss for exactly that reason, and reruns the models whenever new claim data arrives.",{"question":180,"answer":181},"Can insurers detect fraud rings that span several companies?","Yes, through industry data sharing. Member insurers of the General Insurance Association of Singapore analyse travel and motor claims together to find links between people, providers and claims that look genuine to each insurer alone.",[183,184,185,186,187],"claims-triage-and-straight-through-processing","claims-first-notice-of-loss-agent","photo-based-damage-assessment","subrogation-opportunity-detection","application-and-identity-fraud-detection","2026-09-27",[190],{"date":188,"note":191},"First published","claims-fraud-detection",[194,241,269,301,320],{"title":195,"useCases":196,"organization":197,"vendors":202,"summary":205,"stage":206,"year":207,"channels":208,"languages":211,"metrics":213,"outcomeDisclosed":229,"sources":230,"verification":235,"grade":238,"id":239,"organizationSlug":240},"Lemonade: AI Jim claims bot for first notice of loss and automated settlement",[184,183,192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":17},"Lemonade",false,"US","north-america",[203],{"name":198,"role":204},"in-house","Lemonade's claims bot AI Jim takes the first notice of loss in a chat with the customer, pays or declines simple claims within seconds and assigns the claims it may not settle, or has concerns about, to human claims experts based on their specialty, workload and schedule. A separate system, Forensic Graph, uses machine learning to predict, detect and block fraud across the customer engagement. The annual report states that AI Jim took the first notice of loss without human intervention 96% of the time and that roughly 55% of claims were automated from start to finish, both as of December 31, 2025.","scaled",2025,[209,210],"mobile-app","web-chat",[212],"en",[214,223],{"kpi":215,"value":216,"unit":217,"qualifier":218,"period":219,"claimant":220,"quote":221,"sourceUrl":222},"containment-rate",96,"percent","exact","First notice of loss taken without human intervention, as of December 31, 2025","organization","AI Jim is our claims bot, and, as of December 31, 2025, 96% of the time, it is AI Jim that will take the first notice of loss from a Lemonade customer without human intervention","https://www.sec.gov/Archives/edgar/data/1691421/000169142126000016/lmnd-20251231.htm",{"kpi":224,"value":225,"unit":217,"qualifier":226,"period":227,"claimant":220,"quote":228,"sourceUrl":222},"automation-rate",55,"approximately","Share of claims automated end to end, as of December 31, 2025","As of December 31, 2025, roughly 55% of our claims were automated, resulting in instant or near-instant processing from start to finish.",true,[231],{"url":222,"title":232,"publisher":233,"date":234},"Lemonade, Inc. Annual Report on Form 10-K for the fiscal year ended December 31, 2025","U.S. Securities and Exchange Commission (EDGAR)","2026-02-25",{"level":236,"checkedAt":237},"source-verified","2026-09-26","B","lemonade-ai-jim-claims-automation",null,{"title":242,"useCases":243,"organization":244,"vendors":248,"summary":252,"stage":253,"year":207,"channels":254,"languages":255,"metrics":257,"outcomeDisclosed":199,"sources":258,"verification":266,"grade":267,"id":268,"organizationSlug":240},"Tokio Marine & Nichido Fire: AI review of damage photos, estimates and suspicious claims",[183,192,185],{"name":245,"anonymized":199,"country":246,"region":247,"industry":17},"Tokio Marine & Nichido Fire Insurance","JP","asia-pacific",[249],{"name":250,"role":251},"Shift Technology","platform","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.","production",[29],[256],"ja",[],[259,262],{"url":260,"title":261,"publisher":250},"https://www.shift-technology.com/resources/case-studies/tokiomarine_casestudy","Case Study: Tokyo Marine & Nichido Fire Insurance Co., Ltd.",{"url":263,"title":264,"publisher":250,"date":265},"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":236,"checkedAt":237},"C","tokio-marine-nichido-shift-claims-review",{"title":270,"useCases":271,"organization":272,"vendors":275,"summary":277,"stage":206,"year":278,"channels":279,"languages":280,"metrics":281,"outcomeDisclosed":229,"sources":295,"verification":299,"grade":267,"id":300,"organizationSlug":240},"AXA Switzerland: real time claims fraud detection at first notice of loss",[192],{"name":273,"anonymized":199,"country":274,"region":155,"industry":17},"AXA Switzerland","CH",[276],{"name":250,"role":251},"AXA Switzerland checks motor and property claims for fraud in real time at first notice of loss with Shift Claims Fraud Detection, using more than 100 fraud scenarios tuned to the Swiss market and its portfolio, and combining its own policy and claims data with external sources such as government records. Honest claims go straight to processing, suspicious ones go to an expert with the full context of the alert, and the models run again whenever new data is recorded on a claim. Shift reports that AXA has analysed more than 1 million claims and stopped over EUR 12 million in fraud.",2023,[28],[],[282,290],{"kpi":51,"value":283,"unit":284,"qualifier":285,"period":286,"claimant":287,"quote":288,"sourceUrl":289},1000000,"count","at-least","Claims analysed since deployment","vendor","AXA has now analyzed more than 1 million claims with Shift, and stopped over €12M in fraud, freeing its teams to focus on customer satisfaction and achieve the goal of increasing its presence as #1 in the Swiss market.","https://www.shift-technology.com/resources/case-studies/axa-switzerland-insurance-fraud-detection-success",{"kpi":47,"value":291,"unit":292,"currency":293,"qualifier":285,"period":294,"claimant":287,"quote":288,"sourceUrl":289},12000000,"currency","EUR","Fraud stopped since deployment, cumulative",[296],{"url":289,"title":297,"publisher":250,"date":298},"AXA Switzerland stops fraud in real-time to drive customer satisfaction","2023-03-17",{"level":236,"checkedAt":237},"axa-switzerland-claims-fraud-detection",{"title":302,"useCases":303,"organization":304,"vendors":306,"summary":308,"stage":253,"year":309,"channels":310,"languages":311,"metrics":312,"outcomeDisclosed":199,"sources":313,"verification":318,"grade":267,"id":319,"organizationSlug":240},"Assurant: AI claims fraud detection for the special investigations unit",[192],{"name":305,"anonymized":199,"country":200,"region":201,"industry":17},"Assurant",[307],{"name":250,"role":251},"Assurant has used Shift Claims Fraud Detection since 2018 to flag suspicious claims to its special investigations unit, after a trial in which the system identified dozens of fraud cases. Alerts carry the context investigators need, the investigation software was adapted to the unit's workflow, and the models were refined for schemes involving specialty vehicles, extreme weather and multiple claims. Shift reports that the unit's case acceptance rate rose over four years, leading to more fraud mitigated, but gives no figures.",2022,[29],[212],[],[314],{"url":315,"title":316,"publisher":250,"date":317},"https://www.shift-technology.com/resources/case-studies/assurants-focus-on-innovation-increases-stopped-fraud","Assurant's Focus on Innovation Increases Stopped Fraud","2022-12-20",{"level":236,"checkedAt":237},"assurant-claims-fraud-detection",{"title":321,"useCases":322,"organization":324,"vendors":327,"summary":329,"stage":206,"year":330,"channels":331,"languages":332,"metrics":333,"outcomeDisclosed":199,"sources":334,"verification":339,"grade":267,"id":340,"organizationSlug":240},"General Insurance Association of Singapore: shared AI fraud analytics on travel and motor claims",[192,323],"travel-insurance-claims-and-assistance-agent",{"name":325,"anonymized":199,"country":326,"region":247,"industry":17},"General Insurance Association of Singapore","SG",[328],{"name":250,"role":251},"Member insurers of the General Insurance Association of Singapore analyse travel and motor claims together with Shift Technology's AI, which finds connections between people, providers and claims that look genuine when each insurer sees them alone. The data analytics initiative started in 2017 with 25 insurers; fraud alerts are issued to members and prompt joint investigations. The public page gives no outcome figures.",2017,[28],[212],[],[335],{"url":336,"title":337,"publisher":250,"date":338},"https://www.shift-technology.com/resources/case-studies/power-of-the-collective-singapore-insurers-unite-to-fight-fraud","Power of the collective: Singapore insurers unite to fight fraud","2022-09-23",{"level":236,"checkedAt":237},"gia-singapore-industry-fraud-analytics",0,[343,349],{"kpi":47,"label":344,"unit":292,"currency":293,"aggregate":199,"higherIsBetter":229,"n":345,"nUpTo":341,"median":291,"min":291,"max":291,"byClaimant":346,"vendorOnly":229,"points":347},"Fraud losses prevented",1,{"organization":341,"vendor":345,"regulator":341,"independent":341},[348],{"evidenceId":300,"organization":273,"value":291,"qualifier":285,"claimant":287,"grade":267,"pooled":229},{"kpi":51,"label":350,"unit":284,"aggregate":199,"higherIsBetter":229,"n":345,"nUpTo":341,"median":283,"min":283,"max":283,"byClaimant":351,"vendorOnly":229,"points":352},"Interactions handled",{"organization":341,"vendor":345,"regulator":341,"independent":341},[353],{"evidenceId":300,"organization":273,"value":283,"qualifier":285,"claimant":287,"grade":267,"pooled":229},{"low":283,"high":355},6000000,[357,380,399,410,423],{"slug":183,"title":358,"shortTitle":359,"definition":360,"status":9,"industries":361,"functions":362,"patterns":364,"audience":30,"autonomy":367,"adoptionStage":368,"segment":19,"evidenceCount":369,"publicEvidenceCount":370,"organizations":371,"bestGrade":238,"headline":377,"lastVerified":237,"indexable":229},"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],[19,363],"operations",[26,23,24,365,366],"agentic-workflow","summarization","supervised-agent","early-adopters",9,7,[372,373,374,198,375,245,376],"Admiral Seguros","Allianz Partners","Hiscox","Sedgwick","Travelers",{"kpi":224,"label":378,"unit":217,"n":345,"nUpTo":345,"kind":379,"value":225,"qualifier":226,"claimant":220,"organization":198,"vendorReported":199},"Automation rate","reported",{"slug":184,"title":381,"shortTitle":382,"definition":383,"status":9,"industries":384,"functions":385,"patterns":387,"audience":390,"autonomy":367,"adoptionStage":368,"segment":19,"evidenceCount":391,"publicEvidenceCount":391,"organizations":392,"bestGrade":238,"headline":396,"lastVerified":188,"indexable":229},"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],[19,386],"customer-service",[388,389,365,24],"conversational-agent","voice-agent","customer-facing",5,[393,394,198,395,376],"DOMCURA","Hippo","Progressive",{"kpi":53,"label":397,"unit":217,"n":345,"nUpTo":341,"kind":379,"value":398,"qualifier":218,"claimant":287,"organization":393,"vendorReported":229},"Accuracy",90,{"slug":185,"title":400,"shortTitle":401,"definition":402,"status":9,"industries":403,"functions":404,"patterns":405,"audience":390,"autonomy":367,"adoptionStage":368,"segment":19,"evidenceCount":391,"publicEvidenceCount":391,"organizations":406,"bestGrade":267,"headline":240,"lastVerified":188,"indexable":229},"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],[25,23,365],[372,407,408,409,245],"Covéa","Foyer","PZU",{"slug":186,"title":411,"shortTitle":412,"definition":413,"status":9,"industries":414,"functions":415,"patterns":417,"audience":30,"autonomy":31,"adoptionStage":368,"segment":19,"evidenceCount":418,"publicEvidenceCount":419,"organizations":420,"bestGrade":238,"headline":240,"lastVerified":188,"indexable":229},"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.",[17],[19,416],"collections-and-recovery",[26,23,24,366],4,2,[421,422],"Central Insurance","Elephant Insurance",{"slug":187,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":433,"patterns":436,"audience":30,"autonomy":367,"adoptionStage":368,"segment":437,"evidenceCount":438,"publicEvidenceCount":438,"organizations":439,"bestGrade":238,"headline":446,"lastVerified":237,"indexable":229},"AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[428,429,430,431,432],"banking","payments","cross-industry","government","telecommunications",[20,434,435],"onboarding-and-kyc","lending-and-credit",[24,22,25,23],"front-office",6,[440,441,442,443,444,445],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":49,"label":447,"unit":448,"n":345,"nUpTo":341,"kind":379,"value":449,"qualifier":218,"claimant":220,"organization":443,"vendorReported":199},"Detection improvement","multiplier",2.5,{"indexable":229,"reasons":451},[],[453,458,463,470,476,482,488,494,501,508,515,521,528,535,541,546,553,559,565,571,577,583,589,594,599,605,612,617,622,629,635,641,648,653],{"id":145,"label":454,"issuer":160,"region":155,"url":455,"description":456,"useCases":457,"indexable":229},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":146,"label":459,"issuer":160,"region":155,"url":460,"description":461,"useCases":462,"indexable":229},"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":149,"label":464,"issuer":465,"region":466,"url":467,"description":468,"useCases":469,"indexable":229},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":150,"label":471,"issuer":472,"region":201,"url":473,"description":474,"useCases":475,"indexable":229},"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":477,"label":478,"issuer":160,"region":155,"url":479,"description":480,"useCases":481,"indexable":229},"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":147,"label":483,"issuer":484,"region":155,"url":485,"description":486,"useCases":487,"indexable":229},"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":148,"label":489,"issuer":490,"region":155,"url":491,"description":492,"useCases":493,"indexable":229},"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":495,"label":496,"issuer":497,"region":247,"url":498,"description":499,"useCases":500,"indexable":229},"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":502,"label":503,"issuer":504,"region":247,"url":505,"description":506,"useCases":507,"indexable":229},"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":509,"label":510,"issuer":511,"region":466,"url":512,"description":513,"useCases":514,"indexable":229},"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":516,"label":517,"issuer":518,"region":201,"url":519,"description":520,"useCases":514,"indexable":229},"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":522,"label":523,"issuer":524,"region":155,"url":525,"description":526,"useCases":527,"indexable":229},"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":529,"label":530,"issuer":531,"region":466,"url":532,"description":533,"useCases":534,"indexable":229},"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":536,"label":537,"issuer":160,"region":155,"url":538,"description":539,"useCases":540,"indexable":229},"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":542,"label":543,"issuer":160,"region":155,"url":544,"description":545,"useCases":540,"indexable":229},"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":547,"label":548,"issuer":549,"region":201,"url":550,"description":551,"useCases":552,"indexable":229},"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":554,"label":555,"issuer":160,"region":155,"url":556,"description":557,"useCases":558,"indexable":229},"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":560,"label":561,"issuer":562,"region":201,"url":563,"description":564,"useCases":558,"indexable":229},"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":566,"label":567,"issuer":568,"region":466,"url":569,"description":570,"useCases":558,"indexable":229},"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":572,"label":573,"issuer":160,"region":155,"url":574,"description":575,"useCases":576,"indexable":229},"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":578,"label":579,"issuer":580,"region":201,"url":581,"description":582,"useCases":576,"indexable":229},"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":584,"label":585,"issuer":497,"region":247,"url":586,"description":587,"useCases":588,"indexable":229},"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":590,"label":591,"issuer":160,"region":155,"url":592,"description":593,"useCases":588,"indexable":229},"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":595,"label":596,"issuer":160,"region":155,"url":597,"description":598,"useCases":588,"indexable":229},"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":600,"label":601,"issuer":602,"region":155,"url":603,"description":604,"useCases":369,"indexable":229},"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":606,"label":607,"issuer":608,"region":201,"url":609,"description":610,"useCases":611,"indexable":229},"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":613,"label":614,"issuer":160,"region":155,"url":615,"description":616,"useCases":611,"indexable":229},"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":618,"label":619,"issuer":160,"region":155,"url":620,"description":621,"useCases":438,"indexable":229},"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.",{"id":623,"label":624,"issuer":625,"region":626,"url":627,"description":628,"useCases":391,"indexable":229},"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":630,"label":631,"issuer":632,"region":155,"url":633,"description":634,"useCases":418,"indexable":229},"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":636,"label":637,"issuer":638,"region":155,"url":639,"description":640,"useCases":418,"indexable":229},"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":642,"label":643,"issuer":644,"region":247,"url":645,"description":646,"useCases":647,"indexable":229},"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":649,"label":650,"issuer":160,"region":155,"url":651,"description":652,"useCases":647,"indexable":229},"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":654,"label":655,"issuer":656,"region":201,"url":657,"description":658,"useCases":647,"indexable":229},"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.",1790598300185]