[{"data":1,"prerenderedAt":572},["ShallowReactive",2],{"uc-litigation-and-recovery-triage":3,"uc-regulations":350},{"useCase":4,"evidence":204,"blitsAiDeployments":255,"benchmarks":256,"indicative":257,"related":260,"indexability":348,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"problem":34,"problemStats":35,"howItWorks":46,"valueDrivers":47,"kpis":51,"indicativeValue":56,"macroEstimates":98,"feasibility":102,"implementation":113,"risk":151,"blitsAi":179,"faq":181,"related":194,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI for litigation and recovery triage","Litigation and recovery triage","AI litigation risk triage for insurance claims","AI flags claims likely to end in litigation before costs escalate. Sedgwick screens claims early; QBE Australia adopted CLARA Litigation in 2018.","published","An AI system that scores open insurance claims and overdue accounts for the risk that they end up in litigation or formal legal recovery, and ranks the attorneys, law firms or recovery agencies likely to get the best result, so a claims handler, legal panel manager or collections specialist can act early instead of after the case has already escalated.",[12,13,14,15,16],"litigation risk scoring","attorney performance scoring","recovery litigation triage","legal escalation prediction","claims litigation triage",[18],"insurance",[20,21,22],"claims","collections-and-recovery","legal",[24,25,26,27],"prediction-and-scoring","classification-and-routing","summarization","agentic-workflow",[29,30],"internal-tools","api","employee-facing","assist","early-adopters","Litigation is a small share of workers' compensation claims, but it changes the economics of the\ncase completely. Sedgwick's Chief Claims Officer describes three drivers behind it: adversarial\nrelationships, where employees seek legal representation out of frustration or anger toward their\nemployer or the claims administrator; a claims process confusing enough that people turn to an\nattorney for guidance; and a belief that hiring an attorney is simply part of the process,\nespecially when a claim is denied.\n\nBy the time a claim has an attorney, or an overdue account is in a recovery agency's queue, the\norganization has usually lost the cheapest options: an early, well handled conversation, a fair\nsettlement offer, a workable payment plan. Sedgwick reports that litigated workers' compensation\nclaims cost over three and a half times more than claims that never escalate, and CLARA Analytics'\nresearch on casualty claims with attorney involvement found a claim duration 295% higher than\nunrepresented claims.\n\nCLARA Analytics describes QBE Australia's earlier claims triage as \"a rudimentary triage system\nbased on a single criterion.\" In this page's view, a single criterion, whether that is claim\nseverity or days past due, misses the mix of case, claimant and prior outcome signals that actually\npredict whether a case will escalate and how it is likely to resolve. Litigation and recovery\ntriage aims to replace that single signal with that fuller picture.",[36,41],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"Sedgwick reports that litigation is typically less than 4% of workers' compensation claims overall but about 14.4% for indemnity claims, and that litigated claims cost over three and a half times more than non litigated claims.","Technology's transformative role in workers' compensation litigation","https://www.sedgwick.com/blog/technologys-transformative-role-in-workers-compensation-litigation/",2025,{"statement":42,"sourceTitle":43,"sourceUrl":44,"year":45},"CLARA Analytics' own research found that casualty claims with attorney involvement have average indemnity costs 390% higher than unrepresented claims (77,807 US dollars versus 15,936 US dollars), and claim duration 295% higher.","CLARA Litigation: AI Powered Litigation Risk Prediction","https://claraanalytics.com/products/litigation/",2023,"1. **Score continuously, not once.** The model rescores open claims and overdue accounts as new\n   information arrives, such as a new medical bill, a missed payment or a demand letter, not only\n   at intake, because litigation and legal escalation risk changes over the life of the case.\n2. **Surface the drivers, not just a number.** The score comes with the specific signals behind it\n   (injury type, days past due, prior attorney involvement in similar cases, claimant or debtor\n   history) so a handler can act on the reason, not just the rank.\n3. **Score the counsel and agencies too.** For cases that are already litigated or in legal\n   recovery, a second model ranks the attorneys, firms or recovery agencies available by their\n   track record on cost and outcome for similar cases, so the choice of who handles it is evidence\n   based as well.\n4. **Recommend the next step.** The system proposes a specific action, such as engaging a named\n   attorney, making a settlement offer now, or referring an account to legal recovery, with the\n   reasoning attached, for a person to approve.\n5. **Feed outcomes back.** Every closed case, whether it litigated or not and however it resolved,\n   becomes a labelled example that keeps the score and the attorney or agency rankings current.",[48,49,50],"cost-to-serve","risk-reduction","speed",[52,53,54,55],"cost-reduction","escalation-rate-reduction","cycle-time-days","recovery-rate-uplift",{"referenceOrg":57,"inputs":58,"formula":93,"currency":94,"period":95,"resultLabel":96,"caveat":97},"A workers' compensation insurer or claims administrator handling 50,000 lost time claims a year",[59,65,72,80,87],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"claimsPerYear","Lost time claims per year",50000,"claims per year","The reference insurer or claims administrator.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71,"sourceUrl":39},"legalInvolvementShare","Share of lost time claims with attorney involvement",0.1,0.144,"fraction of claims","Sedgwick reports litigation at less than 4% of workers' compensation claims overall but about 14.4% for indemnity (lost time) claims. Litigation and attorney involvement are not the same thing, but no source splits them out for lost time claims, so this range uses the litigation rate as a proxy for attorney involvement, capped at Sedgwick's reported 14.4% rather than above it.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78,"sourceUrl":79},"reductionAchieved","Relative reduction in legal involvement from earlier triage",0.05,0.15,"fraction reduction in legal involvement","Gradient AI's study of over 200,000 lost time claims across more than 60 carriers found AI enabled claims management cut legal involvement by 15%; the low end is conservative against that figure.","https://www.gradientai.com/press_gradient-ai-study-reduces-workers-compensation-lost-time-claims-legal-engagement",{"key":81,"label":82,"low":83,"high":84,"unit":85,"note":86,"sourceUrl":44},"costPerLegalClaim","Average indemnity cost of a claim with attorney involvement",70000,80000,"USD per claim","CLARA Analytics reports average indemnity costs of 77,807 US dollars for casualty claims with attorney involvement, across workers' compensation, commercial auto and general liability lines combined; applied here to the workers' compensation lost time reference book as the closest available figure.",{"key":88,"label":89,"low":90,"high":91,"unit":85,"note":92,"sourceUrl":44},"costPerNonLegalClaim","Average indemnity cost of a claim without attorney involvement",14000,18000,"CLARA Analytics reports average indemnity costs of 15,936 US dollars for unrepresented casualty claims, across the same combined lines; applied here to the same workers' compensation lost time reference book.","claimsPerYear * legalInvolvementShare * reductionAchieved * (costPerLegalClaim - costPerNonLegalClaim)","USD","per year","Annual indemnity cost avoided by reducing unnecessary legal involvement","Gross indemnity cost avoided only. It leaves out the cost of running the triage system and the legal panel, the effect on claimant experience and settlement quality, and debt collection litigation triage, which follows the same logic on different unit economics and would need its own inputs.",[99],{"statement":100,"sourceTitle":101,"sourceUrl":79,"year":45},"Gradient AI's research across more than 200,000 lost time workers' compensation claims from more than 60 insurance carriers found that AI enabled claims management reduced legal involvement in lost time claims by 15%, an estimated 5% saving on lost time claim costs, or about 3.5 million US dollars a year for an insurer with an average 70 million US dollar lost time claims book.","Gradient AI Study: AI Reduces Legal Involvement in Workers' Compensation Lost Time Claims by 15%, Saving Insurers Millions",{"complexity":103,"complexityNote":104,"dataPrerequisites":105,"integrations":109},"medium","Scoring is the easy part. The hard part is joining claims or account data with legal bill review or outside counsel invoice data cleanly enough to trust an attorney or agency score, and building a workflow so handlers act on the flag before the case escalates rather than after.",[106,107,108],"Historical claims or account records labelled by whether the case litigated or went to legal recovery, at what cost and after how long","Legal bill review or outside counsel invoice data, mapped to case and attorney or firm","A definition, agreed with legal and compliance, of what counts as high litigation or recovery risk for this book",[110,111,112],"Claims or collections case management system","Legal bill review or electronic billing platform, for attorney or agency performance data","Document management system for demand letters, pleadings and settlement offers",{"steps":114,"guardrails":130,"humanInTheLoop":135,"kpisToInstrument":136,"failureModes":141},[115,118,121,124,127],{"title":116,"detail":117},"Baseline the current mix","Measure how many cases litigate or go to legal recovery today, at what cost and after how long, split by case type, before building anything.",{"title":119,"detail":120},"Build and validate the risk score","Train or configure the model on closed cases with a documented litigation or legal recovery outcome, and validate it on a holdout period before it touches live cases.",{"title":122,"detail":123},"Design the human decision, not just the score","Define who reviews a flagged case, what they can do (settle, engage a specific attorney, escalate, hold), and within what limits, before the score reaches anyone.",{"title":125,"detail":126},"Add attorney or agency performance scoring where it applies","Benchmark counsel and recovery agencies on cost and outcome for comparable cases, not only on speed, and require a minimum case count before a firm gets a score at all.",{"title":128,"detail":129},"Pilot on one line or portfolio, then expand","Measure the litigation or legal escalation rate, cost per case and cycle time before and after on a comparable population, then widen to other lines.",[131,132,133,134],"A person decides every case flagged as high litigation or recovery risk; the system never files a claim into litigation or writes off a debt on its own","Explainable score drivers shown to the handler, not a single black box number","Fair treatment and vulnerable customer review of any collections use, including unfair, deceptive or abusive practice checks","Legal privilege and confidentiality controls around any data that touches active litigation","Adjusters, claims handlers, legal panel managers and collections specialists make every decision: whether to settle, which attorney or agency to use, and whether to write off a debt. The system's job is to bring the relevant history and a score in front of them earlier than a manual review would.",[137,138,139,140],"Litigation or legal escalation rate, by case type","Average cost and cycle time of litigated versus non litigated cases","Recovery rate on accounts referred to legal action versus settled","Share of flagged cases where the handler followed the recommendation",[142,145,148],{"title":143,"detail":144},"A score without context","A number with no explanation gets ignored or overridden without a record. Show the driving factors and require a reason when a handler departs from the recommendation.",{"title":146,"detail":147},"Attorney or agency scores built on too few cases","A firm with five cases can look as reliable as one with five hundred. Show a confidence measure and require a minimum case count before scoring a firm at all.",{"title":149,"detail":150},"Vulnerable claimants or debtors pushed toward faster settlement","A model tuned purely for cost can recommend against people who would benefit from a fair hearing or hardship support. Test recommendations against vulnerability signals, not only cost.",{"euAiAct":152,"regulations":155,"guidance":161,"controls":173,"incidents":178},{"tier":153,"basis":154},"context-dependent","Litigation and recovery triage is not itself listed in Annex III. It becomes high risk when the same system evaluates a natural person's creditworthiness or credit score (Annex III point 5(b)), for example when a debt write off or pursue decision is based on such an assessment; scoring which attorney to instruct or whether an insurance claim is likely to litigate is outside that point on its own. Automated decisions with a legal or similarly significant effect on an individual are also subject to Article 22 of the GDPR.",[156,157,158,159,160],"eu-ai-act","gdpr","solvency-ii","us-sr-11-7","uk-consumer-duty",[162,168],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"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; a risk based and proportionate approach to AI governance, fairness, explainability and human oversight in insurance, including claims handling.",{"title":169,"issuer":170,"region":165,"url":171,"note":172},"FG21/1: guidance for firms on the fair treatment of vulnerable customers","Financial Conduct Authority","https://www.fca.org.uk/publication/finalised-guidance/fg21-1.pdf","Expectations for identifying and supporting vulnerable customers, relevant whenever a litigation or recovery recommendation touches an individual claimant or debtor.",[174,175,176,177],"Every litigation, settlement or legal recovery decision is made and recorded by a person, not the model","Attorney, firm and recovery agency scores are shown with a confidence measure and a minimum case count before they drive a recommendation","Fair treatment and vulnerable customer review of any recommendation that touches an individual debtor or claimant","Decision log linking the score, the recommendation and the human's final action, for every case",[],{"howToBuild":180},"On Blits.ai the triage step runs as an **agentic workflow**, triggered when a claim or a\ndelinquent account crosses the litigation or recovery risk threshold in the organization's own\nscoring system. The agent calls **custom functions** that pull the case history, prior attorney\nor agency outcomes and cost data from **SQL knowledge bases**, and follows the organization's own\nlitigation and settlement playbook from a **knowledge base** with hybrid retrieval, so the\nrecommendation cites the actual policy and precedent behind it rather than a bare score.\n\nThe agent returns a structured case file: the litigation or recovery risk driver, a ranked list\nof counsel or agencies with their track record, and a recommended next step, for an adjuster,\nlegal panel manager or collections specialist to approve through **human in the loop approval**.\nThe **tool execution policy** keeps the agent's calls inside the approved action set, **PII\nmasking** limits the personal data reaching the model, **test suites** replay closed cases with a\nknown outcome before every change, and **run history with a full audit trail per run** records\nevery recommendation and approval. The underlying risk score can stay in the organization's own\nclaims or collections system; the platform is model agnostic, with EU and UAE data residency for\nthe workflow itself.",[182,185,188,191],{"question":183,"answer":184},"How is litigation risk triage different from claims triage?","Claims triage sorts every new claim by complexity and fraud signals so it reaches the right handler. Litigation and recovery triage is a narrower, later step: scoring which open claims or overdue accounts are heading toward a lawyer or a court, and which attorney, firm or recovery agency is likely to get the best result, so a person can act before costs escalate.",{"question":186,"answer":187},"Does this replace legal judgment?","No. Sedgwick describes flagging claims with a high propensity for litigation so they receive tailored workflows and additional resources, and using outcome data to select the attorneys who consistently deliver the best results. CLARA Analytics describes its litigation risk scores for QBE Australia as alerting claims managers when promptly settling a case is likely to produce the best outcome. Neither source claims the AI decides; this page's own guardrails require a person to make every settlement, attorney or write off decision.",{"question":189,"answer":190},"What KPI shows this is working?","Track the litigation or legal escalation rate by case type, and the average cost and cycle time of litigated versus non litigated cases, before and after. Gradient AI, a vendor, studied more than 60 workers' compensation carriers and found AI enabled claims management cut legal involvement in lost time claims by 15%; treat that as one vendor's study of its own customers, not an independent benchmark or a guarantee for any one book.",{"question":192,"answer":193},"Does this apply to debt collections as well as insurance claims?","The same logic, scoring whether a case is worth escalating to legal action and who should handle it, applies to a lender or collections agency deciding whether to litigate or write off a delinquent account. The named evidence on this page is from insurance claims administration, so treat the collections application as emerging until named deployments confirm it.",[195,196,197,198],"claims-triage-and-straight-through-processing","subrogation-opportunity-detection","collections-and-hardship-agent","loan-restructuring-recommendations","2026-09-29",[201],{"date":199,"note":202},"First published","litigation-and-recovery-triage",[205,228],{"title":206,"useCases":207,"organization":208,"vendors":213,"summary":214,"stage":215,"year":40,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":210,"sources":220,"verification":223,"grade":225,"id":226,"organizationSlug":227},"Sedgwick: predictive modelling to prevent workers' compensation litigation",[203],{"name":209,"anonymized":210,"country":211,"region":212,"industry":18},"Sedgwick",false,"US","north-america",[],"Sedgwick's Chief Claims Officer, Max Koonce, described the claims administrator's use of predictive modelling to identify workers' compensation claims with a high propensity for litigation early in the process, so they receive tailored workflows and additional resources before legal escalation happens. Sedgwick has separately used outcome data, for about four years at the time of writing, to score and select defence attorneys by cost and case results, and adopted AI driven medical record summarisation for examiners and defence counsel around 12 to 15 months before the post. No litigation rate or cost figure specific to the AI tools is given.","production",[],[218],"en",[],[221],{"url":39,"title":38,"publisher":209,"date":222},"2025-09-30",{"level":224,"checkedAt":199},"source-verified","B","sedgwick-litigation-prevention-modeling",null,{"title":229,"useCases":230,"organization":231,"vendors":235,"summary":239,"stage":215,"year":240,"channels":241,"languages":242,"metrics":243,"outcomeDisclosed":210,"sources":244,"verification":252,"grade":253,"id":254,"organizationSlug":227},"QBE Australia: CLARA Litigation to reduce claims litigation cost",[203,195],{"name":232,"anonymized":210,"country":233,"region":234,"industry":18},"QBE Insurance Group","AU","asia-pacific",[236],{"name":237,"role":238},"CLARA Analytics","platform","QBE's Australian Pacific division first engaged CLARA Analytics in September 2017 for its workers' compensation claims, using CLARA's AI and machine learning products as an early warning system for frontline claims teams. In December 2018 QBE Australia expanded its adoption of CLARA's product suite across its Australian statutory claims, covering workers' compensation and CTP (auto liability) businesses, and as part of that expansion will deploy CLARA Litigation, a product CLARA describes as designed to avoid and reduce the overall cost of litigation. A related, undated CLARA blog post, describing the same QBE Australia Pacific claims team, attributes AI and machine learning powered processes to routing high risk claims to experienced adjusters, guiding claims managers toward medical providers likely to produce good outcomes, and scoring cases for overall litigation risk and attorney performance so claims managers know when promptly settling a case is likely to produce the best outcome.",2018,[],[218],[],[245,249],{"url":246,"title":247,"publisher":237,"date":248},"https://claraanalytics.com/news/clara-analytics-announces-expanded-relationship-with-qbe-insurance/","CLARA Analytics Announces Expanded Relationship with QBE Insurance","2018-12-12",{"url":250,"title":251,"publisher":237},"https://claraanalytics.com/blog/ai-enables-operational-excellence-for-casualty-insurers/","AI Enables Operational Excellence for Casualty",{"level":224,"checkedAt":199},"C","qbe-clara-litigation-cost-reduction",0,[],{"low":258,"high":259},14000000,66959999.999999985,[261,292,305,333],{"slug":195,"title":262,"shortTitle":263,"definition":264,"status":9,"industries":265,"functions":266,"patterns":268,"audience":270,"autonomy":271,"adoptionStage":33,"segment":20,"evidenceCount":272,"publicEvidenceCount":273,"organizations":274,"bestGrade":225,"headline":281,"lastVerified":290,"indexable":291},"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],[20,267],"operations",[25,24,269,27,26],"document-processing","back-office","supervised-agent",10,8,[275,276,277,278,232,209,279,280],"Admiral Seguros","Allianz Partners","Hiscox","Lemonade","Tokio Marine & Nichido Fire Insurance","Travelers",{"kpi":282,"label":283,"unit":284,"n":285,"nUpTo":285,"kind":286,"value":287,"qualifier":288,"claimant":289,"organization":278,"vendorReported":210},"automation-rate","Automation rate","percent",1,"reported",55,"approximately","organization","2026-09-26",true,{"slug":196,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":297,"patterns":298,"audience":270,"autonomy":32,"adoptionStage":33,"segment":20,"evidenceCount":299,"publicEvidenceCount":300,"organizations":301,"bestGrade":225,"headline":227,"lastVerified":304,"indexable":291},"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],[20,21],[25,24,269,26],4,2,[302,303],"Central Insurance","Elephant Insurance","2026-09-27",{"slug":197,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":317,"patterns":319,"audience":322,"autonomy":271,"adoptionStage":33,"segment":323,"evidenceCount":324,"publicEvidenceCount":300,"organizations":325,"bestGrade":253,"headline":328,"lastVerified":304,"indexable":291},"AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[310,311,312,313,314,315,316],"cross-industry","banking","payments","telecommunications","energy-and-utilities","automotive","professional-services",[21,318],"customer-service",[320,321,27,25],"voice-agent","conversational-agent","customer-facing","lending",3,[326,327],"Day Knight & Associates","SameDay Auto Finance",{"kpi":52,"label":329,"unit":284,"n":300,"nUpTo":255,"kind":286,"value":330,"qualifier":331,"claimant":332,"organization":327,"vendorReported":291},"Cost reduction",75,"exact","vendor",{"slug":198,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":338,"patterns":341,"audience":31,"autonomy":344,"adoptionStage":345,"segment":323,"evidenceCount":300,"publicEvidenceCount":285,"organizations":346,"bestGrade":225,"headline":227,"lastVerified":304,"indexable":291},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[311],[21,339,340],"lending-and-credit","risk-management",[27,342,269,343],"rag-knowledge-assistant","recommendation-and-personalization","copilot","emerging",[347],"Commonwealth Bank of Australia",{"indexable":291,"reasons":349},[],[351,357,362,370,377,384,390,395,402,409,415,422,428,434,441,448,454,461,467,473,479,486,491,497,502,507,512,517,524,530,538,544,550,556,561,566],{"id":156,"label":352,"issuer":353,"region":165,"url":354,"description":355,"useCases":356,"indexable":291},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":157,"label":358,"issuer":353,"region":165,"url":359,"description":360,"useCases":361,"indexable":291},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":363,"label":364,"issuer":365,"region":366,"url":367,"description":368,"useCases":369,"indexable":291},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":371,"label":372,"issuer":373,"region":212,"url":374,"description":375,"useCases":376,"indexable":291},"nist-ai-rmf","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.",92,{"id":378,"label":379,"issuer":380,"region":165,"url":381,"description":382,"useCases":383,"indexable":291},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":385,"label":386,"issuer":353,"region":165,"url":387,"description":388,"useCases":389,"indexable":291},"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":160,"label":391,"issuer":170,"region":165,"url":392,"description":393,"useCases":394,"indexable":291},"FCA Consumer Duty","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":396,"label":397,"issuer":398,"region":234,"url":399,"description":400,"useCases":401,"indexable":291},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":403,"label":404,"issuer":405,"region":234,"url":406,"description":407,"useCases":408,"indexable":291},"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":159,"label":410,"issuer":411,"region":212,"url":412,"description":413,"useCases":414,"indexable":291},"SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":416,"label":417,"issuer":418,"region":366,"url":419,"description":420,"useCases":421,"indexable":291},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":423,"label":424,"issuer":353,"region":165,"url":425,"description":426,"useCases":427,"indexable":291},"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.",17,{"id":429,"label":430,"issuer":431,"region":165,"url":432,"description":433,"useCases":427,"indexable":291},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":435,"label":436,"issuer":437,"region":212,"url":438,"description":439,"useCases":440,"indexable":291},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":442,"label":443,"issuer":444,"region":366,"url":445,"description":446,"useCases":447,"indexable":291},"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":449,"label":450,"issuer":353,"region":165,"url":451,"description":452,"useCases":453,"indexable":291},"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":455,"label":456,"issuer":457,"region":212,"url":458,"description":459,"useCases":460,"indexable":291},"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":462,"label":463,"issuer":464,"region":212,"url":465,"description":466,"useCases":460,"indexable":291},"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":468,"label":469,"issuer":353,"region":165,"url":470,"description":471,"useCases":472,"indexable":291},"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":474,"label":475,"issuer":476,"region":366,"url":477,"description":478,"useCases":472,"indexable":291},"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":480,"label":481,"issuer":482,"region":212,"url":483,"description":484,"useCases":485,"indexable":291},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":487,"label":488,"issuer":353,"region":165,"url":489,"description":490,"useCases":485,"indexable":291},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":492,"label":493,"issuer":494,"region":165,"url":495,"description":496,"useCases":272,"indexable":291},"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":498,"label":499,"issuer":398,"region":234,"url":500,"description":501,"useCases":272,"indexable":291},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":503,"label":504,"issuer":353,"region":165,"url":505,"description":506,"useCases":272,"indexable":291},"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":508,"label":509,"issuer":353,"region":165,"url":510,"description":511,"useCases":272,"indexable":291},"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":158,"label":513,"issuer":353,"region":165,"url":514,"description":515,"useCases":516,"indexable":291},"Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",9,{"id":518,"label":519,"issuer":520,"region":212,"url":521,"description":522,"useCases":523,"indexable":291},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":525,"label":526,"issuer":353,"region":165,"url":527,"description":528,"useCases":529,"indexable":291},"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":531,"label":532,"issuer":533,"region":534,"url":535,"description":536,"useCases":537,"indexable":291},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":539,"label":540,"issuer":541,"region":165,"url":542,"description":543,"useCases":299,"indexable":291},"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":545,"label":546,"issuer":547,"region":165,"url":548,"description":549,"useCases":299,"indexable":291},"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":551,"label":552,"issuer":553,"region":234,"url":554,"description":555,"useCases":324,"indexable":291},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":557,"label":558,"issuer":353,"region":165,"url":559,"description":560,"useCases":324,"indexable":291},"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":562,"label":563,"issuer":353,"region":165,"url":564,"description":565,"useCases":324,"indexable":291},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":567,"label":568,"issuer":569,"region":212,"url":570,"description":571,"useCases":324,"indexable":291},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683490897]