[{"data":1,"prerenderedAt":695},["ShallowReactive",2],{"uc-health-prior-authorization-and-claims-adjudication":3,"uc-regulations":491},{"useCase":4,"evidence":211,"blitsAiDeployments":370,"benchmarks":371,"indicative":395,"related":398,"indexability":489,"includeUnpublished":216},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":24,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"segment":21,"problem":36,"problemStats":37,"howItWorks":43,"valueDrivers":44,"kpis":50,"indicativeValue":57,"macroEstimates":85,"feasibility":86,"implementation":99,"risk":142,"blitsAi":188,"faq":190,"related":200,"datePublished":206,"dateModified":206,"lastVerified":206,"changelog":207,"slug":210},"AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI prior authorization and health claims review","AI checks health claims and prior authorization files against policy and clinical criteria; people make every denial. ICICI Lombard cut time per claim by over 50%.","published","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[12,13,14,15,16],"AI prior authorization","utilization management AI","health claims adjudication copilot","medical claims automation","appeals letter drafting for health plans",[18,19],"insurance","healthcare",[21,22,23],"claims","case-management","operations",[25,26,27,28,29],"document-processing","summarization","rag-knowledge-assistant","classification-and-routing","content-generation",[31,32],"internal-tools","api","employee-facing","copilot","early-adopters","Health insurers and the administrators that work for them review large volumes of paper heavy cases.\nAn inpatient health claim arrives with a discharge summary, lab reports and bills; at ICICI Lombard\nthat meant reading 20 or more pages per claim. A prior authorization request brings the clinical\nnotes that justify the planned treatment. In both cases an adjudicator or nurse must check the file\nagainst the policy terms and clinical criteria. Much of the time goes into reading and retyping, not\ninto the judgment the role exists for.\n\nThe stakes are high on both sides. Slow reviews delay care and frustrate providers, while loose\nreviews let waste, abuse and billing errors through. Automation also carries a clear public risk:\ninvestigations and lawsuits in the United States have accused insurers of using algorithms to deny\ncare with little human review. Any AI in this process must make reviews faster and more consistent\nwithout taking the clinical decision away from people.",[38],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"The US Centers for Medicare & Medicaid Services states that waste contributes to up to 25% of health care spending in the United States, and targets its WISeR prior authorization model at services with a history of waste, fraud and abuse.","WISeR (Wasteful and Inappropriate Service Reduction) Model","https://www.cms.gov/priorities/innovation/innovation-models/wiser",2025,"1. **Take in the request or claim.** Documents arrive through portals, electronic submissions,\n   email or scans; the AI classifies them and links them to the member and the case.\n2. **Extract and structure.** It extracts diagnosis, clinical presentation, history, treatment,\n   procedures, codes and billed amounts, with confidence levels.\n3. **Check against policy and criteria.** It compares the case with the member's cover and with the\n   published clinical guidelines the insurer uses, and lists what matches, what is missing and what\n   conflicts.\n4. **Prepare the file.** The reviewer receives a summary with the evidence behind each point and a\n   suggested outcome; clean, low risk cases that meet all criteria can be approved automatically.\n5. **Decide and communicate.** A clinician or adjudicator makes every denial or reduction, and the AI\n   drafts the plain language decision or appeal letter for approval.",[45,46,47,48,49],"speed","employee-productivity","cost-to-serve","compliance","customer-experience",[51,52,53,54,55,56],"handling-time-reduction","processing-time-reduction","automation-rate","accuracy","hours-saved","interactions-handled",{"referenceOrg":58,"inputs":59,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A health insurer that reviews 400,000 claims and authorization requests by hand each year",[60,66,73],{"key":61,"label":62,"low":63,"high":63,"unit":64,"note":65},"cases","Claims and authorization requests reviewed by a person per year",400000,"cases per year","The reference insurer.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"minutesSaved","Reviewer minutes saved per case",4,12,"minutes per case","Editorial assumption. The evidence on this page reports percentages, not minutes, for full case reviews (Microsoft reports that ICICI Lombard cut the time to process a single health claim by over 50%), and Acentra Health saved three minutes on the letter step alone (from six to three minutes per appeal letter). Replace with a time study.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerHour","Fully loaded cost per reviewer hour",40,80,"USD per hour","Editorial assumption; nurses and clinical reviewers cost more than claims processors. Replace with your own.","cases * minutesSaved / 60 * costPerHour","USD","per year","Reviewer time released","Review effort only. It leaves out faster decisions for members and providers, the effect of more consistent reviews on waste and appeals, the cost of clinical content and validation, and the cost of the platform.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"high","Extraction from medical documents is proven, but the process handles sensitive health data, works under strict decision deadlines and appeal rules, and needs clinical criteria in a form the system can apply. Clinical, legal and compliance teams must own the design, and every adverse decision stays with a licensed reviewer.",[90,91,92,93],"Policy wordings, benefit rules and the clinical criteria or guidelines the insurer applies","Historical cases with documents, decisions and appeal outcomes","Code sets and provider data for billing checks","Approved letter templates for approvals, denials and appeal outcomes",[95,96,97,98],"Claims adjudication and utilization management systems","Provider portals and electronic prior authorization interfaces","Document capture and storage for clinical records","Member and provider communication channels for decisions and letters",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Start with summaries for reviewers","Let the AI structure documents and write a case summary that the reviewer checks. It saves time immediately and changes no decision rights.",{"title":105,"detail":106},"Encode the criteria with clinicians","Work with medical directors to turn the clinical criteria and benefit rules into checks the system can apply, each linked to its source document and version.",{"title":108,"detail":109},"Automate approvals only","If automation goes further, let it approve clean cases that meet every criterion. Denials, reductions and partial approvals always go to a licensed reviewer. In the EU, an automatic approval is still a solely automated decision based on health data: GDPR Article 22(4) allows it only with the member's explicit consent or on grounds of substantial public interest in law, so check that basis before you switch it on.",{"title":111,"detail":112},"Draft decision letters in plain language","Use the AI to turn the reviewer's rationale into a clear letter for the member and provider, approved by the reviewer before it is sent.",{"title":114,"detail":115},"Monitor outcomes by group and by reviewer","Track approval, denial and overturn rates on appeal across member groups, conditions and reviewers, and investigate any pattern the AI may have introduced.",[117,118,119,120,121],"No denial, reduction or termination of care without a licensed clinician or adjudicator reviewing the case","Every recommendation cites the clinical criterion or policy clause it relies on","Automatic decisions limited to approvals of cases that meet every criterion","Health data processed under HIPAA or GDPR special category rules, masked in logs and prompts","Overturn rates on appeal monitored for AI supported decisions","Licensed clinicians and adjudicators make every adverse decision and approve every letter before it is sent. Medical directors own the criteria encoded in the system, and a quality team samples AI supported approvals and summaries every week.",[124,125,126,127,128],"Reviewer minutes per case, before and after","Time from request to decision, against regulatory deadlines","Share of cases approved automatically and share sent to review","Overturn rate on appeal for AI supported decisions","Agreement between AI summaries and reviewer findings on a weekly sample",[130,133,136,139],{"title":131,"detail":132},"Rubber stamp review","Reviewers approve the AI's suggested denial without reading the file. Measure review time per case, audit samples and never let the system propose a denial without the evidence attached.",{"title":134,"detail":135},"Criteria that drift from medicine","Encoded criteria fall behind updated guidelines. Give every criterion an owner, a version and a review date.",{"title":137,"detail":138},"Extraction errors in clinical detail","A missed comorbidity or wrong code changes the outcome. Show confidence per field and require the reviewer to confirm key facts.",{"title":140,"detail":141},"Unequal outcomes for vulnerable members","The system denies or delays care more often for elderly, disabled or chronically ill members, for example because their files are longer and less standard. Monitor outcomes by group and fix the cause.",{"euAiAct":143,"regulations":146,"guidance":153,"controls":173,"incidents":179},{"tier":144,"basis":145},"context-dependent","Annex III point 5(a) makes AI high risk when it is used by or on behalf of public authorities to evaluate eligibility for essential public assistance benefits and services, including healthcare services, or to grant, reduce or revoke them, which can cover statutory health schemes run by or for public bodies. Point 5(c) covers risk assessment and pricing in life and health insurance, not claim review. A copilot for a private insurer's claim review, where people decide, is usually outside Annex III; for public schemes, Article 6(3) may exempt a system that only performs a preparatory task, unless it profiles natural persons. GDPR rules on health data (Article 9) and on solely automated decisions (Article 22) apply in every case.",[147,148,149,150,151,152],"eu-ai-act","gdpr","hipaa","solvency-ii","nist-ai-rmf","iso-42001",[154,160,162,168],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F)","Centers for Medicare & Medicaid Services","north-america","https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-and-prior-authorization-final-rule-cms-0057-f","Requires impacted payers, such as Medicare Advantage organizations and state Medicaid programs, to send prior authorization decisions within 72 hours for urgent and seven calendar days for standard requests, to give a specific reason for denials from 2026, and to offer a Prior Authorization API.",{"title":40,"issuer":156,"region":157,"url":41,"note":161},"Shows the US government's design for AI assisted prior authorization, in which every recommendation for non payment is decided by a licensed clinician.",{"title":163,"issuer":164,"region":165,"url":166,"note":167},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(a) on public assistance benefits and services, including healthcare services, and point 5(c) on life and health insurance determine when this use is high risk in the EU.",{"title":169,"issuer":170,"region":165,"url":171,"note":172},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Addressed to national supervisors, it clarifies how existing insurance legislation applies to the governance and risk management of AI systems used by insurers, following a risk based and proportionate approach.",[174,175,176,177,178],"Documented decision rights, where AI may recommend and approve within criteria and only people deny or reduce","Versioned clinical criteria and benefit rules with clinical owners","Audit trail of documents, extracted facts, criteria applied, reviewer and decision per case","Outcome and appeal overturn monitoring by member group and condition","Privacy and security controls for health data, including access logging",[180,184],{"title":181,"url":182,"note":183},"How Cigna Saves Millions by Having Its Doctors Reject Claims Without Reading Them","https://www.propublica.org/article/cigna-pxdx-medical-health-insurance-rejection-claims","ProPublica reported that Cigna doctors signed off payment denials flagged by its PXDX review process in batches, spending an average of 1.2 seconds per case according to company documents. Cigna said it was incorrect that the process lets its doctors reject claims without examining them. A warning about human review that becomes a formality.",{"title":185,"url":186,"note":187},"UnitedHealth sued over use of algorithm in Medicare Advantage plans","https://www.statnews.com/2023/11/14/unitedhealth-class-action-lawsuit-algorithm-medicare-advantage/","A class action alleged that UnitedHealth and its subsidiary NaviHealth used an algorithm, nH Predict, to deny rehabilitation care to seriously ill Medicare Advantage patients, and claimed a 90% error rate based on the share of denials reversed on appeal. UnitedHealth said the tool is not used to make coverage determinations.",{"howToBuild":189},"On Blits.ai this is an **agentic workflow** triggered through the API for each request, claim or\nappeal. The case documents are passed to the workflow, an **AI agent** extracts the clinical and\nbilling facts with **structured output**, and **hybrid retrieval** over a **knowledge base** that\nholds the policy wordings and clinical criteria provides the citations for each check. **Custom\nfunctions** read member and benefit data from the adjudication system and write back the summary.\n\n**Human in the loop approval** keeps every adverse decision with a licensed reviewer, and the agent\ndrafts the decision or appeal letter for the reviewer to approve. **PII masking** keeps health data\nout of model prompts where possible, the **audit trail** records every run, and **test suites**\ngrade summaries against reviewed cases. EU and UAE data residency keeps health data in region.",[191,194,197],{"question":192,"answer":193},"Can AI deny prior authorization requests or claims?","It should not. The US government's own WISeR model uses AI to assist prior authorization reviews, but every recommendation for non payment is decided by an appropriately licensed clinician. The safe design lets AI summarise, check criteria and approve clean cases, and leaves every denial or reduction to a person.",{"question":195,"answer":196},"What time savings do health insurers report?","Microsoft reports that ICICI Lombard's claims copilot cut the time to process a single health claim by over 50%, and that Acentra Health halved nurse time per Medicare appeal letter, saving 11,000 nursing hours. Sprout.ai reports that AdvanceCare settles some routine claims in 60 seconds.",{"question":198,"answer":199},"What went wrong in the publicised cases of algorithmic denials?","ProPublica reported that Cigna doctors signed off denials in batches, at an average of 1.2 seconds per case, and a class action alleged that UnitedHealth used an algorithm to deny rehabilitation care and that most appealed denials were reversed. Both companies disputed these accounts. The lesson is to measure real human review (time per case, overturn rates on appeal) and to keep denials out of automation.",[201,202,203,204,205],"claims-triage-and-straight-through-processing","life-underwriting-medical-record-summarization","outbound-notice-drafting","correspondence-triage-and-routing","medical-coding-automation","2026-09-27",[208],{"date":206,"note":209},"First published","health-prior-authorization-and-claims-adjudication",[212,248,268,310,335],{"title":213,"useCases":214,"organization":215,"vendors":219,"summary":233,"stage":234,"year":235,"channels":236,"languages":237,"metrics":239,"outcomeDisclosed":216,"sources":240,"verification":242,"grade":245,"id":246,"organizationSlug":247},"Centers for Medicare & Medicaid Services: WISeR model for technology assisted prior authorization",[210],{"name":156,"anonymized":216,"country":217,"region":157,"industry":218},false,"US","government",[220,223,225,227,229,231],{"name":221,"role":222},"Cohere Health","platform",{"name":224,"role":222},"Genzeon",{"name":226,"role":222},"Humata Health",{"name":228,"role":222},"Innovaccer",{"name":230,"role":222},"Virtix Health",{"name":232,"role":222},"Zyter","In the WISeR model, the US Centers for Medicare & Medicaid Services works with technology companies that use AI and machine learning, together with clinical review, to review prior authorization requests and claims before payment for a selected set of Original Medicare services with a history of waste, fraud and abuse. It runs from January 1, 2026 to December 31, 2031 in New Jersey, Ohio, Oklahoma, Texas, Arizona and Washington. Every recommendation for non payment is decided by an appropriately licensed clinician. Six technology companies take part, one per state, and are paid a share of the averted spending, adjusted for performance measures that include provider experience. The model page reports no results yet.","production",2026,[32],[238],"en",[],[241],{"url":41,"title":40,"publisher":156},{"level":243,"checkedAt":244},"source-verified","2026-09-26","B","cms-wiser-prior-authorization-model","centers-for-medicare-and-medicaid-services",{"title":249,"useCases":250,"organization":251,"vendors":254,"summary":255,"stage":234,"year":235,"channels":256,"languages":257,"metrics":258,"outcomeDisclosed":216,"sources":259,"verification":265,"grade":245,"id":266,"organizationSlug":267},"Manulife: AI document processing for health and dental claims",[210],{"name":252,"anonymized":216,"country":253,"region":157,"industry":18},"Manulife","CA",[],"In its first quarter 2026 report to shareholders, Manulife says it enhanced online claims processing for its Affinity health and dental business in Canada with AI driven document processing for the majority of claims that were processed manually, which improved processing speed and paid customers faster. No figures were published.",[32],[238],[],[260],{"url":261,"title":262,"publisher":263,"date":264},"https://www.sec.gov/Archives/edgar/data/1086888/000108688826000044/q12026reporttoshareholderl.htm","Manulife Financial Corporation first quarter 2026 report to shareholders (Form 6-K)","Manulife Financial Corporation (via SEC EDGAR)","2026-05-13",{"level":243,"checkedAt":244},"manulife-health-dental-claims-document-ai","manulife",{"title":269,"useCases":270,"organization":271,"vendors":273,"summary":276,"stage":234,"year":277,"channels":278,"languages":279,"metrics":280,"outcomeDisclosed":301,"sources":302,"verification":306,"grade":307,"id":308,"organizationSlug":309},"Acentra Health: MedScribe drafting of Medicare appeal determination letters",[210,203],{"name":272,"anonymized":216,"country":217,"region":157,"industry":19},"Acentra Health",[274],{"name":275,"role":222},"Microsoft","Acentra Health, which reviews Medicare appeals, built MedScribe on Azure OpenAI Service to turn a physician's clinical rationale into a plain language, empathetic appeal determination letter for the beneficiary and the provider, a task specially trained nurses used to do by hand in an appeals process where a decision can be due within 24 hours. It was tested with 10 nurses who rated every draft, then rolled out to all nurses who write these letters. Microsoft reports that time per letter fell by about 50%, from six to three minutes, saving 11,000 nursing hours and nearly USD 800,000 since deployment, and that nurses gave the generated letters a 99% approval rating.",2024,[31],[238],[281,289,295],{"kpi":51,"value":282,"unit":283,"qualifier":284,"period":285,"claimant":286,"quote":287,"sourceUrl":288},50,"percent","approximately","Nurse time per appeal determination letter","vendor","With MedScribe, Acentra Health reduced the time that its specially trained nursing staff spent on each appeal determination letter by approximately 50%.","https://www.microsoft.com/en/customers/story/19280-acentra-health-azure",{"kpi":55,"value":290,"unit":291,"qualifier":292,"period":293,"claimant":286,"quote":294,"sourceUrl":288},11000,"hours","exact","Nursing hours saved by mid 2024","By mid-2024, the company had saved 11,000 nursing hours and begun preparations to expand MedScribe to other areas to drive organizational efficiency.",{"kpi":296,"value":297,"unit":298,"currency":81,"qualifier":284,"period":299,"claimant":286,"quote":300,"sourceUrl":288},"cost-savings",800000,"currency","Saved since deployment","This adds up to 11,000 nursing hours and nearly $800,000 that have been saved since deploying MedScribe.",true,[303],{"url":288,"title":304,"publisher":305},"Acentra Health boosts employee productivity with generative AI and Azure OpenAI Service, saving 11,000 nursing hours and nearly $800,000","Microsoft Customer Stories",{"level":243,"checkedAt":206},"C","acentra-health-medscribe-appeal-letters",null,{"title":311,"useCases":312,"organization":313,"vendors":317,"summary":319,"stage":234,"year":277,"channels":320,"languages":321,"metrics":322,"outcomeDisclosed":301,"sources":328,"verification":333,"grade":307,"id":334,"organizationSlug":309},"ICICI Lombard: claims copilot for health claim adjudicators",[210],{"name":314,"anonymized":216,"country":315,"region":316,"industry":18},"ICICI Lombard","IN","asia-pacific",[318],{"name":275,"role":222},"ICICI Lombard built a claims copilot for its health claim adjudicators with Azure OCR, Azure AI Document Intelligence, Azure OpenAI and an in house model. It structures discharge summaries, lab reports and bills into diagnosis, clinical presentation, history, treatment and investigations, and compares the treatment with National Health Authority and disease treatment guidelines, so the adjudicator reads a summary instead of 20 or more pages. Microsoft reports that the time to process a single health claim fell by over 50%.",[31],[238],[323],{"kpi":51,"value":282,"unit":283,"qualifier":324,"period":325,"claimant":286,"quote":326,"sourceUrl":327},"at-least","Time for an adjudicator to process a single health claim","This solution has reduced the time for claims adjudicators to process a single health claim by over 50%.","https://www.microsoft.com/en-in/aifirstmovers/icici-lombard",[329],{"url":327,"title":330,"publisher":331,"archivedUrl":332},"ICICI Lombard: Increasing productivity using a claims copilot","Microsoft India","https://web.archive.org/web/20250317095025/https://www.microsoft.com/en-in/aifirstmovers/icici-lombard",{"level":243,"checkedAt":244},"icici-lombard-health-claims-copilot",{"title":336,"useCases":337,"organization":338,"vendors":341,"summary":344,"stage":345,"year":346,"channels":347,"languages":348,"metrics":350,"outcomeDisclosed":301,"sources":362,"verification":368,"grade":307,"id":369,"organizationSlug":309},"AdvanceCare: AI extraction and automation of health insurance claims",[210],{"name":339,"anonymized":216,"country":340,"region":165,"industry":18},"AdvanceCare","PT",[342],{"name":343,"role":222},"Sprout.ai","AdvanceCare, a health insurance group in Portugal that is part of the Generali Group and acts as a third party administrator for 1.7 million members, has used Sprout.ai's platform since January 2023 to extract, structure and validate data from hospital, dental, pharmacy and other healthcare invoices, map claim descriptions to codes with confidence levels, and automate settlement of routine claims. Sprout.ai reports that some routine claims are now settled in 60 seconds, that over a million claims went through the platform in the past year, that automation levels rose by more than 10%, and that a pilot on a sample of invoices before the partnership was 98% accurate.","scaled",2023,[32],[349],"pt",[351,357],{"kpi":56,"value":352,"unit":353,"qualifier":324,"period":354,"claimant":286,"quote":355,"sourceUrl":356},1000000,"count","Claims processed in the past year","The milestone comes after the health insurer TPA (Third-Party Administrator) processed over a million claims in the past year using Sprout.ai’s patented AI platform.","https://sprout.ai/resource/advancecare-slashes-claims-processing-times-to-60-seconds-using-sprout-ais-technology/",{"kpi":54,"value":358,"unit":283,"qualifier":292,"period":359,"claimant":286,"quote":360,"sourceUrl":361},98,"Pilot on a sample of healthcare invoices across categories, before the partnership","The results were 98% accurate across all categories.","https://sprout.ai/resource/case-study-advancecare-and-sprout-ai/",[363,366],{"url":356,"title":364,"publisher":343,"date":365},"AdvanceCare slashes claims processing times to 60 seconds using Sprout.ai’s technology","2025-07-16",{"url":361,"title":367,"publisher":343},"AdvanceCare & Sprout.ai collaborate on AI to achieve 60-second claim turnaround",{"level":243,"checkedAt":244},"advancecare-health-claims-automation",0,[372,379,385,390],{"kpi":51,"label":373,"unit":283,"aggregate":301,"higherIsBetter":301,"n":374,"nUpTo":370,"median":282,"min":282,"max":282,"byClaimant":375,"vendorOnly":301,"points":376},"Handling time reduction",2,{"organization":370,"vendor":374,"regulator":370,"independent":370},[377,378],{"evidenceId":308,"organization":272,"value":282,"qualifier":284,"claimant":286,"grade":307,"pooled":301},{"evidenceId":334,"organization":314,"value":282,"qualifier":324,"claimant":286,"grade":307,"pooled":301},{"kpi":54,"label":380,"unit":283,"aggregate":301,"higherIsBetter":301,"n":381,"nUpTo":370,"median":358,"min":358,"max":358,"byClaimant":382,"vendorOnly":301,"points":383},"Accuracy",1,{"organization":370,"vendor":381,"regulator":370,"independent":370},[384],{"evidenceId":369,"organization":339,"value":358,"qualifier":292,"claimant":286,"grade":307,"pooled":301},{"kpi":55,"label":386,"unit":291,"aggregate":216,"higherIsBetter":301,"n":381,"nUpTo":370,"median":290,"min":290,"max":290,"byClaimant":387,"vendorOnly":301,"points":388},"Hours saved",{"organization":370,"vendor":381,"regulator":370,"independent":370},[389],{"evidenceId":308,"organization":272,"value":290,"qualifier":292,"claimant":286,"grade":307,"pooled":301},{"kpi":56,"label":391,"unit":353,"aggregate":216,"higherIsBetter":301,"n":381,"nUpTo":370,"median":352,"min":352,"max":352,"byClaimant":392,"vendorOnly":301,"points":393},"Interactions handled",{"organization":370,"vendor":381,"regulator":370,"independent":370},[394],{"evidenceId":369,"organization":339,"value":352,"qualifier":324,"claimant":286,"grade":307,"pooled":301},{"low":396,"high":397},1066666.6666666667,6400000,[399,425,435,457,474],{"slug":201,"title":400,"shortTitle":401,"definition":402,"status":9,"industries":403,"functions":404,"patterns":405,"audience":408,"autonomy":409,"adoptionStage":35,"segment":21,"evidenceCount":410,"publicEvidenceCount":411,"organizations":412,"bestGrade":245,"headline":420,"lastVerified":244,"indexable":301},"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],[21,23],[28,406,25,407,26],"prediction-and-scoring","agentic-workflow","back-office","supervised-agent",9,7,[413,414,415,416,417,418,419],"Admiral Seguros","Allianz Partners","Hiscox","Lemonade","Sedgwick","Tokio Marine & Nichido Fire Insurance","Travelers",{"kpi":53,"label":421,"unit":283,"n":381,"nUpTo":381,"kind":422,"value":423,"qualifier":284,"claimant":424,"organization":416,"vendorReported":216},"Automation rate","reported",55,"organization",{"slug":202,"title":426,"shortTitle":427,"definition":428,"status":9,"industries":429,"functions":430,"patterns":432,"audience":33,"autonomy":34,"adoptionStage":35,"segment":431,"evidenceCount":374,"publicEvidenceCount":374,"organizations":433,"bestGrade":245,"headline":309,"lastVerified":206,"indexable":301},"AI summarization of medical evidence for life and health underwriting","Life underwriting medical summaries","AI that reads the medical evidence behind a life or health insurance application (attending physician statements, electronic health records, lab results and disclosures), turns it into a structured, cited summary of conditions, treatments and dates, and maps it to the insurer's underwriting manual so an underwriter can decide faster and more consistently.",[18],[431],"underwriting",[25,26,27],[252,434],"Prudential plc",{"slug":203,"title":436,"shortTitle":437,"definition":438,"status":9,"industries":439,"functions":443,"patterns":447,"audience":33,"autonomy":34,"adoptionStage":35,"segment":408,"evidenceCount":449,"publicEvidenceCount":449,"organizations":450,"bestGrade":245,"headline":453,"lastVerified":244,"indexable":301},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[440,441,18,218,19,442],"cross-industry","banking","wealth-and-asset-management",[23,444,445,446,21],"customer-service","collections-and-recovery","regulatory-compliance",[29,27,448],"translation",5,[272,415,451,452],"Health Resources and Services Administration","SS&C Technologies",{"kpi":52,"label":454,"unit":283,"n":381,"nUpTo":370,"kind":422,"value":455,"qualifier":292,"claimant":286,"organization":456,"vendorReported":301},"Cycle time reduction",25,"SS&C GIDS and RS",{"slug":204,"title":458,"shortTitle":459,"definition":460,"status":9,"industries":461,"functions":462,"patterns":463,"audience":408,"autonomy":409,"adoptionStage":464,"segment":408,"evidenceCount":465,"publicEvidenceCount":465,"organizations":466,"bestGrade":245,"headline":472,"lastVerified":206,"indexable":301},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[440,441,18,218],[23,444,22],[28,25,26],"mainstream",6,[467,468,469,470,419,471],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","U.S. Department of Veterans Affairs",{"kpi":54,"label":380,"unit":283,"n":381,"nUpTo":370,"kind":422,"value":473,"qualifier":292,"claimant":286,"organization":419,"vendorReported":301},91,{"slug":205,"title":475,"shortTitle":476,"definition":477,"status":9,"industries":478,"functions":479,"patterns":481,"audience":408,"autonomy":409,"adoptionStage":35,"evidenceCount":482,"publicEvidenceCount":482,"organizations":483,"bestGrade":245,"headline":487,"lastVerified":206,"indexable":301},"AI medical coding for clinical encounters","Medical coding automation","AI that reads the clinical documentation of an encounter and assigns the diagnosis and procedure codes (such as ICD-10, CPT and HCPCS) needed for billing and reporting, either as suggestions for a certified coder or autonomously for encounters it can code with high confidence, sending the rest to coders with the reasons.",[19],[480,23],"finance-and-accounting",[28,25],3,[484,485,486],"Mass General Brigham","US Department of Veterans Affairs, Veterans Health Administration","Your Health",{"kpi":54,"label":380,"unit":283,"n":381,"nUpTo":370,"kind":422,"value":488,"qualifier":292,"claimant":424,"organization":486,"vendorReported":216},98.3,{"indexable":301,"reasons":490},[],[492,497,502,509,515,521,528,535,542,548,555,561,568,575,581,586,593,598,603,609,615,621,627,632,637,643,650,654,659,666,672,678,684,689],{"id":147,"label":493,"issuer":164,"region":165,"url":494,"description":495,"useCases":496,"indexable":301},"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":148,"label":498,"issuer":164,"region":165,"url":499,"description":500,"useCases":501,"indexable":301},"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":152,"label":503,"issuer":504,"region":505,"url":506,"description":507,"useCases":508,"indexable":301},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":151,"label":510,"issuer":511,"region":157,"url":512,"description":513,"useCases":514,"indexable":301},"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":516,"label":517,"issuer":164,"region":165,"url":518,"description":519,"useCases":520,"indexable":301},"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":522,"label":523,"issuer":524,"region":165,"url":525,"description":526,"useCases":527,"indexable":301},"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":529,"label":530,"issuer":531,"region":165,"url":532,"description":533,"useCases":534,"indexable":301},"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":536,"label":537,"issuer":538,"region":316,"url":539,"description":540,"useCases":541,"indexable":301},"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":543,"label":544,"issuer":545,"region":316,"url":546,"description":547,"useCases":455,"indexable":301},"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.",{"id":549,"label":550,"issuer":551,"region":505,"url":552,"description":553,"useCases":554,"indexable":301},"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":556,"label":557,"issuer":558,"region":157,"url":559,"description":560,"useCases":554,"indexable":301},"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":562,"label":563,"issuer":564,"region":165,"url":565,"description":566,"useCases":567,"indexable":301},"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":569,"label":570,"issuer":571,"region":505,"url":572,"description":573,"useCases":574,"indexable":301},"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":576,"label":577,"issuer":164,"region":165,"url":578,"description":579,"useCases":580,"indexable":301},"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":582,"label":583,"issuer":164,"region":165,"url":584,"description":585,"useCases":580,"indexable":301},"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":587,"label":588,"issuer":589,"region":157,"url":590,"description":591,"useCases":592,"indexable":301},"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":594,"label":595,"issuer":164,"region":165,"url":596,"description":597,"useCases":70,"indexable":301},"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.",{"id":149,"label":599,"issuer":600,"region":157,"url":601,"description":602,"useCases":70,"indexable":301},"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":604,"label":605,"issuer":606,"region":505,"url":607,"description":608,"useCases":70,"indexable":301},"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":610,"label":611,"issuer":164,"region":165,"url":612,"description":613,"useCases":614,"indexable":301},"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":616,"label":617,"issuer":618,"region":157,"url":619,"description":620,"useCases":614,"indexable":301},"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":622,"label":623,"issuer":538,"region":316,"url":624,"description":625,"useCases":626,"indexable":301},"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":628,"label":629,"issuer":164,"region":165,"url":630,"description":631,"useCases":626,"indexable":301},"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":633,"label":634,"issuer":164,"region":165,"url":635,"description":636,"useCases":626,"indexable":301},"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":638,"label":639,"issuer":640,"region":165,"url":641,"description":642,"useCases":410,"indexable":301},"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":644,"label":645,"issuer":646,"region":157,"url":647,"description":648,"useCases":649,"indexable":301},"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":150,"label":651,"issuer":164,"region":165,"url":652,"description":653,"useCases":649,"indexable":301},"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":655,"label":656,"issuer":164,"region":165,"url":657,"description":658,"useCases":465,"indexable":301},"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":660,"label":661,"issuer":662,"region":663,"url":664,"description":665,"useCases":449,"indexable":301},"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":667,"label":668,"issuer":669,"region":165,"url":670,"description":671,"useCases":69,"indexable":301},"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":673,"label":674,"issuer":675,"region":165,"url":676,"description":677,"useCases":69,"indexable":301},"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":679,"label":680,"issuer":681,"region":316,"url":682,"description":683,"useCases":482,"indexable":301},"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":685,"label":686,"issuer":164,"region":165,"url":687,"description":688,"useCases":482,"indexable":301},"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":690,"label":691,"issuer":692,"region":157,"url":693,"description":694,"useCases":482,"indexable":301},"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.",1790598300092]