[{"data":1,"prerenderedAt":622},["ShallowReactive",2],{"uc-admissions-application-review-assistant":3,"uc-regulations":402},{"useCase":4,"evidence":217,"blitsAiDeployments":304,"benchmarks":305,"indicative":311,"related":314,"indexability":400,"includeUnpublished":223},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":43,"valueDrivers":44,"kpis":48,"indicativeValue":53,"macroEstimates":94,"feasibility":95,"implementation":108,"risk":154,"blitsAi":197,"faq":199,"related":209,"datePublished":212,"dateModified":212,"lastVerified":212,"changelog":213,"slug":216},"AI support for university admissions application review","Admissions application review","AI admissions application review for universities","Universities use AI to score essays and screen files. Virginia Tech pairs an AI reader with one human; the Zucker School of Medicine reports 88% test accuracy.","published","AI admissions application review is software that reads parts of a university application, such as essays or grades and activities, and gives staff a score or recommendation; people still decide admission. It speeds up the first read of a very large pool and applies one rubric to every file.",[12,13,14,15],"AI essay reader for admissions","AI application screening","AI admissions screener","machine learning admissions review",[17],"education",[19,20],"case-management","operations",[22,23,24],"prediction-and-scoring","document-processing","summarization",[26],"internal-tools","back-office","copilot","early-adopters","higher-education","Application volumes have outgrown the staff who read them. Virginia Tech received a record 57,622\nfirst year applications for fall 2025, 10.2 percent more than the year before and the fifth\nstraight year of record highs, and says it needs to keep pace while returning decisions sooner. The\nZucker School of Medicine at Hofstra\u002FNorthwell gets more than 5,000 applications a year for a class\nof about 100, and its authors note that the number of medical school applicants has grown by 86%\nover two decades.\n\nReading every file twice is slow, and human readers vary: the same rubric is applied with different\nweight by different people and, over a long season, by the same person. AI can apply one rubric to\nevery file at any hour, which is the reason the schools in the evidence give for adopting it.\n\nThe risks follow from the same design. A model trained on past human decisions can copy the bias in\nthem, as the Zucker paper warns, and admission to an educational institution is a listed high risk\nuse in the EU AI Act. Applicants also notice. The University of North Carolina at Chapel Hill\nfaced strong negative feedback after its student newspaper reported that it used AI to evaluate\nthe grammar and writing style of application essays. The schools that publish their design all\nkeep the admit or deny decision with people.",[33,38],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"Virginia Tech reports a record 57,622 first year applications for fall 2025, a 10.2 percent increase over the previous year and the fifth consecutive year of record highs.","FAQs: Updates to 2025-26 undergraduate admissions process","https:\u002F\u002Fnews.vt.edu\u002Farticles\u002F2025\u002F07\u002Fadmissions-updatesFAQ-2025.html",2025,{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"The authors from the Zucker School of Medicine at Hofstra\u002FNorthwell write that the number of medical school applicants has been increasing 86% over the last 2 decades.","Using artificial intelligence in medical school admissions screening to decrease inter- and intra-observer variability","https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC9936956\u002F",2023,"1. **Staff define the job and the rubric.** The admissions office decides which part of the file the\n   AI reads (the essays or the structured fields such as grades and activities) and\n   writes the scoring rubric with anchor examples.\n2. **A model is built and tested on past cycles.** Virginia Tech trained its reader on past\n   applicant essays and the scoring rubric. NYU Grossman School of Medicine trained on more than\n   14,000 applications from 2013 to 2017 with the human screening outcomes, and the Zucker School\n   used five consecutive years of applications.\n3. **The AI scores or sorts the file.** At Virginia Tech the AI replaces one of the two human\n   readers of each essay. At the Zucker School it recommends who to invite to interview, who does\n   not meet the baseline criteria and who needs further review, the largest group.\n4. **A disagreement rule brings in a person.** Under the old process at Virginia Tech, a gap of\n   more than four points on a 12 point scale between two human readers brought in a third reader.\n   Now a gap of more than two points between the human and the AI brings in a second human reader,\n   so a large difference always gets another human read.\n5. **People decide.** Staff review the files the AI marks \"yes\" or \"further review\", and final\n   admission decisions stay with admissions professionals. The Zucker model leaves out name, place\n   of birth and photo.\n6. **The team checks the model every cycle.** It compares AI and human scores, tests results across\n   applicant groups and decides whether to keep, retrain or switch off the model.",[45,46,47],"speed","employee-productivity","cost-to-serve",[49,50,51,52],"hours-saved","processing-time-reduction","accuracy","error-reduction",{"referenceOrg":54,"inputs":55,"formula":89,"currency":90,"period":91,"resultLabel":92,"caveat":93},"A public university with 40,000 first year applications",[56,61,68,75,82],{"key":57,"label":58,"low":59,"high":59,"unit":57,"note":60},"applications","First year applications per cycle",40000,"The reference university.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"essays","Essays per application that the AI reads",1,4,"essays per application","Editorial assumption. Virginia Tech's reader scores each of the application essays; a first deployment may start with one.",{"key":69,"label":70,"low":71,"high":65,"unit":72,"note":73,"sourceUrl":74},"minutesPerRead","Minutes for one human read of an essay",2,"minutes per read","The VPM news report says a human reader averages two minutes per essay at Virginia Tech. The high end is an editorial assumption for a longer essay.","https:\u002F\u002Fwww.vpm.org\u002Fnews\u002F2025-12-02\u002Fcollege-admissions-artificial-intelligence-virginia-tech-juan-espinoza",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"replacedShare","Share of second reads that the AI replaces",0.5,0.9,"fraction of second reads","Editorial assumption. A second human reader is still needed whenever the AI and the first reader differ by more than the set threshold, and no school in the evidence has published how often that happens.",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88},"readerCost","Cost of one reader hour",20,35,"USD per hour","Editorial assumption for a trained seasonal reader. Replace with your own fully loaded cost.","applications * essays * minutesPerRead \u002F 60 * replacedShare * readerCost","USD","per year","Reader time cost avoided on essay reading","A proxy that prices reader hours. It leaves out the cost of building and validating the model, the fairness testing, staff time to handle disagreements and applicant questions, and the cost of a failed or withdrawn deployment. Virginia Tech's vice provost said at launch that the university is saving at least 8,000 hours, based on the previous year's application pool; that is an estimate, not an audited figure and not capacity measured after a full cycle. The value of earlier decisions to applicants is not counted.",[],{"complexity":96,"complexityNote":97,"dataPrerequisites":98,"integrations":103},"high","The build needs several past cycles of applications with the human scores attached, a research team able to test agreement and fairness across applicant groups, and sign off from the admissions office, legal counsel and the registrar before the model touches a live file. Virginia Tech reports three years of development before its essay reader went live, and the NYU model ran in a prospective trial where it did not affect decisions.",[99,100,101,102],"Several past cycles of applications with the human scores or screening outcomes, kept in a form the model can learn from","The scoring rubric with anchor examples and a record of how readers were trained","Applicant group data held apart from the model, used only to test results across groups","A notice for applicants that says how AI is used in review",[104,105,106,107],"Application portal or admissions CRM where essays and application forms arrive","Reader workflow tool where human scores and notes are recorded","Identity and access management for admissions staff","Reporting for the fairness and agreement checks each cycle",{"steps":109,"guardrails":128,"humanInTheLoop":134,"kpisToInstrument":135,"failureModes":141},[110,113,116,119,122,125],{"title":111,"detail":112},"Pick the narrowest first job","Start with a second reader on essays, or a sort of structured data that staff review in full, not a decision on who is admitted. All three schools in the evidence keep the decision with staff.",{"title":114,"detail":115},"Replay past cycles before any live use","Run the model on last year's applications with the human scores in hand and measure agreement per rubric trait. The Zucker paper reports 88% accuracy on its held out test set; NYU ran a prospective trial in which the AI assessments did not affect any decision.",{"title":117,"detail":118},"Test results across applicant groups","Compare scores and error rates by group, and set a rule where the model underperforms. The Zucker authors found lower performance for Native American applicants and say a human should review all of those applications.",{"title":120,"detail":121},"Set the disagreement rule and the staffing for it","Decide how large a gap between the AI and the human score sends a file to a second human, and staff for that volume. Virginia Tech uses a gap of more than two points on a 12 point scale.",{"title":123,"detail":124},"Tell applicants and publish the design","Say on the admissions site what the AI reads, what it does not do and who decides. UNC faced a backlash after its student paper reported on AI essay analysis, and its website now states that every application is evaluated by trained human evaluators.",{"title":126,"detail":127},"Monitor every cycle and keep a switch off","Recheck agreement and group results each cycle, log every score, and keep a documented way to turn the model off and return to two human readers.",[129,130,131,132,133],"The AI never makes the admit or deny decision, and applicants can be told who did","Direct identifiers such as name, place of birth and photo are kept out of what the model sees","A second human read whenever the AI and the human score differ by more than the set threshold","A fairness and agreement report for each cycle before the model is used on live files","No inference of emotions, personality or health from essays or interviews","Admissions professionals own the rubric, the threshold and every decision. They read the files the AI marks \"yes\" or \"further review\", handle all disagreements, and review a sample of AI scores each week of the season for scores that look fluent but wrong. A named officer signs off the model before each cycle and can switch it off.",[136,137,138,139,140],"Agreement between AI and human scores per rubric trait, and per applicant group","Share of files sent to a second human reader, and the reasons","Reader hours spent per application, against the previous cycle","Days from application to decision","Applicant complaints and queries that mention AI",[142,145,148,151],{"title":143,"detail":144},"Past bias in the training data","A model that learns from past human decisions can repeat their bias. Test across groups, keep identifiers out and treat a model that merely matches old decisions as a risk, not a success.",{"title":146,"detail":147},"Scoring style instead of substance","Grammar and writing style can stand in for quality and disadvantage applicants who write in a second language. Check scores against content, use anchor essays and read disputed files by hand.",{"title":149,"detail":150},"Applicant backlash","Applicants and parents react badly to hidden AI use, as UNC found. Publish what the tool does and who decides.",{"title":152,"detail":153},"Drift between cycles","Prompts, applicant pools and the way applicants write change from year to year, including with AI help. Retest each cycle and retrain only with approval.",{"euAiAct":155,"regulations":157,"guidance":163,"controls":187,"incidents":193},{"tier":96,"basis":156},"Annex III point 3(a): AI systems intended to be used to determine access or admission or to assign natural persons to educational and vocational training institutions at all levels are high risk. Even where the AI only recommends or scores one part of the file, the system is intended to be used in the admission process. Scoring an applicant's essay is profiling, so the Article 6(3) exemption for narrow procedural or preparatory tasks does not apply. Providers then need risk management, data governance, logging and human oversight, and the university as deployer has the duties in Article 26.",[158,159,160,161,162],"eu-ai-act","gdpr","uk-gdpr","nist-ai-rmf","iso-42001",[164,170,174,178,182],{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Annex III, high risk AI systems (point 3, education and vocational training)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 3(a) lists AI used to determine access or admission or to assign people to educational institutions as high risk.",{"title":171,"issuer":166,"region":167,"url":172,"note":173},"Article 14, human oversight","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F14\u002F","High risk systems must be designed so that people can effectively oversee them while they are in use.",{"title":175,"issuer":166,"region":167,"url":176,"note":177},"Article 26, obligations of deployers of high risk AI systems","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F26\u002F","Sets out what a university that deploys a high risk system must do, including human oversight by trained staff and keeping logs.",{"title":179,"issuer":166,"region":167,"url":180,"note":181},"Article 22 GDPR, automated individual decision making, including profiling","https:\u002F\u002Fgdpr-info.eu\u002Fart-22-gdpr\u002F","Gives people the right not to be subject to a decision based solely on automated processing that significantly affects them, which an admission decision does.",{"title":183,"issuer":184,"region":167,"url":185,"note":186},"Guidance on AI and data protection","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002Fartificial-intelligence\u002Fguidance-on-ai-and-data-protection\u002F","The UK regulator's guidance on fairness, transparency and accountability when AI processes personal data; it is under review because of changes made by the Data (Use and Access) Act.",[188,189,190,191,192],"A conformity and fairness file for the model, with validation results from past cycles","Applicant notice and a route to ask for a human review of the file","Immutable log of every AI score, threshold and routing decision","Named accountable owner and a documented switch off procedure","A data protection impact assessment covering applicant data and any special categories",[194],{"title":195,"url":74,"note":196},"UNC Chapel Hill faces criticism over AI analysis of application essays","After the student newspaper, The Daily Tar Heel, reported in January that the university used AI to evaluate the grammar and writing style of application essays, applicants, parents and students gave strong negative feedback. The university's website says it uses AI programs to provide data points and that every application is evaluated by trained human evaluators.",{"howToBuild":198},"On Blits.ai an application reader is an **agentic workflow** that starts through an **API token**\nwhen an application is complete. An **AI agent** with **structured output** returns a score for\neach rubric trait, a rationale that quotes the passages it relied on and a confidence value,\nusing the rubric and the anchor essays held in a **knowledge base**. **PII masking** at the\ngateway removes names and other identifiers before the text reaches a model, and **custom\nfunctions** compare the AI score with the first reader's score and write the result back to the\nreader tool over REST.\n\nWhen the gap is larger than the threshold, the custom function flags the file for a second human\nreader in the reader tool, and a **human in the loop confirmation** with approve and reject\ncontrols lets staff accept or reject a score before it is written back. The workflow never\nwrites an admit or deny decision. **Test suites** evaluate the agent on example applications with\nrules for the expected scores after every prompt or model change, while the agreement report\nagainst past human scores is built from the logged scores. **Run history** and analytics give the\naudit trail, and the platform is model agnostic with EU data residency, so the admissions office\ncan change the model without rebuilding the workflow.",[200,203,206],{"question":201,"answer":202},"Does AI decide who is admitted?","Not at the schools in the evidence. Virginia Tech states that its AI only confirms the score of a human reviewer, that it does not make decisions and that final decisions are made exclusively by admissions professionals. At the Zucker School of Medicine and NYU Grossman School of Medicine the AI screens or recommends and staff review the files.",{"question":204,"answer":205},"How accurate are AI admissions screeners?","The Zucker School of Medicine at Hofstra\u002FNorthwell has published figures in JAMIA Open. Its model \"had an accuracy of 95% on the training set, 88% on the validation set, and 88% on the test set\", measured against who the human process invited to interview. In a simulated real world trial on the 2020 to 2021 pool of 6,092 applicants, the paper says \"When the results of the algorithm and human evaluations were combined, the entire process had an accuracy of 96%.\" These figures show how closely the model and the combined process match past decisions, which saves time but does not show the decisions were fair. NYU Grossman School of Medicine has published a validation study of its own model that this page has not reviewed, and Virginia Tech has not published agreement or fairness figures that we found.",{"question":207,"answer":208},"Is AI in admissions high risk under the EU AI Act?","Yes. Annex III point 3(a) lists AI intended to determine access or admission to educational institutions as high risk, so the system needs risk management, human oversight and logging, and the university needs a data protection assessment. The university has the deployer duties in Article 26, and it is also the provider when it builds the system in house, as all three schools in the evidence did.",[210,211],"automated-scoring-of-written-responses","student-enrollment-and-services-assistant","2026-10-13",[214],{"date":212,"note":215},"First published","admissions-application-review-assistant",[218,252,287],{"title":219,"useCases":220,"organization":221,"vendors":227,"summary":230,"stage":231,"year":37,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":223,"sources":236,"verification":247,"grade":249,"id":250,"organizationSlug":251},"Virginia Tech: AI essay reader as the second scorer of undergraduate application essays",[216],{"name":222,"anonymized":223,"country":224,"region":225,"industry":17,"wikidata":226},"Virginia Tech",false,"US","north-america","Q65379",[228],{"name":222,"role":229},"in-house","For the 2025 to 2026 undergraduate cycle Virginia Tech replaced one of the two human readers of each application essay with an AI model built by its own researchers, after a record 57,622 first year applications for fall 2025. The model uses a large language model that the university says was trained, tested for accuracy and fairness and validated by its researchers. Each essay is scored by one admissions staff member and the AI reviewer. Under the old process, a gap of more than four points on a 12 point scale between two human readers brought in a third reader; now a gap of more than two points between the human and the AI brings in a second human reader. The university states that the AI only confirms the human score, that final admission decisions are made exclusively by admissions professionals, and that the aim is earlier decisions without raising application fees. In a news report published by VPM in December 2025, in the first cycle of use, vice provost Juan Espinoza said that based on last year's application pool the university is saving at least 8,000 hours. That is an estimate made at launch from the previous year's volume, not capacity measured after a cycle, so it is not recorded as a metric.","production",[26],[234],"en",[],[237,242,243],{"url":238,"title":239,"publisher":240,"date":241},"https:\u002F\u002Fnews.vt.edu\u002Farticles\u002F2025\u002F07\u002Fadmissions-changes-2025.html","Virginia Tech updates undergraduate admissions process","Virginia Tech News","2025-07-24",{"url":36,"title":35,"publisher":240,"date":241},{"url":74,"title":244,"publisher":245,"date":246},"Welcome to the new era of college admissions: AI may be scoring your essay","VPM","2025-12-02",{"level":248,"checkedAt":212},"source-verified","B","virginia-tech-ai-essay-reader-admissions",null,{"title":253,"useCases":254,"organization":255,"vendors":258,"summary":260,"stage":231,"year":42,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":273,"sources":274,"verification":285,"grade":249,"id":286,"organizationSlug":251},"Zucker School of Medicine at Hofstra\u002FNorthwell: AI initial screening of medical school applications",[216],{"name":256,"anonymized":223,"country":224,"region":225,"industry":17,"wikidata":257},"Donald and Barbara Zucker School of Medicine at Hofstra\u002FNorthwell","Q20857983",[259],{"name":256,"role":229},"The Zucker School of Medicine receives more than 5,000 applications a year for a class of about 100 students. It uses an AI system as the first reader of every application: the system recommends who should be invited to interview, who does not meet the baseline criteria, and a middle group (the largest) for further review, and human screeners on the admissions committee then review the \"yes\" and \"further review\" applications to decide which roughly 800 applicants get interview offers. The associate dean for admissions told AAMC News that the school took the pool of 5,000 applications down to 1,500 to 2,000 for committee review. The model was trained on five years of applications and, per the school's JAMIA Open paper, reached 88% accuracy on its held out test set. It works on the structured parts of the application, withholds name, place of birth and photo, and the paper warns that lower performance for Native American applicants means a human should review all of them.",[26],[234],[264],{"kpi":51,"value":265,"unit":266,"qualifier":267,"period":268,"baseline":269,"claimant":270,"quote":271,"sourceUrl":272},88,"percent","exact","offline result on the held out test set of historical applications, before live use","Whether the applicant was invited to interview by the human process","organization","The algorithm had an accuracy of 95% on the training set, 88% on the validation set, and 88% on the test set.","https:\u002F\u002Fwww.ebi.ac.uk\u002Feuropepmc\u002Fwebservices\u002Frest\u002FPMC9936956\u002FfullTextXML",true,[275,277,280],{"url":41,"title":40,"publisher":276},"JAMIA Open (authors at Northwell Health and the Zucker School of Medicine)",{"url":272,"title":278,"publisher":279},"Using artificial intelligence in medical school admissions screening to decrease inter- and intra-observer variability (Europe PMC full text)","JAMIA Open, via Europe PMC",{"url":281,"title":282,"publisher":283,"date":284},"https:\u002F\u002Fwww.aamc.org\u002Fnews\u002Fai-will-now-read-your-medical-school-application","AI will now read your medical school application","AAMCNews, Association of American Medical Colleges","2025-01-21",{"level":248,"checkedAt":212},"zucker-school-of-medicine-ai-application-screening",{"title":288,"useCases":289,"organization":290,"vendors":293,"summary":295,"stage":231,"year":37,"channels":296,"languages":297,"metrics":298,"outcomeDisclosed":273,"sources":299,"verification":301,"grade":302,"id":303,"organizationSlug":251},"NYU Grossman School of Medicine: machine learning screening of medical school applications",[216],{"name":291,"anonymized":223,"country":224,"region":225,"industry":17,"wikidata":292},"NYU Grossman School of Medicine","Q3100195",[294],{"name":291,"role":229},"NYU Grossman School of Medicine built a machine learning model to screen medical school applications and now uses it for initial screening, according to AAMC News. The model was trained on a sample of more than 14,000 applications from 2013 to 2017, together with the outcomes of the human screening for those applications, so it could learn which elements most often led to an interview invitation. A prospective trial in a later admission cycle compared its output with the human reviews while the AI assessments did not affect any decision. AAMC reports that the pilot showed the AI recommendations repeatedly replicated those of the human reviewers, after which the school moved to AI initial screening. The model assesses the structured parts of the application, and AAMC quotes a study of the NYU program estimating that manual screening \"involves more than 6,000 hours of faculty time yearly\".",[26],[234],[],[300],{"url":281,"title":282,"publisher":283,"date":284},{"level":248,"checkedAt":212},"C","nyu-grossman-school-of-medicine-ai-application-screening",0,[306],{"kpi":51,"label":307,"unit":266,"aggregate":273,"higherIsBetter":273,"n":64,"nUpTo":304,"median":265,"min":265,"max":265,"byClaimant":308,"vendorOnly":223,"points":309},"Accuracy",{"organization":64,"vendor":304,"regulator":304,"independent":304},[310],{"evidenceId":286,"organization":256,"value":265,"qualifier":267,"claimant":270,"grade":249,"pooled":273},{"low":312,"high":313},13333.333333333332,336000,[315,332,350,377],{"slug":210,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":321,"patterns":322,"audience":27,"autonomy":324,"adoptionStage":325,"evidenceCount":326,"publicEvidenceCount":326,"organizations":327,"bestGrade":249,"headline":251,"lastVerified":331,"indexable":273},"AI scoring of essays and written answers in assessments","Essay and written answer scoring","AI scoring of written responses is AI that scores students' essays and short written answers against a rubric for a testing programme. It is trained on responses scored by human raters, with human raters rescoring a sample of responses and every response the engine is unsure about; in hybrid programmes such as Texas, a human score is the score of record whenever a human scores a response.",[17,320],"government",[20],[323,22],"classification-and-routing","supervised-agent","mainstream",3,[328,329,330],"ETS","Massachusetts Department of Elementary and Secondary Education","Texas Education Agency","2026-09-27",{"slug":211,"title":333,"shortTitle":334,"definition":335,"status":9,"industries":336,"functions":337,"patterns":339,"audience":342,"autonomy":324,"adoptionStage":29,"evidenceCount":343,"publicEvidenceCount":343,"organizations":344,"bestGrade":249,"headline":251,"lastVerified":331,"indexable":273},"AI assistant for student enrollment and student services","Student enrollment assistant","A student enrollment and services assistant is an AI assistant that answers admitted and current students' questions about admissions, financial aid, registration, housing and deadlines. It answers them by text message and web chat, sends timely reminders for the tasks each student still has to complete, and hands personal or complex cases to staff.",[17],[338,20],"customer-service",[340,341,323],"conversational-agent","rag-knowledge-assistant","customer-facing",5,[345,346,347,348,349],"Adelphi University","Austin Peay State University","Georgia State University","University of Maryland Eastern Shore","University of West Florida",{"slug":351,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":358,"patterns":360,"audience":362,"autonomy":28,"adoptionStage":29,"segment":359,"evidenceCount":363,"publicEvidenceCount":363,"organizations":364,"bestGrade":249,"headline":371,"lastVerified":331,"indexable":273},"health-prior-authorization-and-claims-adjudication","AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI for health insurance prior authorization and claims adjudication reads prior authorization requests, medical claims and appeals, and prepares a summary and recommendation for a clinician or adjudicator. It works from the clinical and billing documents attached to each case, extracts diagnoses, treatments and costs, and checks them against the policy and published clinical criteria, while the clinician or adjudicator makes every adverse decision.",[356,357],"insurance","healthcare",[359,19,20],"claims",[23,24,341,323,361],"content-generation","employee-facing",6,[365,366,367,368,369,370],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","Humana","ICICI Lombard","Manulife",{"kpi":372,"label":373,"unit":266,"n":326,"nUpTo":304,"kind":374,"value":375,"qualifier":267,"claimant":376,"organization":251,"vendorReported":273},"handling-time-reduction","Handling time reduction","median",50,"vendor",{"slug":378,"title":379,"shortTitle":380,"definition":381,"status":9,"industries":382,"functions":385,"patterns":386,"audience":27,"autonomy":324,"adoptionStage":325,"segment":27,"evidenceCount":387,"publicEvidenceCount":387,"organizations":388,"bestGrade":249,"headline":397,"lastVerified":331,"indexable":273},"correspondence-triage-and-routing","AI for inbound correspondence triage and routing","Correspondence triage and routing","AI correspondence triage and routing is AI that sorts inbound letters, emails, uploads and secure messages before anyone answers them, so each one reaches the right team. It brings everything into one intake, identifies what each item is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right workflow, replacing the manual sorting desk.",[383,384,356,320],"cross-industry","banking",[20,338,19],[323,23,24],8,[389,390,391,392,393,394,395,396],"Ecclesia Group","Encova Insurance","Irish Life","Loadsure","The Master Trust Bank of Japan","PKO Bank Polski","Travelers","U.S. Department of Veterans Affairs",{"kpi":51,"label":307,"unit":266,"n":71,"nUpTo":304,"kind":398,"value":399,"qualifier":267,"claimant":376,"organization":391,"vendorReported":273},"reported",95,{"indexable":273,"reasons":401},[],[403,408,413,420,426,431,437,444,452,459,466,472,478,485,492,499,505,511,518,523,528,535,541,548,553,558,564,569,575,581,588,593,599,605,611,616],{"id":158,"label":404,"issuer":166,"region":167,"url":405,"description":406,"useCases":407,"indexable":273},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":159,"label":409,"issuer":166,"region":167,"url":410,"description":411,"useCases":412,"indexable":273},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":162,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":273},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":161,"label":421,"issuer":422,"region":225,"url":423,"description":424,"useCases":425,"indexable":273},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":160,"label":427,"issuer":184,"region":167,"url":428,"description":429,"useCases":430,"indexable":273},"UK GDPR","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":432,"label":433,"issuer":166,"region":167,"url":434,"description":435,"useCases":436,"indexable":273},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":438,"label":439,"issuer":440,"region":167,"url":441,"description":442,"useCases":443,"indexable":273},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":445,"label":446,"issuer":447,"region":448,"url":449,"description":450,"useCases":451,"indexable":273},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":453,"label":454,"issuer":455,"region":448,"url":456,"description":457,"useCases":458,"indexable":273},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":460,"label":461,"issuer":462,"region":225,"url":463,"description":464,"useCases":465,"indexable":273},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":467,"label":468,"issuer":166,"region":167,"url":469,"description":470,"useCases":471,"indexable":273},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":473,"label":474,"issuer":475,"region":416,"url":476,"description":477,"useCases":471,"indexable":273},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":479,"label":480,"issuer":481,"region":167,"url":482,"description":483,"useCases":484,"indexable":273},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":486,"label":487,"issuer":488,"region":225,"url":489,"description":490,"useCases":491,"indexable":273},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":493,"label":494,"issuer":495,"region":416,"url":496,"description":497,"useCases":498,"indexable":273},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":500,"label":501,"issuer":166,"region":167,"url":502,"description":503,"useCases":504,"indexable":273},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":506,"label":507,"issuer":508,"region":225,"url":509,"description":510,"useCases":504,"indexable":273},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":512,"label":513,"issuer":514,"region":225,"url":515,"description":516,"useCases":517,"indexable":273},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":519,"label":520,"issuer":166,"region":167,"url":521,"description":522,"useCases":517,"indexable":273},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":524,"label":525,"issuer":166,"region":167,"url":526,"description":527,"useCases":517,"indexable":273},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":529,"label":530,"issuer":531,"region":225,"url":532,"description":533,"useCases":534,"indexable":273},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":536,"label":537,"issuer":538,"region":416,"url":539,"description":540,"useCases":534,"indexable":273},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":542,"label":543,"issuer":544,"region":167,"url":545,"description":546,"useCases":547,"indexable":273},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":549,"label":550,"issuer":166,"region":167,"url":551,"description":552,"useCases":547,"indexable":273},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":554,"label":555,"issuer":166,"region":167,"url":556,"description":557,"useCases":547,"indexable":273},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":559,"label":560,"issuer":447,"region":448,"url":561,"description":562,"useCases":563,"indexable":273},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":565,"label":566,"issuer":166,"region":167,"url":567,"description":568,"useCases":563,"indexable":273},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":570,"label":571,"issuer":572,"region":225,"url":573,"description":574,"useCases":387,"indexable":273},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":576,"label":577,"issuer":166,"region":167,"url":578,"description":579,"useCases":580,"indexable":273},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":582,"label":583,"issuer":584,"region":585,"url":586,"description":587,"useCases":343,"indexable":273},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":589,"label":590,"issuer":166,"region":167,"url":591,"description":592,"useCases":65,"indexable":273},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":594,"label":595,"issuer":596,"region":167,"url":597,"description":598,"useCases":65,"indexable":273},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":600,"label":601,"issuer":602,"region":167,"url":603,"description":604,"useCases":65,"indexable":273},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":606,"label":607,"issuer":608,"region":448,"url":609,"description":610,"useCases":326,"indexable":273},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":612,"label":613,"issuer":166,"region":167,"url":614,"description":615,"useCases":326,"indexable":273},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":617,"label":618,"issuer":619,"region":225,"url":620,"description":621,"useCases":326,"indexable":273},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904361621]