[{"data":1,"prerenderedAt":519},["ShallowReactive",2],{"uc-ai-drug-discovery-platform":3,"uc-regulations":309},{"useCase":4,"evidence":158,"blitsAiDeployments":220,"benchmarks":221,"indicative":229,"related":232,"indexability":307,"includeUnpublished":164},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":36,"indicativeValue":39,"macroEstimates":67,"feasibility":68,"implementation":79,"risk":111,"blitsAi":137,"faq":139,"related":152,"datePublished":153,"dateModified":153,"lastVerified":153,"changelog":154,"slug":157},"AI native platform for drug target discovery and molecule design","AI drug discovery platform","AI drug discovery platform for biotech","Generative AI screens drug targets and designs molecules before chemists synthesize them. Insilico reports 12 to 18 months to a candidate, against 2.5 to 4 years.","published","An AI native research platform that prioritizes disease targets from biological data, generates and optimizes candidate drug molecules computationally, and predicts their properties before a chemist synthesizes and tests them, so a pharmaceutical or biotech company reaches a validated preclinical candidate with far fewer molecules made and tested than a conventional medicinal chemistry program.",[12,13,14],"generative AI drug discovery","AI drug design platform","computational drug discovery",[16],"pharma-and-life-sciences",[18,19],"operations","analytics-and-reporting",[21,22],"prediction-and-scoring","content-generation",[24],"internal-tools","employee-facing","copilot","early-adopters","drug discovery","Conventional small molecule drug discovery starts from a target hypothesis and then screens, makes\nand tests thousands of candidate compounds to find a handful worth advancing, before any clinical\ntesting begins. Insilico Medicine puts traditional early stage discovery at two and a half to four\nyears to a preclinical candidate; Recursion Pharmaceuticals puts the industry average at over 2,500\ncompounds synthesized and 42 months per program.\n\nAn AI native platform tries to spend that cost computationally instead: predicting which targets are\nworth pursuing and which molecules are likely to bind, be selective and be safe before a chemist\nmakes anything, so the synthesis and testing budget is spent on a much shorter list of higher\nprobability candidates. It does not remove the need for real chemistry, real assays or real clinical\ntrials; it changes how many molecules a team has to make to find one worth testing in a person.",[],"1. **Model the biology.** Multi omics data, scientific literature and patent intelligence feed models\n   that score and rank potential disease targets against criteria such as novelty, druggability and\n   safety.\n2. **Generate candidate molecules.** Generative chemistry models propose novel molecular structures\n   against the chosen target, rather than screening only a fixed, existing compound library.\n3. **Predict properties computationally.** Models predict binding, selectivity, toxicity and\n   pharmacokinetic properties before any molecule is made, narrowing a large design space to a short\n   list worth synthesizing.\n4. **Synthesize and test the short list.** Chemists make and test only the highest scoring molecules,\n   and the assay results feed back into the models for the next design round.\n5. **Advance to preclinical and clinical development.** The nominated candidate moves into the same\n   regulated preclinical and clinical pathway as any other drug. In this use case, the AI's role ends\n   at candidate nomination (some platforms, such as Insilico's, also offer separate clinical trial\n   prediction tools, which are a different use case); every later stage here is validated by\n   conventional testing.",[33,34,35],"speed","cost-to-serve","employee-productivity",[37,38],"processing-time-reduction","productivity-gain",{"referenceOrg":40,"inputs":41,"formula":62,"currency":63,"period":64,"resultLabel":65,"caveat":66},"A biotech starting 10 early discovery programs a year",[42,48,55],{"key":43,"label":44,"low":45,"high":45,"unit":46,"note":47},"programs","Discovery programs started per year",10,"programs per year","The reference biotech.",{"key":49,"label":50,"low":51,"high":52,"unit":53,"note":54},"monthsSaved","Months saved per program versus a conventional discovery timeline",12,24,"months per program","Conservative against the benchmarks on this page (Insilico Medicine reports reaching preclinical candidate nomination in an average of 12 to 18 months against a traditional 2.5 to 4 years; Recursion Pharmaceuticals reports about 17 months against an industry average of 42 months), because a first program on a new platform rarely matches a mature platform's average.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"costPerMonth","Fully loaded cost of a discovery program team per month",150000,300000,"USD per program per month","Editorial assumption for a mid sized medicinal chemistry and biology team; replace with your own.","programs * monthsSaved * costPerMonth","USD","per year","Annual discovery program cost avoided through faster preclinical candidate nomination","Time saved in discovery only. It leaves out the platform's own cost, the preclinical and clinical development that follows candidate nomination (unchanged by this use case), and the fact that a faster nomination is not the same as a successful drug: Recursion Pharmaceuticals' own risk disclosure says the risk of failure in pharmaceutical research and development is high and failure can occur at any stage before or after regulatory approval, AI discovered or not.",[],{"complexity":69,"complexityNote":70,"dataPrerequisites":71,"integrations":75},"high","The AI shortens discovery, not validation. A nominated candidate still needs full preclinical safety and efficacy testing, an investigational new drug filing and clinical trials under existing pharmaceutical regulation. Getting real predictive power out of the models needs a large, curated internal dataset of assay results to train and validate against, not just public data.",[72,73,74],"Curated multi omics data (genomics, proteomics, disease models) for target scoring","A large internal dataset of assay results (binding, ADMET, toxicity) to train and validate predictive models against","Structural biology data for the targets in scope, where available",[76,77,78],"Electronic lab notebook and assay data management systems","Compound registration and inventory systems","Computational chemistry and structural biology tools",{"steps":80,"guardrails":96,"humanInTheLoop":99,"kpisToInstrument":100,"failureModes":104},[81,84,87,90,93],{"title":82,"detail":83},"Pick a target class the model can actually score","Start where public and internal data on the biology and known chemical matter is strongest; a completely novel target with almost no data starves the model of signal.",{"title":85,"detail":86},"Set a synthesize and test budget per round","Cap how many AI proposed molecules a chemistry team commits to making per cycle, and measure hit rate against that budget, rather than running an open ended search.",{"title":88,"detail":89},"Keep a qualified chemist in every loop","A medicinal chemist reviews every proposed structure for synthesizability and known liabilities before it is made, and can veto a candidate the model scored well.",{"title":91,"detail":92},"Track the whole funnel, not just discovery speed","Preclinical candidate nomination is not success. Follow programs into IND filing and Phase 1 to see whether faster discovery produces better drugs, not just more of them, faster.",{"title":94,"detail":95},"Validate predictions against your own assay data before trusting them","A platform's published benchmark numbers come from its own historical programs. Run a blinded internal validation before betting a program's timeline on the model.",[97,98],"A qualified medicinal chemist reviews and approves every AI proposed molecule before synthesis","Predictive model performance is validated against blinded internal assay data, not only the platform's own published benchmarks","Chemists and biologists review every AI proposed target and molecule; the AI narrows the design space and predicts properties, it does not decide what gets synthesized or what advances. A program only moves to candidate nomination after conventional wet lab confirmation of the predicted properties.",[101,102,103],"Molecules synthesized and tested per program, against the target budget","Time from project initiation to preclinical candidate nomination","Attrition rate of AI nominated candidates through IND filing and Phase 1, compared with the company's historical baseline",[105,108],{"title":106,"detail":107},"Optimizing for a metric that is not a drug","A model can hit its binding or property targets and still nominate a molecule that fails for reasons the model was not trained to predict, such as manufacturability or an off target effect found only in later testing. Track attrition through clinical development, not only discovery speed.",{"title":109,"detail":110},"Overfitting to public and vendor benchmark data","A platform's published cycle time and molecule count figures come from its own historical programs and target classes. Validate on your own target and internal data before assuming the same numbers apply.",{"euAiAct":112,"regulations":115,"guidance":119,"controls":132,"incidents":136},{"tier":113,"basis":114},"minimal","Target scoring and molecule generation are not a safety component of an Annex I product and are not one of the Annex III high risk areas (Article 6), so they do not become high risk on that route. Insofar as the platform and its training are themselves scientific research and development, activity that stays there falls outside the Regulation entirely under the Article 2(6) research exclusion. The resulting drug candidate is separately regulated as a medicine, not as an AI system, through the normal pharmaceutical approval pathway; conventional preclinical and clinical testing validates the AI's outputs before anything reaches a patient.",[116,117,118],"eu-ai-act","iso-42001","nist-ai-rmf",[120,126],{"title":121,"issuer":122,"region":123,"url":124,"note":125},"Article 2: Scope","European Union","europe","https://artificialintelligenceact.eu/article/2/","Article 2(6) says the Regulation \"does not apply to AI systems or AI models, including their output, specifically developed and put into service for the sole purpose of scientific research and development\"; Article 2(8) extends this to \"any research, testing or development activity regarding AI systems or AI models prior to their being placed on the market or put into service.\" Article 6 itself, by contrast, says nothing about research: it sets the Annex I safety component route and the Annex III route for high risk classification.",{"title":127,"issuer":128,"region":129,"url":130,"note":131},"Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products","US Food and Drug Administration","north-america","https://www.fda.gov/media/184830/download","This January 2025 draft guidance covers AI models used to produce information that supports a regulatory decision on a drug, such as evidence in a submission. It explicitly excludes this use case's discovery stage: \"the use of AI for the purposes of drug discovery is not in the scope of this guidance,\" so a platform that stops at candidate nomination sits outside it; the guidance's credibility assessment framework becomes relevant only once AI generated outputs are used to support a later regulatory decision.",[133,134,135],"Documented model validation against internal assay data before a program relies on a prediction","Named chemist and biologist sign off on every candidate before it advances to synthesis and formal preclinical testing","Data provenance and access controls over the proprietary assay data used to train and validate models",[],{"howToBuild":138},"Drug target scoring and molecule generation need specialized biology and chemistry models that sit\noutside Blits.ai's scope; Blits.ai is not a computational chemistry or structural biology platform.\nWhat Blits.ai can support well is the research operations layer around a discovery program: an\n**AI agent** backed by a **knowledge base** and **hybrid retrieval** over a program's internal\nliterature reviews, target briefs and assay reports, so scientists can ask plain language questions\nacross a program's own documentation instead of searching file by file.\n\n**Custom functions** can connect the agent to an electronic lab notebook or compound registration\nsystem to look up assay results or a compound's registration status, and an **agentic workflow**\ncan route a newly generated candidate through the internal review and sign off steps described in\nthe implementation guardrails above, with **human in the loop approval** at each checkpoint. Keep\nthe target scoring and molecule generation models themselves outside the platform; use Blits.ai for\nthe knowledge access and review workflow around them.",[140,143,146,149],{"question":141,"answer":142},"Has an AI discovered drug reached patients yet?","Insilico Medicine's rentosertib, a candidate for idiopathic pulmonary fibrosis whose target and molecule were both identified with its AI platform, became the company's first asset to enter a Phase III clinical trial, in July 2026, after a randomized Phase IIa trial published in Nature Medicine showed a positive lung function signal. According to Insilico, rentosertib remains investigational and has not been approved by any regulatory authority.",{"question":144,"answer":145},"How much faster is AI native drug discovery?","Insilico Medicine reports reaching preclinical candidate nomination for 22 programs between 2021 and 2024 in an average of 12 to 18 months, against a traditional 2.5 to 4 years, synthesizing only about 60 to 200 molecules per project. Recursion Pharmaceuticals reports advanced candidates have been delivered by synthesizing about 330 compounds per program in about 17 months, against industry averages of over 2,500 compounds and 42 months. Both figures describe the companies' own historical programs, not a guarantee for any specific target.",{"question":147,"answer":148},"Does this replace medicinal chemists?","No. The platform proposes structures and predicts properties, but the molecules still have to be made and tested in real assays, and the programs on this page still rely on chemistry and biology teams to validate what the models propose. We recommend a qualified chemist review every AI proposed structure before synthesis (see the implementation guardrails).",{"question":150,"answer":151},"Is this the same as AI used in clinical trials or regulatory writing?","No. This use case covers the discovery stage, from target identification to a nominated preclinical candidate. Matching patients to trials and drafting clinical study reports are separate, later stage use cases with their own evidence.",[],"2026-09-28",[155],{"date":153,"note":156},"First published","ai-drug-discovery-platform",[159,197],{"title":160,"useCases":161,"organization":162,"vendors":166,"summary":167,"stage":168,"year":169,"channels":170,"languages":171,"metrics":173,"outcomeDisclosed":183,"sources":184,"verification":192,"grade":194,"id":195,"organizationSlug":196},"Insilico Medicine: Pharma.AI discovery platform",[157],{"name":163,"anonymized":164,"region":165,"industry":16},"Insilico Medicine",false,"global",[],"Insilico Medicine runs an end to end AI platform, Pharma.AI, that combines target discovery (PandaOmics), generative molecule design (Chemistry42) and translational and clinical support tools to move programs from a biological hypothesis to a nominated preclinical candidate. Its most advanced program, rentosertib (formerly ISM001-055 / INS018_055), a TNIK inhibitor for idiopathic pulmonary fibrosis whose target and molecule were both identified and designed with this platform, entered a Phase III clinical trial in July 2026 after a randomized Phase IIa trial published in Nature Medicine showed a dose dependent lung function signal. Beyond rentosertib, the company reports 31 preclinical candidate nominations from its pipeline, 13 of which received IND clearance and 8 of which have reached ongoing Phase I trials.","production",2026,[24],[172],"en",[174],{"kpi":37,"value":175,"unit":176,"qualifier":177,"period":178,"baseline":179,"claimant":180,"quote":181,"sourceUrl":182},60,"percent","approximately","as stated in Insilico's June 2025 Nature Medicine publication release (22 nominated candidates, 2021 to 2024); the July 2026 release quoted above restates the same 12 to 18 month range without an updated candidate count","traditional early stage drug discovery, typically 2.5 to 4 years to preclinical candidate nomination","organization","While traditional early-stage drug discovery typically takes 2.5 to 4 years, Insilico has consistently reached preclinical candidate (PCC) nomination in an average of just 12 to 18 months, with only 60 to 200 molecules synthesized and tested per program.","https://insilico.com/news/xmjsn4l091-insilico-initiates-phase-iii-clinical-tr",true,[185,188],{"url":182,"title":186,"publisher":163,"date":187},"Insilico Initiates Phase III Clinical Trial for Rentosertib, Its AI-Empowered TNIK Inhibitor for Idiopathic Pulmonary Fibrosis","2026-07-07",{"url":189,"title":190,"publisher":163,"date":191},"https://insilico.com/news/tnrecuxsc1-insilico-announces-nature-medicine-publi","Insilico Announces Nature Medicine Publication of Phase IIa Results of Rentosertib","2025-06-03",{"level":193,"checkedAt":153},"source-verified","B","insilico-medicine-pharma-ai-platform",null,{"title":198,"useCases":199,"organization":200,"vendors":203,"summary":204,"stage":168,"year":169,"channels":205,"languages":206,"metrics":207,"outcomeDisclosed":183,"sources":213,"verification":218,"grade":194,"id":219,"organizationSlug":196},"Recursion Pharmaceuticals: Recursion OS drug design platform",[157],{"name":201,"anonymized":164,"country":202,"region":129,"industry":16},"Recursion Pharmaceuticals","US",[],"Recursion Pharmaceuticals, which completed its business combination with Exscientia in November 2024, runs an AI native operating system that combines phenomic screening with automated, precision small molecule chemistry to take programs from an initial hit to a development candidate. As of its February 2026 results, the company reports the platform has delivered more than 10 development candidates to date, including REC-617, a CDK7 inhibitor identified as lead candidate in under 11 months with 136 novel compounds synthesized, and REC-7735, a PI3Kα H1047R inhibitor precision designed with 242 compounds synthesized from first novel hit to REC-7735 in 10 months, now in IND enabling studies. Recursion's partnership with Sanofi has the potential for up to 15 AI designed small molecule programs, of which 5 or more span immunology and oncology, and had reached five progress based milestones as of the same results.",[24],[172],[208],{"kpi":37,"value":175,"unit":176,"qualifier":177,"period":209,"baseline":210,"claimant":180,"quote":211,"sourceUrl":212},"reported February 2026; average for advanced candidates delivered by the platform, per program","industry average of over 2,500 compounds and 42 months per program","Advanced candidates have been delivered by synthesizing ~330 compounds per program in ~17 months, compared to industry averages of over 2,500 compounds and 42 months, respectively.","https://www.globenewswire.com/news-release/2026/02/25/3244408/0/en/recursion-reports-fourth-quarter-and-full-year-2025-financial-results-and-provides-business-update.html",[214],{"url":212,"title":215,"publisher":216,"date":217},"Recursion Reports Fourth Quarter and Full Year 2025 Financial Results and Provides Business Update","Recursion Pharmaceuticals, Inc.","2026-02-25",{"level":193,"checkedAt":153},"recursion-pharmaceuticals-os-platform",0,[222],{"kpi":37,"label":223,"unit":176,"aggregate":183,"higherIsBetter":183,"n":224,"nUpTo":220,"median":175,"min":175,"max":175,"byClaimant":225,"vendorOnly":164,"points":226},"Cycle time reduction",2,{"organization":224,"vendor":220,"regulator":220,"independent":220},[227,228],{"evidenceId":195,"organization":163,"value":175,"qualifier":177,"claimant":180,"grade":194,"pooled":183},{"evidenceId":219,"organization":201,"value":175,"qualifier":177,"claimant":180,"grade":194,"pooled":183},{"low":230,"high":231},18000000,72000000,[233,256,271,293],{"slug":234,"title":235,"shortTitle":236,"definition":237,"status":9,"industries":238,"functions":241,"patterns":243,"audience":246,"autonomy":26,"adoptionStage":247,"segment":248,"evidenceCount":249,"publicEvidenceCount":250,"organizations":251,"bestGrade":194,"headline":196,"lastVerified":255,"indexable":183},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[239,240],"wealth-and-asset-management","banking",[18,242,19],"risk-management",[244,245,21,22],"anomaly-detection","agentic-workflow","back-office","emerging","middle-office",4,3,[252,253,254],"Morgan Stanley","SimCorp","Vanguard","2026-09-27",{"slug":257,"title":258,"shortTitle":259,"definition":260,"status":9,"industries":261,"functions":263,"patterns":264,"audience":246,"autonomy":265,"adoptionStage":27,"segment":266,"evidenceCount":224,"publicEvidenceCount":224,"organizations":267,"bestGrade":270,"headline":196,"lastVerified":153,"indexable":183},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[262],"energy-and-utilities",[18,19],[244,21],"assist","metering-and-billing",[268,269],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)","C",{"slug":272,"title":273,"shortTitle":274,"definition":275,"status":9,"industries":276,"functions":278,"patterns":279,"audience":25,"autonomy":265,"adoptionStage":27,"evidenceCount":250,"publicEvidenceCount":250,"organizations":282,"bestGrade":194,"headline":286,"lastVerified":255,"indexable":183},"clinical-trial-patient-matching","AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI that reads structured data and clinical notes in the health record, compares each patient with the inclusion and exclusion criteria of open clinical trials, and gives research staff and treating clinicians a ranked list of likely eligible patients with the evidence for each criterion, so that people confirm eligibility and invite the patient.",[277,16],"healthcare",[18,19],[280,281],"document-processing","classification-and-routing",[283,284,285],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":287,"label":288,"unit":176,"n":289,"nUpTo":220,"kind":290,"value":291,"qualifier":292,"claimant":180,"organization":283,"vendorReported":164},"accuracy","Accuracy",1,"reported",100,"exact",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":299,"patterns":300,"audience":25,"autonomy":265,"adoptionStage":27,"segment":301,"evidenceCount":224,"publicEvidenceCount":224,"organizations":302,"bestGrade":194,"headline":305,"lastVerified":153,"indexable":183},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[277],[18,19],[21,281,244],"hospital operations",[303,304],"Humber River Health","Johns Hopkins Medicine",{"kpi":37,"label":223,"unit":176,"n":224,"nUpTo":220,"kind":290,"value":306,"qualifier":292,"claimant":180,"organization":304,"vendorReported":164},38,{"indexable":183,"reasons":308},[],[310,315,321,327,333,339,346,353,361,368,375,381,388,395,401,406,413,418,424,430,436,442,447,452,457,464,471,476,482,490,496,502,508,513],{"id":116,"label":311,"issuer":122,"region":123,"url":312,"description":313,"useCases":314,"indexable":183},"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":316,"label":317,"issuer":122,"region":123,"url":318,"description":319,"useCases":320,"indexable":183},"gdpr","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":117,"label":322,"issuer":323,"region":165,"url":324,"description":325,"useCases":326,"indexable":183},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":118,"label":328,"issuer":329,"region":129,"url":330,"description":331,"useCases":332,"indexable":183},"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":334,"label":335,"issuer":122,"region":123,"url":336,"description":337,"useCases":338,"indexable":183},"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":340,"label":341,"issuer":342,"region":123,"url":343,"description":344,"useCases":345,"indexable":183},"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":347,"label":348,"issuer":349,"region":123,"url":350,"description":351,"useCases":352,"indexable":183},"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":354,"label":355,"issuer":356,"region":357,"url":358,"description":359,"useCases":360,"indexable":183},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":362,"label":363,"issuer":364,"region":357,"url":365,"description":366,"useCases":367,"indexable":183},"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":369,"label":370,"issuer":371,"region":165,"url":372,"description":373,"useCases":374,"indexable":183},"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":376,"label":377,"issuer":378,"region":129,"url":379,"description":380,"useCases":374,"indexable":183},"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":382,"label":383,"issuer":384,"region":123,"url":385,"description":386,"useCases":387,"indexable":183},"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":389,"label":390,"issuer":391,"region":165,"url":392,"description":393,"useCases":394,"indexable":183},"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":396,"label":397,"issuer":122,"region":123,"url":398,"description":399,"useCases":400,"indexable":183},"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":402,"label":403,"issuer":122,"region":123,"url":404,"description":405,"useCases":400,"indexable":183},"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":407,"label":408,"issuer":409,"region":129,"url":410,"description":411,"useCases":412,"indexable":183},"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":414,"label":415,"issuer":122,"region":123,"url":416,"description":417,"useCases":51,"indexable":183},"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":419,"label":420,"issuer":421,"region":129,"url":422,"description":423,"useCases":51,"indexable":183},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":425,"label":426,"issuer":427,"region":165,"url":428,"description":429,"useCases":51,"indexable":183},"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":431,"label":432,"issuer":122,"region":123,"url":433,"description":434,"useCases":435,"indexable":183},"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":437,"label":438,"issuer":439,"region":129,"url":440,"description":441,"useCases":435,"indexable":183},"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":443,"label":444,"issuer":356,"region":357,"url":445,"description":446,"useCases":45,"indexable":183},"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":448,"label":449,"issuer":122,"region":123,"url":450,"description":451,"useCases":45,"indexable":183},"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":453,"label":454,"issuer":122,"region":123,"url":455,"description":456,"useCases":45,"indexable":183},"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":458,"label":459,"issuer":460,"region":123,"url":461,"description":462,"useCases":463,"indexable":183},"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.",9,{"id":465,"label":466,"issuer":467,"region":129,"url":468,"description":469,"useCases":470,"indexable":183},"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":472,"label":473,"issuer":122,"region":123,"url":474,"description":475,"useCases":470,"indexable":183},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":477,"label":478,"issuer":122,"region":123,"url":479,"description":480,"useCases":481,"indexable":183},"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":483,"label":484,"issuer":485,"region":486,"url":487,"description":488,"useCases":489,"indexable":183},"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":491,"label":492,"issuer":493,"region":123,"url":494,"description":495,"useCases":249,"indexable":183},"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":497,"label":498,"issuer":499,"region":123,"url":500,"description":501,"useCases":249,"indexable":183},"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":503,"label":504,"issuer":505,"region":357,"url":506,"description":507,"useCases":250,"indexable":183},"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":509,"label":510,"issuer":122,"region":123,"url":511,"description":512,"useCases":250,"indexable":183},"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":514,"label":515,"issuer":516,"region":129,"url":517,"description":518,"useCases":250,"indexable":183},"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.",1790598302889]