[{"data":1,"prerenderedAt":567},["ShallowReactive",2],{"uc-procurement-spend-classification":3,"uc-regulations":354},{"useCase":4,"evidence":196,"blitsAiDeployments":270,"benchmarks":271,"indicative":272,"related":275,"indexability":352,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":26,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":91,"feasibility":92,"implementation":105,"risk":147,"blitsAi":174,"faq":176,"related":186,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI spend classification and spend analytics for procurement","Spend classification","AI spend classification for procurement","AI assigns purchase lines to spend categories so buyers see what they buy. GSA uses it for category management and the VHA for executive spend analysis.","published","AI that reads purchase orders, invoices, card transactions and contracts and assigns each line of spend to a category in the organization's taxonomy, and to the right supplier, so that procurement can see what is bought, from whom and where to consolidate or negotiate.",[12,13,14,15,16],"spend analytics AI","AI spend categorization","spend cube automation","UNSPSC classification with AI","procurement category classification",[18,19,20,21],"cross-industry","government","manufacturing","healthcare",[23,24,25],"procurement","finance-and-accounting","analytics-and-reporting",[27,28,29],"classification-and-routing","document-processing","summarization",[31,32],"internal-tools","api","back-office","supervised-agent","early-adopters","Procurement can only manage what it can see, and raw spend data does not show it on its own.\nPurchases can arrive from several ERP systems, purchasing cards and expense tools, with free\ntext descriptions (\"gloves blk L 100\"), inconsistent supplier names and general ledger codes that\ndescribe the budget, not the thing bought. Building a reliable view of spend by category means\ncleaning and classifying large volumes of lines. Where that is done by hand in a periodic\nexercise, the view can be out of date by the time it is finished.\n\nThe consequences are practical. Category managers cannot tell how much the organization spends on\na category across units, so they cannot consolidate demand or negotiate on volume, contract\ncompliance and maverick buying go unmeasured, and savings claims are hard to prove. The US federal\ngovernment manages its buying through a government wide category management taxonomy. In the\n2025 inventory, USDA says that to plan for the upcoming fire season all of the previous year's\nincident related purchases are categorized by hand, a process it describes as time consuming; it\nhas piloted a machine learning classifier for this since December 2024.",[],"1. **Gather and clean.** Purchase order lines, invoice lines, card transactions and contract\n   records are extracted from each source system, and supplier names are normalized and matched\n   to one supplier record.\n2. **Classify each line.** A model trained on lines already labeled by buyers, or a language\n   model given the taxonomy with definitions and examples, assigns each line to a category and a\n   subcategory and returns a confidence score.\n3. **Read the contracts.** For contract spend, the AI reads the contract document and summarizes\n   what goods or services it covers, which gives category managers a view of what each contract\n   buys. The IRS uses generative AI to summarize its contracts in support of category management.\n4. **Review the uncertain.** Low confidence lines and high value lines go to a category analyst,\n   whose corrections are fed back as training examples.\n5. **Analyze.** The classified spend feeds dashboards by category, supplier, unit and period,\n   showing consolidation opportunities, contract coverage and trends.",[40,41,42,43],"employee-productivity","cost-to-serve","speed","compliance",[45,46,47,48,49],"automation-rate","accuracy","hours-saved","cost-savings","processing-time-reduction",{"referenceOrg":51,"inputs":52,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"An organization with 2 million purchase lines a year across several ERP and card systems",[53,59,66,73,79],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"lines","Purchase, invoice and card lines per year",2000000,"lines per year","The reference organization. Replace with your own volumes.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"manualShare","Share of lines that need manual classification today",0.2,0.4,"fraction of lines","Editorial assumption, replace with the share your rules or suppliers do not classify.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"automatedShare","Share of that manual work the AI takes over",0.5,0.8,"fraction of manual lines","Editorial assumption; analysts still review low confidence and high value lines.",{"key":74,"label":75,"low":69,"high":76,"unit":77,"note":78},"minutesPerLine","Analyst minutes per manually classified line",1,"minutes per line","Editorial assumption for a trained analyst working in batches.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"hourlyCost","Fully loaded analyst hour",40,70,"USD per hour","Editorial assumption.","lines * manualShare * automatedShare * minutesPerLine / 60 * hourlyCost","USD","per year","Classification labor avoided","Counts only the labor of classifying spend. It leaves out the larger but harder to attribute value of better category decisions (consolidation, negotiation, contract compliance), the cost of the platform and model, and the one time work of building the taxonomy and training data.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","The model is the easy part. The work is in extracting and joining data from several source systems, agreeing a taxonomy with clear definitions, cleaning supplier names and building a labeled sample that reflects the organization's own language.",[96,97,98,99],"Purchase order, invoice and card line data with free text descriptions","A spend taxonomy with definitions and examples per category","A supplier master, or at least a supplier normalization table","A labeled sample of lines classified by experienced buyers",[101,102,103,104],"ERP and purchasing systems","Purchasing card and expense platforms","Contract repository","Analytics or business intelligence tools for the spend dashboards",{"steps":106,"guardrails":122,"humanInTheLoop":127,"kpisToInstrument":128,"failureModes":134},[107,110,113,116,119],{"title":108,"detail":109},"Fix the taxonomy first","Choose the taxonomy (your own, UNSPSC or a public sector category structure) and write a definition and examples for each category. A model cannot be more consistent than the definitions it is given.",{"title":111,"detail":112},"Build a labeled benchmark","Have buyers classify a random sample of a few thousand lines, stratified by source and value, and keep it aside to measure accuracy on every change.",{"title":114,"detail":115},"Classify with confidence thresholds","Let the model classify all lines, accept high confidence lines automatically and route low confidence and high value lines to analysts. Tune the threshold on the benchmark.",{"title":117,"detail":118},"Close the loop","Feed analyst corrections back into training data or prompt examples, and rerun the benchmark before each release.",{"title":120,"detail":121},"Put the result to work","Connect classified spend to category plans and supplier negotiations, and report savings by category, so the classification earns its keep.",[123,124,125,126],"Every line keeps its original description and source, so a classification can be traced and corrected","Confidence score on every line, with low confidence and high value lines reviewed by a person","Accuracy measured on a fixed benchmark before each change to the model or taxonomy","Taxonomy changes versioned, with reclassification of history when definitions change","Category analysts own the taxonomy, review low confidence and high value lines and sample accepted lines each period. Classifications inform decisions but do not trigger purchases or payments.",[129,130,131,132,133],"Share of spend value and of lines classified automatically","Accuracy on the labeled benchmark, by category and source","Analyst hours spent on classification per period","Time from period end to an updated spend view","Savings identified and realized per category",[135,138,141,144],{"title":136,"detail":137},"High accuracy by line, low accuracy by value","The model classifies many small lines well and a few large ones badly. Measure accuracy weighted by spend value and review large lines by hand.",{"title":139,"detail":140},"Taxonomy drift","Categories are renamed or split but old spend is not reclassified, so trends break. Version the taxonomy and reclassify history.",{"title":142,"detail":143},"Garbage descriptions","Lines with empty or generic descriptions cannot be classified reliably by any model. Use the supplier, contract and ledger code as extra signals and fix the capture at the source.",{"title":145,"detail":146},"Dashboards nobody uses","Spend is classified but category plans do not change. Tie the output to specific sourcing decisions from the start.",{"euAiAct":148,"regulations":151,"guidance":156,"controls":168,"incidents":173},{"tier":149,"basis":150},"minimal","Classifying the organization's own purchase lines into categories is not listed in Annex III and is used internally by procurement staff, so no specific obligations apply beyond AI literacy. The data can still contain personal data, for example in purchasing card and expense lines, which brings GDPR duties. Using the classified card and expense lines to monitor or evaluate individual employees would move the system towards Annex III point 4 (employment and worker management) and a high risk assessment.",[152,153,154,155],"eu-ai-act","gdpr","iso-42001","nist-ai-rmf",[157,163],{"title":158,"issuer":159,"region":160,"url":161,"note":162},"M-19-13: Category Management: Making Smarter Use of Common Contract Solutions and Practices","Office of Management and Budget","north-america","https://www.whitehouse.gov/wp-content/uploads/2019/03/M-19-13.pdf","The OMB memorandum that implements category management government wide, defines the role of the Category Management Leadership Council and asks agencies to use spending data and data analytics tools to make data driven buying decisions. It is not specific to AI.",{"title":164,"issuer":165,"region":160,"url":166,"note":167},"Category management","U.S. General Services Administration","https://www.gsa.gov/buy-through-us/category-management","Describes category management as identifying categories of spend and using data to consolidate contracts, manage suppliers and demand, and reduce total cost of ownership.",[169,170,171,172],"Named owner for the taxonomy and for classification quality","Benchmark results and model versions kept with each release","Masking or exclusion of personal data in expense and card lines before classification","Periodic sample audit of automatically accepted lines",[],{"howToBuild":175},"On Blits.ai this runs as an **agentic workflow** on a schedule or through the **REST API**:\n**custom functions** read new purchase, invoice and card lines from the ERP, or from a **SQL\nknowledge base** that holds the extract, and an **agent** with the taxonomy definitions in its\n**knowledge base** returns a category, a confidence and a short reason per line as **structured\noutput**. The workflow routes lines below a confidence score or above a value limit to an\nanalyst, and **human in the loop** confirmation can be required before results are written back\nto the ERP. Every run keeps a full **audit trail**.\n\n**Test suites** built from the buyers' labeled sample act as the accuracy benchmark on each\nchange of model, prompt or taxonomy, and the platform is **model agnostic**, so a cheaper model\ncan classify routine lines and a stronger one the hard cases. **PII masking** applies to traffic\nthrough the gateway; personal data that custom functions pull from expense and card lines should\nalso be masked in the extract before it reaches a model. Category managers can then query the\nclassified results in plain language through an **agent** connected to the SQL knowledge base.",[177,180,183],{"question":178,"answer":179},"Who uses AI for spend classification?","In the US federal government, GSA classifies transactions into the government wide category management taxonomy, the Veterans Health Administration uses generative AI to categorize purchase order lines for an executive spend dashboard, and the IRS has generative AI summarize what each contract buys. None of the inventory entries reports accuracy or savings figures, so measure your own.",{"question":181,"answer":182},"Should we use a language model or a trained classifier?","Both work. A classifier trained on your own labeled lines is cheap and fast at volume; a language model given the taxonomy and examples needs less training data and copes better with new categories. One option is to use the classifier for routine lines and a language model for uncertain ones; whichever you choose, compare both on the same benchmark.",{"question":184,"answer":185},"How accurate does it need to be?","Accurate enough by value for the decisions it supports. Measure accuracy weighted by spend, review high value lines by hand, and report the share of spend classified with high confidence rather than a single accuracy number.",[187,188,189,190],"supplier-invoice-processing","procurement-contract-review","vendor-due-diligence","ledger-and-payment-reconciliation","2026-09-27",[193],{"date":191,"note":194},"First published","procurement-spend-classification",[197,222,237,252],{"title":198,"useCases":199,"organization":200,"vendors":204,"summary":205,"stage":206,"year":207,"channels":208,"languages":209,"metrics":211,"outcomeDisclosed":202,"sources":212,"verification":217,"grade":219,"id":220,"organizationSlug":221},"Internal Revenue Service: generative AI summaries of what each contract buys, for category management",[195],{"name":201,"anonymized":202,"country":203,"region":160,"industry":19},"Internal Revenue Service",false,"US",[],"Since April 2025 the IRS runs an analytics hub for contract documents and contract spending data in which generative AI reads contract PDFs and writes a summary of the products or services bought under each agency contract into a table. The aim is better category management and a better view of what the agency buys. Outputs go to contracting officials for review, and the inventory classifies the use as not high impact because it is not the principal basis for significant decisions. No outcome figures are published.","production",2025,[31],[210],"en",[],[213],{"url":214,"title":215,"publisher":216},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry TREAS-IRS-61, Procurement Data Transparency, Reporting, & Decision Tracking)","Office of Management and Budget (GitHub)",{"level":218,"checkedAt":191},"source-verified","B","irs-procurement-contract-spend-summaries","internal-revenue-service",{"title":223,"useCases":224,"organization":225,"vendors":226,"summary":227,"stage":206,"year":207,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":202,"sources":231,"verification":234,"grade":219,"id":235,"organizationSlug":236},"US General Services Administration: classifying federal transactions into the category management taxonomy",[195],{"name":165,"anonymized":202,"country":203,"region":160,"industry":19},[],"The General Services Administration runs an Acquisition Analytics capability that uses natural language processing to classify transactions within the Government-wide Category Management Taxonomy. The classification lets category managers see how obligations are distributed across categories and decide where to aggregate spend across agencies. The inventory lists it as deployed; no operational date or outcome figures are published.",[31],[210],[],[232],{"url":214,"title":233,"publisher":216},"2025 individually reported AI use cases (GSA entry \"Acquisition Analytics\")",{"level":218,"checkedAt":191},"gsa-acquisition-analytics-spend-classification",null,{"title":238,"useCases":239,"organization":240,"vendors":242,"summary":243,"stage":206,"year":207,"channels":244,"languages":245,"metrics":246,"outcomeDisclosed":202,"sources":247,"verification":250,"grade":219,"id":251,"organizationSlug":236},"Veterans Health Administration: generative AI categorizing purchase order lines for executive spend analysis",[195],{"name":241,"anonymized":202,"country":203,"region":160,"industry":21},"Veterans Health Administration",[],"The Veterans Health Administration uses generative AI to enrich purchase order data from its Integrated Funds Control, Accounting, and Procurement system (IFCAP) by assigning each line item to predefined spend categories that are more useful for analysis. The result feeds an executive dashboard with total spend, line items, categories and trends that can be filtered by region, budget object code, fund control point and vendor, so that network and national leaders can find areas to drive spending efficiencies. The inventory lists it as deployed and not high impact; no outcome figures are published.",[31],[210],[],[248],{"url":214,"title":249,"publisher":216},"2025 individually reported AI use cases (entry VA-25-364, Executive Spend Analysis)",{"level":218,"checkedAt":191},"va-executive-spend-analysis",{"title":253,"useCases":254,"organization":255,"vendors":257,"summary":258,"stage":259,"year":260,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":202,"sources":264,"verification":267,"grade":219,"id":268,"organizationSlug":269},"US Department of Agriculture: machine learning classification of fire season incident purchases (pilot)",[195],{"name":256,"anonymized":202,"country":203,"region":160,"industry":19},"U.S. Department of Agriculture",[],"To plan for each fire season, USDA staff in Natural Resources and Environment categorized all of the previous year's incident related purchases by hand, which took a long time and could miss subtle purchasing patterns. An in house classical machine learning model, trained on 2023 item descriptions (such as protective work gloves) and validated on human labeled item categories from the same year, assigns purchases to common purchase categories identified by procurement staff in the pilot. The goal is to find purchasing patterns and cost savings opportunities, which the agency says could lead to quicker resource allocation to incident locations. The inventory lists it as a pilot since December 2024; no outcome figures are published.","pilot",2024,[31],[210],[],[265],{"url":214,"title":266,"publisher":216},"2025 individually reported AI use cases (entry USDA-135, Yearly Incident Procurement Classification Report)",{"level":218,"checkedAt":191},"usda-incident-procurement-classification","u-s-department-of-agriculture",0,[],{"low":273,"high":274},66666.66666666667,746666.6666666666,[276,305,322,337],{"slug":187,"title":277,"shortTitle":278,"definition":279,"status":9,"industries":280,"functions":284,"patterns":285,"audience":33,"autonomy":34,"adoptionStage":288,"segment":33,"evidenceCount":289,"publicEvidenceCount":290,"organizations":291,"bestGrade":219,"headline":296,"lastVerified":191,"indexable":304},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI that captures supplier invoices from any format, extracts header and line data, matches them to purchase orders and goods receipts, proposes tax and cost centre coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.",[18,281,19,282,283],"banking","retail-and-ecommerce","energy-and-utilities",[24,23],[28,286,287,27],"agentic-workflow","anomaly-detection","mainstream",5,4,[292,293,294,295],"Federal Deposit Insurance Corporation","Kingfisher","U.S. Immigration and Customs Enforcement","Veolia",{"kpi":297,"label":298,"unit":299,"n":76,"nUpTo":76,"kind":300,"value":301,"qualifier":302,"claimant":303,"organization":293,"vendorReported":202},"productivity-gain","Productivity gain","percent","reported",80,"exact","organization",true,{"slug":188,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":310,"patterns":312,"audience":315,"autonomy":316,"adoptionStage":35,"evidenceCount":317,"publicEvidenceCount":289,"organizations":318,"bestGrade":219,"headline":236,"lastVerified":191,"indexable":304},"AI assistant for procurement and supplier contract review","Procurement and contract review","An assistant for procurement and vendor management that reads supplier contracts and proposals, extracts the key terms, flags deviations from the organization's standard positions, drafts requests for proposal and evaluation matrices, and prepares negotiation positions, with a procurement or legal owner approving every conclusion.",[18,281,19,282,20],[23,311,24],"legal",[28,313,314,286],"rag-knowledge-assistant","content-generation","employee-facing","copilot",6,[319,320,201,321],"General Services Administration","Administration for Children and Families","Walmart",{"slug":189,"title":323,"shortTitle":324,"definition":325,"status":9,"industries":326,"functions":329,"patterns":332,"audience":315,"autonomy":316,"adoptionStage":35,"segment":333,"evidenceCount":290,"publicEvidenceCount":290,"organizations":334,"bestGrade":219,"headline":236,"lastVerified":191,"indexable":304},"AI for third party and vendor risk due diligence","Vendor due diligence","AI that reviews a vendor's security questionnaires, SOC and assurance reports, contracts and model documentation against the organization's control requirements, researches the vendor's ownership, sanctions, financial health and adverse media, drafts the risk assessment for a human to approve and keeps the register of material service providers current with ongoing monitoring.",[18,281,327,19,328],"insurance","payments",[23,330,331],"risk-management","regulatory-compliance",[28,313,286,29],"second-line",[335,201,256,336],"U.S. Department of Justice","U.S. Trade and Development Agency",{"slug":190,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":344,"patterns":346,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"evidenceCount":290,"publicEvidenceCount":290,"organizations":347,"bestGrade":219,"headline":236,"lastVerified":191,"indexable":304},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[281,328,342,18,343,19],"capital-markets","wealth-and-asset-management",[24,345],"operations",[286,287,28],[348,349,350,351],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"indexable":304,"reasons":353},[],[355,362,367,374,380,386,393,400,408,415,422,428,435,442,448,453,460,466,472,478,484,490,496,501,506,513,520,525,530,537,543,549,556,561],{"id":152,"label":356,"issuer":357,"region":358,"url":359,"description":360,"useCases":361,"indexable":304},"EU AI Act","European Union","europe","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":153,"label":363,"issuer":357,"region":358,"url":364,"description":365,"useCases":366,"indexable":304},"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":154,"label":368,"issuer":369,"region":370,"url":371,"description":372,"useCases":373,"indexable":304},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":155,"label":375,"issuer":376,"region":160,"url":377,"description":378,"useCases":379,"indexable":304},"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":381,"label":382,"issuer":357,"region":358,"url":383,"description":384,"useCases":385,"indexable":304},"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":387,"label":388,"issuer":389,"region":358,"url":390,"description":391,"useCases":392,"indexable":304},"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":394,"label":395,"issuer":396,"region":358,"url":397,"description":398,"useCases":399,"indexable":304},"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":401,"label":402,"issuer":403,"region":404,"url":405,"description":406,"useCases":407,"indexable":304},"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":409,"label":410,"issuer":411,"region":404,"url":412,"description":413,"useCases":414,"indexable":304},"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":416,"label":417,"issuer":418,"region":370,"url":419,"description":420,"useCases":421,"indexable":304},"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":423,"label":424,"issuer":425,"region":160,"url":426,"description":427,"useCases":421,"indexable":304},"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":429,"label":430,"issuer":431,"region":358,"url":432,"description":433,"useCases":434,"indexable":304},"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":436,"label":437,"issuer":438,"region":370,"url":439,"description":440,"useCases":441,"indexable":304},"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":443,"label":444,"issuer":357,"region":358,"url":445,"description":446,"useCases":447,"indexable":304},"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":449,"label":450,"issuer":357,"region":358,"url":451,"description":452,"useCases":447,"indexable":304},"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":454,"label":455,"issuer":456,"region":160,"url":457,"description":458,"useCases":459,"indexable":304},"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":461,"label":462,"issuer":357,"region":358,"url":463,"description":464,"useCases":465,"indexable":304},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":467,"label":468,"issuer":469,"region":160,"url":470,"description":471,"useCases":465,"indexable":304},"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":473,"label":474,"issuer":475,"region":370,"url":476,"description":477,"useCases":465,"indexable":304},"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":479,"label":480,"issuer":357,"region":358,"url":481,"description":482,"useCases":483,"indexable":304},"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":485,"label":486,"issuer":487,"region":160,"url":488,"description":489,"useCases":483,"indexable":304},"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":491,"label":492,"issuer":403,"region":404,"url":493,"description":494,"useCases":495,"indexable":304},"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":497,"label":498,"issuer":357,"region":358,"url":499,"description":500,"useCases":495,"indexable":304},"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":502,"label":503,"issuer":357,"region":358,"url":504,"description":505,"useCases":495,"indexable":304},"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":507,"label":508,"issuer":509,"region":358,"url":510,"description":511,"useCases":512,"indexable":304},"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":514,"label":515,"issuer":516,"region":160,"url":517,"description":518,"useCases":519,"indexable":304},"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":521,"label":522,"issuer":357,"region":358,"url":523,"description":524,"useCases":519,"indexable":304},"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":526,"label":527,"issuer":357,"region":358,"url":528,"description":529,"useCases":317,"indexable":304},"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":531,"label":532,"issuer":533,"region":534,"url":535,"description":536,"useCases":289,"indexable":304},"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":538,"label":539,"issuer":540,"region":358,"url":541,"description":542,"useCases":290,"indexable":304},"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":544,"label":545,"issuer":546,"region":358,"url":547,"description":548,"useCases":290,"indexable":304},"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":550,"label":551,"issuer":552,"region":404,"url":553,"description":554,"useCases":555,"indexable":304},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":557,"label":558,"issuer":357,"region":358,"url":559,"description":560,"useCases":555,"indexable":304},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":562,"label":563,"issuer":564,"region":160,"url":565,"description":566,"useCases":555,"indexable":304},"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.",1790598306354]