[{"data":1,"prerenderedAt":652},["ShallowReactive",2],{"uc-autonomous-rfq-and-tail-spend-sourcing":3,"uc-regulations":431},{"useCase":4,"evidence":211,"blitsAiDeployments":322,"benchmarks":323,"indicative":342,"related":345,"indexability":429,"includeUnpublished":217},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":25,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":48,"macroEstimates":97,"feasibility":98,"implementation":111,"risk":157,"blitsAi":186,"faq":188,"related":201,"datePublished":206,"dateModified":206,"lastVerified":206,"changelog":207,"slug":210},"AI agent for autonomous RFQ and tail spend sourcing","RFQ and tail spend sourcing","AI for RFQ and tail spend sourcing","AI sends RFQs, compares bids and awards small purchases within rules a buyer sets. Fairmarkit reports Snowflake's average cycle fell from about 25 to 6.4 days.","published","Autonomous RFQ sourcing is AI that sends requests for quotation to suppliers, compares the bids and recommends or makes the award for small purchases, within rules a buyer sets. It covers the tail of spend, the many low value or repeat requests that buyers rarely have time to compete, and hands complex or high value events to a person.",[12,13,14,15,16],"autonomous sourcing","tail spend automation","automated RFQ","sourcing bot","agentic sourcing",[18,19,20,21],"cross-industry","energy-and-utilities","technology","logistics-and-transportation",[23,24],"procurement","operations",[26,27,28],"agentic-workflow","document-processing","content-generation",[30,31,32],"email","internal-tools","api","back-office","supervised-agent","early-adopters","Large, strategic categories usually get a proper sourcing event. The tail does not: maintenance\nparts, facilities services, one off services and repeat buys that each look small. A buyer who\nsources one of them by hand writes the requirements, finds vendors, sends the request by email,\nchases answers, copies bids into a spreadsheet and writes the award. When buyers are short of\ntime, a small request can end up with a known vendor without being competed.\n\nThe case studies on this page describe the same starting point. Fairmarkit's account of Snowflake\nsays its procurement team of about 12 people ran sourcing largely by email, with an average cycle of\nabout 25 days. Its account of G42 describes an average cycle of 5 to 7 days for a team of two\nmanagers and four buyers, with requesters unsure how to source what they needed. A bp director\ndescribes sourcing and contract negotiations as a manual process that takes a great deal of time\nand people, and Fairmarkit's case study adds that bp's organization had shrunk by 25% over\n18 months while the same volume of work remained. Public buyers add a rule on top: the MBTA case\nstudy describes bidding regulations under which two bids are received before an order is finalized.",[],"1. **Take the request in.** A requester describes what they need in a form or a chat, or a\n   requisition arrives from the ERP. The agent turns it into a structured request: item or scope,\n   quantity, delivery date and the attachments a vendor needs.\n2. **Check it against the rules.** Policy decides whether the request may be sourced automatically:\n   value limit, category, approved or required supplier lists, number of bids needed. Anything outside\n   the rules goes to a buyer as a normal sourcing task.\n3. **Pick vendors and send the RFQ.** The agent selects vendors from the supplier master and the\n   history of past bids, and sends the request to several of them.\n4. **Collect and compare the bids.** Quotes come back by email or portal and are extracted into one\n   comparison on price, delivery and stated terms. The agent chases vendors that have not answered.\n5. **Award or recommend.** Within set limits the agent awards to the best bid and notifies the\n   winner and the others. Above the limits, or when bids are unclear, it shows the comparison and a\n   recommendation to a buyer, who decides.\n6. **Record and learn.** The award, the bids and the savings against the earlier price are stored\n   with the request, and the data feeds the next vendor selection and the spend reports.",[40,41,42,43],"speed","employee-productivity","cost-to-serve","compliance",[45,46,47],"cycle-time-days","processing-time-reduction","automation-rate",{"referenceOrg":49,"inputs":50,"formula":92,"currency":93,"period":94,"resultLabel":95,"caveat":96},"A manufacturer with 6,000 small sourcing requests a year",[51,57,64,71,78,85],{"key":52,"label":53,"low":54,"high":54,"unit":55,"note":56},"requests","Small sourcing requests per year",6000,"requests per year","The reference organization. Replace with your own count of requisitions below your sourcing threshold.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"automatedShare","Share of requests the agent sources without a buyer",0.1,0.3,"fraction of requests","At or below the one share reported on this page (Fairmarkit reports 31.8% of Snowflake's sourcing events running autonomously). That is a single vendor reported result, and the share you reach depends on the rules you set.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"hoursSaved","Buyer hours saved per automated request",1,3,"hours per request","Editorial assumption for writing, sending, chasing and comparing a small RFQ by hand. Replace with a time study from your own buyers.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"hourlyCost","Fully loaded buyer hour",40,70,"USD per hour","Editorial assumption.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"averageSpend","Average spend per request",3000,8000,"USD per request","Editorial assumption for tail spend, replace with the average from your own requisitions.",{"key":86,"label":87,"low":88,"high":89,"unit":90,"note":91},"savingsRate","Price improvement from competing the request",0.02,0.05,"fraction of spend","Editorial assumption, deliberately low. Fairmarkit reports an additional 5% of awarded savings at Snowflake, but gives no baseline for it, so the range here is set lower. Replace with the savings you can show on your own awarded events.","requests * automatedShare * (hoursSaved * hourlyCost + averageSpend * savingsRate)","USD","per year","Buyer time and price savings from automated RFQs","Counts buyer time and a flat price improvement on automated requests only. It leaves out the cost of the platform and its set up, the effort of onboarding vendors, savings that would have happened anyway through an ordinary quote, compliance benefits, and the value of buyers moving to strategic categories.",[],{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"medium","The agent needs requisitions and supplier data from the ERP or procurement suite, a written rule set for which purchases may be sourced and awarded without a buyer, and vendors who answer through a channel the system can read. Awarding is a commercial commitment, so approval thresholds and a purchase order handoff have to be agreed with finance before anything runs autonomously.",[102,103,104,105],"Requisitions with item descriptions, quantities and delivery dates","A supplier master with categories, contacts and approval status","History of past quotes and awards per category","Written sourcing policy with value limits, bid counts and approved supplier rules",[107,108,109,110],"ERP or purchasing system for requisitions, purchase orders and supplier master","Procurement suite or intake tool where requesters submit needs","Email or supplier portal for RFQs and bids","Contract repository, to check for existing agreements before going to market",{"steps":112,"guardrails":131,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[113,116,119,122,125,128],{"title":114,"detail":115},"Choose the first categories","Take a spend cube and pick two or three tail categories with many repeat requests, clear specifications and several known vendors, such as maintenance parts or facilities services. Leave engineered items and sole source purchases out of the first wave.",{"title":117,"detail":118},"Write the award rules","Decide per category the value limit for automatic award, the number of bids required, the approved and excluded vendors and the tie break between price and delivery. Have finance and legal sign the rules before the agent can use them.",{"title":120,"detail":121},"Start with recommend only","Run the agent so that it sends RFQs and compares bids but a buyer clicks the award. Compare its recommendation with what buyers would have chosen, on a sample of at least a few hundred events.",{"title":123,"detail":124},"Switch on auto send, then auto award","Allow the agent to send requests without review once the requests it drafts are accepted almost every time. Allow automatic award only for the categories and limits where the comparison has matched the buyers' choice.",{"title":126,"detail":127},"Open the supplier base up","Add vendors to each category deliberately, including local and diverse vendors where policy calls for it, and measure how many bids each request receives.",{"title":129,"detail":130},"Report savings against a baseline","Fix how savings are calculated before launch, for example against the last paid price or the median bid, and report them per category so finance accepts the number.",[132,133,134,135,136],"Automatic award only inside written value limits, categories and vendor rules, with everything else routed to a buyer","Every RFQ, bid, comparison and award stored with the version of the rules that applied","Vendor communication from approved templates, with no price or volume commitment made by the agent beyond the award rule","Sanctions, conflict of interest and vendor onboarding checks run before a vendor can win an award","A named buyer who owns each category and can switch the autonomous mode off","Buyers own the rules, review every recommendation above the award limit and sample automated awards each month. They handle vendor disputes, sole source requests and any request where the requirements are unclear, and decide when a category moves between recommend only and autonomous.",[139,140,141,142,143],"Share of sourcing events that run without a buyer touch, by category and value band","Days from request to award, split between automated and manual events","Bids received per RFQ and the share of RFQs with fewer than the required number","Price improvement against the baseline, with the calculation method stated","Awards a buyer reverses or a requester disputes",[145,148,151,154],{"title":146,"detail":147},"Savings that are really a lower baseline","Savings are measured against an inflated reference price, so the number looks large while the paid price barely moves. Fix the baseline method before launch and audit a sample.",{"title":149,"detail":150},"Cheapest bid wins a poor specification","The request was vague, vendors quoted different things, and the lowest price won. Check specification completeness at intake and send unclear requests to a buyer.",{"title":152,"detail":153},"Vendors stop answering","Automated requests arrive in volume and vendors learn that most are ignored or low value. Track answer rates, feedback to losing bidders and the number of repeat requests per vendor.",{"title":155,"detail":156},"Split purchases to dodge the limit","Requesters divide a large need into several small ones to stay under the automatic award limit. Monitor repeat requests for the same item or vendor and aggregate them.",{"euAiAct":158,"regulations":161,"guidance":166,"controls":179,"incidents":185},{"tier":159,"basis":160},"minimal","Sourcing indirect goods and services for the organization's own use is not listed in Annex III, and the system has no direct effect on individuals, so only AI literacy duties apply. Check the design if bidders are natural persons, for example sole traders, or if the agent produces any assessment of individual workers. Public bodies also remain bound by national public procurement rules on equal treatment and transparency, whichever tool runs the event.",[162,163,164,165],"eu-ai-act","gdpr","iso-42001","nist-ai-rmf",[167,173],{"title":168,"issuer":169,"region":170,"url":171,"note":172},"AI Risk Management Framework","National Institute of Standards and Technology","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary framework, with a generative AI profile, for governing and measuring the risks of an AI system such as an agent that commits the organization to purchases.",{"title":174,"issuer":175,"region":176,"url":177,"note":178},"PPN 017: Improving transparency of AI use in procurement","Cabinet Office","europe","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Fppn-017-improving-transparency-of-ai-use-in-procurement\u002Fppn-017-improving-transparency-of-ai-use-in-procurement-html","UK note for central government departments, their executive agencies and non departmental public bodies (other contracting authorities may apply it), mainly on suppliers' use of AI in tenders. It says decisions should be made with the support of AI systems and without reliance on them, and that authorities must not discriminate against suppliers when asking about AI use.",[180,181,182,183,184],"Written award rules with an owner, a version and a change log","Immutable record of each RFQ, bid, comparison and award","Separation of duties, so the agent cannot create a vendor, change bank details or approve its own limit","Monthly sample review of automated awards by a buyer who did not set the rules","Vendor communication logged and available for audit",[],{"howToBuild":187},"On Blits.ai this is an **agentic workflow** that a **REST API** call from the ERP or procurement\nsuite triggers when a requisition arrives, or that a schedule starts. **Custom functions** read the requisition and the supplier master from the ERP and write\nthe award back as a purchase order request, and a **knowledge base** holds the sourcing policy and\nthe category rules, retrieved with hybrid search so the agent cites the rule it applied. Vendor\nselection and bid comparison are steps of the workflow, using the agent's tools to read the\nquotes that come in through the **email channel**, and the **tool execution policy** limits what the\nagent may call.\n\n**Human in the loop confirmation** holds any award above the configured threshold for a buyer to\napprove or reject, and the same step covers requests the rules do not cover. **Guardrails** and\n**PII masking** apply to what vendors send and what the agent replies, **test suites** replay\npast sourcing events to compare the agent's choice with the buyer's before each rule change, and\n**monitors** run scheduled health checks on the agent, and **analytics** and the run history show\nrun volumes and failures. The run history keeps an audit trail per event, and the platform is model agnostic, so the model can differ per step.",[189,192,195,198],{"question":190,"answer":191},"How much of the sourcing workload can be automated?","It depends on the rules you allow. Fairmarkit reports that 31.8% of Snowflake's sourcing events now run autonomously, up nearly 15 times from the year before, and that bp sources $100M through automated processes with zero human intervention. The bp page describes automation and does not mention AI. Snowflake's page says it is expanding to more categories, so start with a recommend only phase and measure your own share.",{"question":193,"answer":194},"Is this the same as electronic sourcing software?","Electronic sourcing software gives buyers a place to run events. The autonomous version also writes and sends the request, compares the bids and, within limits, makes the award, so the buyer sets rules instead of running each event. Fairmarkit describes auto send and auto award as separate features at Snowflake, and Fairmarkit's case study on G42 describes an AI agent that creates the requisition, sources suppliers, gathers bids and awards contracts.",{"question":196,"answer":197},"Who is accountable when the agent awards to the wrong vendor?","The organization is, the same as for a buyer's decision. That is why automatic award sits inside written limits, why every award is logged with the rule version, and why a named buyer owns each category and a monthly sample of automated awards is reviewed by someone who did not set the rules.",{"question":199,"answer":200},"What do these programs report on cycle time?","Fairmarkit reports that Snowflake's average cycle fell from about 25 days to 6.4 days and that G42's fell from 5 to 7 days to 1 to 2 days. A bp director says cycle time is down by about half. All three come from Fairmarkit's own case study pages, and each organization counts cycle time its own way.",[202,203,204,205],"procurement-spend-classification","procurement-contract-review","supplier-invoice-processing","vendor-due-diligence","2026-10-13",[208],{"date":206,"note":209},"First published","autonomous-rfq-and-tail-spend-sourcing",[212,250,276,302],{"title":213,"useCases":214,"organization":215,"vendors":221,"summary":225,"stage":226,"year":227,"channels":228,"languages":230,"metrics":232,"outcomeDisclosed":241,"sources":242,"verification":245,"grade":247,"id":248,"organizationSlug":249},"G42: AI sourcing agent on Fairmarkit",[210],{"name":216,"anonymized":217,"country":218,"region":219,"industry":20,"wikidata":220},"G42",false,"AE","middle-east","Q98918715",[222],{"name":223,"role":224},"Fairmarkit","platform","G42's procurement team of two managers and four buyers had an average sourcing cycle of 5 to 7 days, with requesters unsure how to source what they needed. It adopted Fairmarkit, whose intake tool lets a requester photograph an item on a phone. Fairmarkit's case study says an AI agent then creates the requisition, sources suppliers, gathers bids and awards contracts, and reports that the average cycle fell to 1 to 2 days.","production",2025,[229,31],"mobile-app",[231],"en",[233],{"kpi":46,"value":234,"unit":235,"qualifier":236,"baseline":237,"claimant":238,"quote":239,"sourceUrl":240},75,"percent","exact","average procurement cycle of 5 to 7 days","vendor","75% Reduction in Cycle Times: The average procurement cycle went from 5-7 days to just 1-2 days.","https:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fg42",true,[243],{"url":240,"title":216,"publisher":223,"archivedUrl":244},"https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20251116124548\u002Fhttps:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fg42",{"level":246,"checkedAt":206},"source-verified","C","g42-fairmarkit-ai-sourcing-agent",null,{"title":251,"useCases":252,"organization":253,"vendors":256,"summary":258,"stage":226,"year":227,"channels":259,"languages":260,"metrics":261,"outcomeDisclosed":241,"sources":271,"verification":274,"grade":247,"id":275,"organizationSlug":249},"Snowflake: autonomous sourcing events with Fairmarkit",[210],{"name":254,"anonymized":217,"country":255,"region":170,"industry":20},"Snowflake","US",[257],{"name":223,"role":224},"Snowflake's procurement team of about 12 people sourced largely by email, with an average sourcing cycle of about 25 days. A new procurement leadership team switched on Fairmarkit, including its auto send and auto award features, an intake assistant and a scope of work generator. Fairmarkit reports the average cycle fell to 6.4 days and that 31.8% of sourcing events now run autonomously.",[30,31],[231],[262,268],{"kpi":45,"value":263,"unit":264,"qualifier":236,"baseline":265,"claimant":238,"quote":266,"sourceUrl":267},6.4,"days","about 25 days","Cycle times dropped dramatically, from 25 days to 6.4 days.","https:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fsnowflake",{"kpi":47,"value":269,"unit":235,"qualifier":236,"claimant":238,"quote":270,"sourceUrl":267},31.8,"31.8% of sourcing events now run autonomously through Fairmarkit, up nearly 15x from the previous year.",[272],{"url":267,"title":254,"publisher":223,"archivedUrl":273},"https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20250524165229\u002Fhttps:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fsnowflake",{"level":246,"checkedAt":206},"snowflake-fairmarkit-autonomous-sourcing",{"title":277,"useCases":278,"organization":279,"vendors":283,"summary":285,"stage":226,"year":286,"channels":287,"languages":288,"metrics":289,"outcomeDisclosed":241,"sources":297,"verification":300,"grade":247,"id":301,"organizationSlug":249},"bp: automated RFQ sourcing with Fairmarkit",[210],{"name":280,"anonymized":217,"country":281,"region":176,"industry":19,"wikidata":282},"bp","GB","Q152057",[284],{"name":223,"role":224},"bp's procurement team replaced a manual sourcing process of sending requirements to suppliers, collecting bids and selecting a vendor with Fairmarkit, after the organization shrank by 25% and still had to deliver the same volume of work. According to Fairmarkit's case study, bp put $1.2B of spend through the platform in 18 months and sources $100M through automated processes with zero human intervention. A bp director says cycle time has come down by about half.",2021,[30,31],[231],[290],{"kpi":46,"value":291,"unit":235,"qualifier":292,"baseline":293,"claimant":294,"quote":295,"sourceUrl":296},50,"approximately","a manual sourcing process","organization","So for example, we have taken our cycle time down by about half and what that means in real terms is our stakeholders get their work and their product and services about 50% quicker.","https:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fbp",[298],{"url":296,"title":280,"publisher":223,"archivedUrl":299},"https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20210923050012\u002Fhttps:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fbp",{"level":246,"checkedAt":206},"bp-fairmarkit-autonomous-sourcing",{"title":303,"useCases":304,"organization":305,"vendors":308,"summary":310,"stage":226,"year":311,"channels":312,"languages":313,"metrics":314,"outcomeDisclosed":241,"sources":315,"verification":320,"grade":247,"id":321,"organizationSlug":249},"MBTA: tail spend RFQs on Fairmarkit",[210],{"name":306,"anonymized":217,"country":255,"region":170,"industry":21,"wikidata":307},"Massachusetts Bay Transportation Authority (MBTA)","Q171985",[309],{"name":223,"role":224},"The Massachusetts Bay Transportation Authority moved its small purchases from a paper process to Fairmarkit in 2017. Buyers upload a request, the platform sends the RFQ to several vendors in seconds and lays the bids side by side, and the buyer chooses the award. Fairmarkit reports more than 14,000 RFQs and 36,000 bids since the start and $10M of cost avoidance. The MBTA's Chief Transformation Officer says he paid for the system in the first month.",2019,[30,31],[231],[],[316],{"url":317,"title":318,"publisher":223,"archivedUrl":319},"https:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fmbta","MBTA","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20191122015717\u002Fhttps:\u002F\u002Fwww.fairmarkit.com\u002Fcase-studies\u002Fmbta",{"level":246,"checkedAt":206},"mbta-fairmarkit-tail-spend-rfq",0,[324,332,337],{"kpi":46,"label":325,"unit":235,"aggregate":241,"higherIsBetter":241,"n":326,"nUpTo":322,"median":327,"min":291,"max":234,"byClaimant":328,"vendorOnly":217,"points":329},"Cycle time reduction",2,62.5,{"organization":67,"vendor":67,"regulator":322,"independent":322},[330,331],{"evidenceId":248,"organization":216,"value":234,"qualifier":236,"claimant":238,"grade":247,"pooled":241},{"evidenceId":301,"organization":280,"value":291,"qualifier":292,"claimant":294,"grade":247,"pooled":241},{"kpi":47,"label":333,"unit":235,"aggregate":241,"higherIsBetter":241,"n":67,"nUpTo":322,"median":269,"min":269,"max":269,"byClaimant":334,"vendorOnly":241,"points":335},"Automation rate",{"organization":322,"vendor":67,"regulator":322,"independent":322},[336],{"evidenceId":275,"organization":254,"value":269,"qualifier":236,"claimant":238,"grade":247,"pooled":241},{"kpi":45,"label":338,"unit":264,"aggregate":217,"higherIsBetter":217,"n":67,"nUpTo":322,"median":263,"min":263,"max":263,"byClaimant":339,"vendorOnly":241,"points":340},"Cycle time",{"organization":322,"vendor":67,"regulator":322,"independent":322},[341],{"evidenceId":275,"organization":254,"value":263,"qualifier":236,"claimant":238,"grade":247,"pooled":241},{"low":343,"high":344},60000,1098000,[346,368,391,412],{"slug":202,"title":347,"shortTitle":348,"definition":349,"status":9,"industries":350,"functions":354,"patterns":357,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":360,"publicEvidenceCount":360,"organizations":361,"bestGrade":366,"headline":249,"lastVerified":367,"indexable":241},"AI spend classification and spend analytics for procurement","Spend classification","Spend classification is AI that reads purchase orders, invoices, card transactions and contracts and assigns each line of spend to a category, for procurement teams. It assigns spend to the organization's taxonomy and to the right supplier, so procurement can see what is bought, from whom and where to consolidate or negotiate.",[18,351,352,353],"government","manufacturing","healthcare",[23,355,356],"finance-and-accounting","analytics-and-reporting",[358,27,359],"classification-and-routing","summarization",4,[362,363,364,365],"U.S. General Services Administration","Internal Revenue Service","U.S. Department of Agriculture","Veterans Health Administration","B","2026-09-27",{"slug":203,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":375,"patterns":377,"audience":379,"autonomy":380,"adoptionStage":35,"evidenceCount":381,"publicEvidenceCount":382,"organizations":383,"bestGrade":366,"headline":249,"lastVerified":367,"indexable":241},"AI assistant for procurement and supplier contract review","Procurement and contract review","Procurement and contract review is an assistant that reads supplier contracts and proposals, for procurement and vendor management teams. It 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.",[18,373,351,374,352],"banking","retail-and-ecommerce",[23,376,355],"legal",[27,378,28,26],"rag-knowledge-assistant","employee-facing","copilot",10,9,[384,385,386,363,387,388,389,390],"Bridgewater Associates","General Services Administration","Administration for Children and Families","Pension Benefit Guaranty Corporation","Pacific Northwest National Laboratory","Technology Common Services Center","Walmart",{"slug":204,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":396,"patterns":397,"audience":33,"autonomy":34,"adoptionStage":399,"segment":33,"evidenceCount":381,"publicEvidenceCount":382,"organizations":400,"bestGrade":366,"headline":410,"lastVerified":367,"indexable":241},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI supplier invoice processing is AI that captures supplier invoices in any format and extracts the header and line data. It matches invoices 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.",[18,373,351,374,19],[355,23],[27,26,398,358],"anomaly-detection","mainstream",[401,402,403,404,405,406,407,408,409],"Adyen","Charter Hall","Federal Deposit Insurance Corporation","Fugro","Georgetown University","Kingfisher","Octopus Energy","U.S. Immigration and Customs Enforcement","Veolia",{"kpi":47,"label":333,"unit":235,"n":360,"nUpTo":322,"kind":411,"value":75,"qualifier":236,"claimant":249,"organization":249,"vendorReported":217},"median",{"slug":205,"title":413,"shortTitle":414,"definition":415,"status":9,"industries":416,"functions":419,"patterns":422,"audience":379,"autonomy":380,"adoptionStage":35,"segment":423,"evidenceCount":424,"publicEvidenceCount":424,"organizations":425,"bestGrade":366,"headline":249,"lastVerified":367,"indexable":241},"AI for third party and vendor risk due diligence","Vendor due diligence","AI for vendor due diligence reviews a vendor's security questionnaires, SOC and assurance reports and contracts against an organization's control requirements, and researches the vendor for a risk team. It also checks model documentation, 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.",[18,373,417,351,418],"insurance","payments",[23,420,421],"risk-management","regulatory-compliance",[27,378,26,359],"second-line",5,[426,363,427,364,428],"U.S. Department of Justice","Northwell Health","U.S. Trade and Development Agency",{"indexable":241,"reasons":430},[],[432,438,443,450,455,462,468,475,483,490,497,503,509,516,523,530,536,542,549,554,559,566,572,579,584,589,594,599,606,612,618,623,629,635,641,646],{"id":162,"label":433,"issuer":434,"region":176,"url":435,"description":436,"useCases":437,"indexable":241},"EU AI Act","European Union","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":163,"label":439,"issuer":434,"region":176,"url":440,"description":441,"useCases":442,"indexable":241},"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":164,"label":444,"issuer":445,"region":446,"url":447,"description":448,"useCases":449,"indexable":241},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":165,"label":451,"issuer":452,"region":170,"url":171,"description":453,"useCases":454,"indexable":241},"NIST AI Risk Management Framework","NIST","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":456,"label":457,"issuer":458,"region":176,"url":459,"description":460,"useCases":461,"indexable":241},"uk-gdpr","UK GDPR","Information Commissioner's Office","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":463,"label":464,"issuer":434,"region":176,"url":465,"description":466,"useCases":467,"indexable":241},"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":469,"label":470,"issuer":471,"region":176,"url":472,"description":473,"useCases":474,"indexable":241},"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":476,"label":477,"issuer":478,"region":479,"url":480,"description":481,"useCases":482,"indexable":241},"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":484,"label":485,"issuer":486,"region":479,"url":487,"description":488,"useCases":489,"indexable":241},"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":491,"label":492,"issuer":493,"region":170,"url":494,"description":495,"useCases":496,"indexable":241},"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":498,"label":499,"issuer":434,"region":176,"url":500,"description":501,"useCases":502,"indexable":241},"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":504,"label":505,"issuer":506,"region":446,"url":507,"description":508,"useCases":502,"indexable":241},"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":510,"label":511,"issuer":512,"region":176,"url":513,"description":514,"useCases":515,"indexable":241},"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":517,"label":518,"issuer":519,"region":170,"url":520,"description":521,"useCases":522,"indexable":241},"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":524,"label":525,"issuer":526,"region":446,"url":527,"description":528,"useCases":529,"indexable":241},"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":531,"label":532,"issuer":434,"region":176,"url":533,"description":534,"useCases":535,"indexable":241},"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":537,"label":538,"issuer":539,"region":170,"url":540,"description":541,"useCases":535,"indexable":241},"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":543,"label":544,"issuer":545,"region":170,"url":546,"description":547,"useCases":548,"indexable":241},"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":550,"label":551,"issuer":434,"region":176,"url":552,"description":553,"useCases":548,"indexable":241},"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":555,"label":556,"issuer":434,"region":176,"url":557,"description":558,"useCases":548,"indexable":241},"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":560,"label":561,"issuer":562,"region":170,"url":563,"description":564,"useCases":565,"indexable":241},"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":567,"label":568,"issuer":569,"region":446,"url":570,"description":571,"useCases":565,"indexable":241},"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":573,"label":574,"issuer":575,"region":176,"url":576,"description":577,"useCases":578,"indexable":241},"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":580,"label":581,"issuer":434,"region":176,"url":582,"description":583,"useCases":578,"indexable":241},"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":585,"label":586,"issuer":434,"region":176,"url":587,"description":588,"useCases":578,"indexable":241},"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":590,"label":591,"issuer":478,"region":479,"url":592,"description":593,"useCases":381,"indexable":241},"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.",{"id":595,"label":596,"issuer":434,"region":176,"url":597,"description":598,"useCases":381,"indexable":241},"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":600,"label":601,"issuer":602,"region":170,"url":603,"description":604,"useCases":605,"indexable":241},"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.",8,{"id":607,"label":608,"issuer":434,"region":176,"url":609,"description":610,"useCases":611,"indexable":241},"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":613,"label":614,"issuer":615,"region":219,"url":616,"description":617,"useCases":424,"indexable":241},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":619,"label":620,"issuer":434,"region":176,"url":621,"description":622,"useCases":360,"indexable":241},"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":624,"label":625,"issuer":626,"region":176,"url":627,"description":628,"useCases":360,"indexable":241},"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":630,"label":631,"issuer":632,"region":176,"url":633,"description":634,"useCases":360,"indexable":241},"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":636,"label":637,"issuer":638,"region":479,"url":639,"description":640,"useCases":68,"indexable":241},"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":642,"label":643,"issuer":434,"region":176,"url":644,"description":645,"useCases":68,"indexable":241},"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":647,"label":648,"issuer":649,"region":170,"url":650,"description":651,"useCases":68,"indexable":241},"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.",1791904353243]