[{"data":1,"prerenderedAt":614},["ShallowReactive",2],{"uc-freight-dispatch-and-load-matching-agent":3,"uc-regulations":405},{"useCase":4,"evidence":194,"blitsAiDeployments":288,"benchmarks":289,"indicative":306,"related":309,"indexability":403,"includeUnpublished":200},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":26,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":53,"macroEstimates":95,"feasibility":96,"implementation":108,"risk":154,"blitsAi":173,"faq":175,"related":188,"datePublished":189,"dateModified":189,"lastVerified":189,"changelog":190,"slug":193},"AI agent for freight dispatch and load matching","Freight dispatch and load matching","AI load matching and dispatch for freight","AI agents read freight quote emails and match loads to carriers. C.H. Robinson reports a 40% productivity gain, Uber Freight 12% more bookings among active carriers.","published","An AI agent that does the coordination work behind moving a truckload: reading an emailed quote request and replying with a price, ranking which loads to show which carriers, matching pickup and delivery details to an open dock appointment slot, and chasing the exceptions, so a broker's or carrier's own staff plan lanes and handle disputes instead of typing quotes and making appointment calls.",[12,13,14,15,16],"AI freight broker automation","digital freight matching","AI dispatch optimization for trucking","automated freight quoting","AI load recommendation engine",[18],"logistics-and-transportation",[20],"operations",[22,23,24,25],"agentic-workflow","recommendation-and-personalization","document-processing","prediction-and-scoring",[27,28,29],"email","api","internal-tools","employee-facing","supervised-agent","early-adopters","freight-brokerage","Moving a truckload by road still runs on a lot of manual coordination: a shipper emails asking\nfor a price, a broker or carrier planner searches capacity and quotes back, a driver or carrier\nis matched to the load, and someone calls or emails the loading dock to book an appointment\nslot that works for both sides. C.H. Robinson, which describes its scale in the industry as\nunmatched, says it manages more than 37 million shipments a year, over 100,000 a day, across\ntruckload, less than truckload, ocean and air, for 75,000 customers, so even a small share of\nthat volume arriving as free text email adds up to a large manual workload.\n\nTwo things make the coordination hard to remove by force: freight requests do not arrive in a\nclean form (an email, a phone call, an EDI message with missing fields), and every match has\nreal constraints, capacity, lane preferences, dock hours, detention risk, that a spreadsheet or\na simple rules engine does not capture well. The work is also urgent and perishable: an unbooked\nload has to be rescheduled, which strains the relationship with the shipper, and a quote that\ntakes too long to arrive loses the business to a broker who answered first.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"C.H. Robinson says it manages more than 37 million shipments a year, over 100,000 daily, serving 75,000 customers globally across truckload, less than truckload, ocean and air transportation.","With AI, C.H. Robinson is the disruptor","https://www.chrobinson.com/en-us/about-us/newsroom/news/2026/ai-disruptor-ch-robinson/",2026,"1. **Read the request.** The agent classifies an incoming email, EDI message or API call: is\n   this a quote request, a booking, a change, a status question, and for which lane, mode and\n   equipment.\n2. **Price or match it.** For a quote, the agent prices the lane from current rates and\n   capacity; for a load, it scores which carriers or which loads best fit, using lane history,\n   equipment, prior bookings and stated preferences, not just who searched first.\n3. **Book the real world detail.** For an appointment, the agent matches the load's ready date\n   and cargo details to an open dock or delivery slot, confirms it in the transportation\n   management system, and tells both sides.\n4. **Watch the load and flag exceptions.** Once booked, the agent tracks the shipment against\n   the plan and raises appointment conflicts, missing paperwork or capacity gaps before they\n   become a missed pickup.\n5. **Escalate what needs judgment.** Unusual freight, rate disputes, a carrier that repeatedly\n   falls through, and any request outside the agent's price or capacity limits go to a human\n   planner or account manager with the context already gathered.",[43,44,45,46],"employee-productivity","cost-to-serve","speed","revenue-growth",[48,49,50,51,52],"productivity-gain","response-time-reduction","interactions-handled","conversion-rate-uplift","cost-reduction",{"referenceOrg":54,"inputs":55,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A freight broker handling 500,000 truckload shipments a year",[56,62,69,76,83],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"shipments","Truckload shipments per year",500000,"shipments per year","The reference broker.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"tasksPerShipment","Repetitive coordination tasks per shipment (quote, appointment, status update)",1.5,2.5,"tasks per shipment","Editorial assumption, replace with your own task counts per shipment.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"automationRate","Share of those tasks completed without a person",0.15,0.4,"fraction of tasks","Editorial assumption, replace with your own automation rate. The low end is close to C.H. Robinson's own primary source, which says it receives over 11,000 truckload pricing emails a day and automatically replies to 2,000 of them, about 18%; the high end covers a more mature, multi year deployment. The 40% figure on this page is a company wide productivity gain, not a task automation rate, and is not used to set this range.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"minutesSavedPerTask","Minutes saved per automated task",5,12,"minutes per task","Editorial assumption for a mix of quoting and appointment tasks; replace with your own time studies.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":89},"costPerHour","Fully loaded cost of a broker or dispatcher hour",25,45,"USD per hour","Editorial assumption for North America; replace with your own fully loaded cost.","shipments * tasksPerShipment * automationRate * minutesSavedPerTask / 60 * costPerHour","USD","per year","Coordinator time cost avoided","Gross time saved only. It leaves out the cost of building and running the AI and its integrations, the revenue effect of faster quotes and better load matching, and any change in detention, missed pickups or claims.",[],{"complexity":97,"complexityNote":98,"dataPrerequisites":99,"integrations":103},"medium","Reading a quote or a status question is the easy part; the work is in clean, current rate and capacity data, and in integrating with the transportation management system, the dock scheduling tool and whatever email or EDI gateway the shipper or carrier actually uses.",[100,101,102],"Historical lane, rate and booking data to train or tune a matching and pricing model","Current capacity, rates and dock appointment availability, reachable through an API, not a screen","A catalog of recurring email and EDI request types with their required fields",[104,105,106,107],"Transportation or freight management system (loads, rates, tenders)","Dock or carrier appointment scheduling system","Email and EDI ingestion and parsing","Track and trace or telematics feed for shipment status",{"steps":109,"guardrails":128,"humanInTheLoop":134,"kpisToInstrument":135,"failureModes":141},[110,113,116,119,122,125],{"title":111,"detail":112},"Automate the highest volume, lowest risk task first","Start with routine transactional quote replies or status questions on well understood lanes, where a wrong answer is cheap to correct, before automating anything that commits capacity or money.",{"title":114,"detail":115},"Keep pricing and matching inside a stated allow list","Define the rate bands, lanes and capacity the agent may quote or book on its own, and what it must not do (spot rates outside a margin band, new lanes, named accounts under contract review).",{"title":117,"detail":118},"Add appointment and exception handling once quoting is stable","Layer in dock appointment matching and shipment exception flags once the team trusts the quoting behaviour, so a wrong appointment does not compound a wrong quote.",{"title":120,"detail":121},"Confirm every automated action back to a person","Send the shipper, carrier or internal planner a clear confirmation of exactly what the agent quoted, matched or booked, so no one discovers the automation only when something goes wrong.",{"title":123,"detail":124},"Test before shippers and carriers do","Build a test set of real email and EDI requests per lane and exception type, including ambiguous or incomplete ones, and run it on every change to the pricing or matching logic.",{"title":126,"detail":127},"Instrument before you scale to more lanes or modes","Measure automation rate, error rate and booking conversion on the first lanes before widening to more freight modes or customer segments.",[129,130,131,132,133],"Price and capacity actions only within a stated rate, lane and margin allow list","Human review of any automated quote or match above a size, margin or risk threshold","A clear confirmation sent to the customer or carrier of exactly what the agent did","Immutable audit trail of every automated quote, match and appointment","Regular sampling of automated quotes and matches against a human reviewed sample","Planners and account managers own the exceptions: unusual freight, rate disputes, a carrier that repeatedly falls through, and any new lane, customer or task before it is added to the agent's allow list. They also review a sample of automated quotes and matches every week.",[136,137,138,139,140],"Share of quotes, matches and appointments completed without a person, per lane and task type","Booking or acceptance rate of automated quotes and recommended loads versus the prior process","Time from request to quote or to a booked appointment","Error rate on automated actions caught by manual audit or by a customer complaint","Handover rate and handover reasons",[142,145,148,151],{"title":143,"detail":144},"Quoting or matching on stale data","The agent prices a lane or matches capacity from rate or availability data that has already changed. Refresh rate and capacity data on a short cycle and refuse to quote when the data is stale.",{"title":146,"detail":147},"A confident wrong match","The agent books an appointment or recommends a load that does not actually fit the equipment, cargo or dock hours. Validate hard constraints (equipment type, hazmat, appointment windows) before committing, not only preference signals.",{"title":149,"detail":150},"Automation that erodes the relationship","A shipper or carrier who wanted to negotiate gets an automated reply instead. Give every automated quote and match an easy, visible way to reach a person.",{"title":152,"detail":153},"Scope creep into rate setting","New lanes or larger discretion are added to the agent's pricing allow list without a margin review. Treat every change to the allow list as a change with sign off.",{"euAiAct":155,"regulations":158,"guidance":161,"controls":168,"incidents":172},{"tier":156,"basis":157},"context-dependent","Article 50(1) applies whenever the agent interacts directly with a shipper or carrier, for example replying to a quote email or confirming an appointment: the recipient must be able to tell they are dealing with an AI system, unless this is obvious from the context. Article 50(2) is a separate duty on the provider: the generated quote or confirmation text itself must be marked in a machine readable format as artificially generated, and that duty does not apply only where the system performs an assistive function for standard editing or does not substantially alter input data the deployer supplied. Whether dispatch is high risk depends on who is being ranked. Matching freight capacity and pricing a quote for a shipper is not a listed Annex III use. But allocating loads or tasks based on an individual's behaviour in a work related relationship is Annex III point 4(b), so a deployment that ranks or assigns work to a named driver or owner operator based on their own behaviour, for example an asset carrier's employed drivers or a platform ranking owner operators on their clicks, saves and booking history, needs a fresh assessment against that point even though the reference design here scores capacity and price, not a person.",[159,160],"eu-ai-act","gdpr",[162],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Article 50(1): a system that interacts directly with a shipper or carrier must let them know they are dealing with AI, unless this is obvious from the context. Article 50(2) separately requires the provider to mark generated content, such as an automated quote or confirmation, as artificially generated in a machine readable format, an obligation that does not apply where the system only performs an assistive function for standard editing or does not substantially alter the deployer's input data.",[169,170,132,171],"Disclosure that a quote, match or confirmation may be automated, with an easy way to reach a person","Rate, lane and capacity allow list with human approval required above it","Regular sampling of automated actions against a human reviewed sample, with an owner for corrections",[],{"howToBuild":174},"On Blits.ai this is an **AI agent** with **custom functions** that call the broker's or\ncarrier's transportation management, rating and appointment scheduling systems as REST calls,\neach scoped to one action (get a rate, check capacity, book a slot). Quote replies and\nappointment confirmations run as an **agentic workflow** with **human in the loop approval**\nabove a stated margin or risk threshold, so a quote is only sent automatically inside the\nallow list; a **knowledge base** with lane notes and exception procedures, retrieved with\nhybrid search, answers open questions.\n\nThe **email channel** ingests and classifies incoming quote requests and status questions, and\nthe same agent can serve **Microsoft Teams** or **Slack** for internal planners, or the\n**REST or WebSocket API** for a carrier or shipper portal. **Guardrails** check input and\noutput before a price or a commitment goes out, and **human handover** passes an exception to\na planner with the load and conversation context attached. **Test suites** run regression\nconversations per lane and exception type on every change; **monitors** run scheduled health\nchecks against the agent with alerts on failure, and **analytics** show interactions,\nrecognition rate and satisfaction broken down by channel. The platform is model agnostic, so\na broker can route pricing decisions through a different model than open ended questions.",[176,179,182,185],{"question":177,"answer":178},"Can an AI agent negotiate freight rates on its own?","Keep automated quotes inside a stated rate and margin band, and hand anything that needs negotiation, an unusual lane or a large account to a human. C.H. Robinson, for example, describes generative AI reading a transactional truckload quote email and supplying the price from its Dynamic Pricing Engine, not negotiating contract rates.",{"question":180,"answer":181},"How is this different from a transportation management system (TMS)?","The TMS stays the system of record for loads, rates and appointments. The AI agent reads unstructured requests, such as an email or an EDI message with missing fields, decides what is being asked, and calls the TMS and scheduling systems to act, instead of a person doing that translation by hand.",{"question":183,"answer":184},"What results have freight companies reported from this kind of AI?","C.H. Robinson reports Lean AI increased its productivity by more than 40% since 2022, and in 2024 C.H. Robinson said its AI replied to 2,000 quote requests a day. Uber Freight reported in 2023 that a recommendations system lifted bookings by 12% for active carrier users in an A/B test. J.B. Hunt has put agentic AI from Overroute to work across all of its business units on millions of loads, though it has not disclosed a percentage outcome yet.",{"question":186,"answer":187},"What should stay with a human planner?","Unusual or high value freight, rate disputes, a carrier that repeatedly falls through, and any lane, customer or task outside what the agent's allow list already covers.",[],"2026-09-28",[191],{"date":189,"note":192},"First published","freight-dispatch-and-load-matching-agent",[195,244,264],{"title":196,"useCases":197,"organization":198,"vendors":203,"summary":206,"stage":207,"year":40,"channels":208,"languages":209,"metrics":211,"outcomeDisclosed":226,"sources":227,"verification":239,"grade":241,"id":242,"organizationSlug":243},"C.H. Robinson: Lean AI across the shipment lifecycle",[193],{"name":199,"anonymized":200,"country":201,"region":202,"industry":18},"C.H. Robinson",false,"US","north-america",[204],{"name":199,"role":205},"in-house","C.H. Robinson, a global freight broker and third party logistics provider, says it has applied what it calls Lean AI across the shipment lifecycle since 2022: pricing, order entry, capacity sourcing, pickup and delivery appointments, freight tracking, document handling and invoicing. Generative AI reads incoming emails, classifies them and, for a truckload quote request, replies with a price; a separate system matches load and dock details to book touchless pickup and delivery appointments. The company reports the approach has automated millions of shipping tasks and raised productivity, while continuing to provide premium service.","scaled",[28,29,27],[210],"en",[212,219],{"kpi":48,"value":213,"unit":214,"qualifier":215,"period":216,"claimant":217,"quote":218,"sourceUrl":39},40,"percent","at-least","since 2022","organization","Lean AI has increased C.H. Robinson's productivity by more than 40%, automated millions of shipping tasks, saved thousands of hours of work per day and lowered its cost to serve, while continuing to provide premium service.",{"kpi":50,"value":220,"unit":221,"qualifier":222,"period":223,"claimant":217,"quote":224,"sourceUrl":225},2000,"count","exact","per day","While the technology is replying to 2,000 customer quote requests a day, it opens the door to automating other transactions shippers and carriers choose to do by email.","https://investor.chrobinson.com/News-and-Events/Press-Releases/press-release-details/2024/New-C.H.-Robinson-Technology-Breaks-a-Decades-Old-Barrier-to-Automation-in-the-Logistics-Industry/default.aspx",true,[228,230,234],{"url":39,"title":38,"publisher":199,"date":229},"2026-02-13",{"url":225,"title":231,"publisher":199,"date":232,"archivedUrl":233},"New C.H. Robinson Technology Breaks a Decades-Old Barrier to Automation in the Logistics Industry","2024-05-07","https://web.archive.org/web/20241016003806/https://investor.chrobinson.com/News-and-Events/Press-Releases/press-release-details/2024/New-C.H.-Robinson-Technology-Breaks-a-Decades-Old-Barrier-to-Automation-in-the-Logistics-Industry/default.aspx",{"url":235,"title":236,"publisher":237,"date":238},"https://www.truckingdive.com/news/ch-robinson-ai-automate-emailed-price-quotes-touchless-appointments/714925/","How CH Robinson is using AI to automate logistics tasks","Trucking Dive","2024-05-08",{"level":240,"checkedAt":189},"source-verified","B","ch-robinson-lean-ai-freight-automation",null,{"title":245,"useCases":246,"organization":247,"vendors":249,"summary":253,"stage":254,"year":40,"channels":255,"languages":256,"metrics":257,"outcomeDisclosed":200,"sources":258,"verification":262,"grade":241,"id":263,"organizationSlug":243},"J.B. Hunt: agentic AI for freight execution with Overroute",[193],{"name":248,"anonymized":200,"country":201,"region":202,"industry":18},"J.B. Hunt Transport Services",[250],{"name":251,"role":252},"Overroute","platform","J.B. Hunt spent a year working with Overroute to design an agentic AI platform for freight execution, and then put its AI agents to work across all of J.B. Hunt's business units, on millions of loads. The agents work inside operators' existing systems to automate the coordination work behind every load: they read live data, surface exceptions, and support operators in managing customer communications, rather than replacing the operators' tools.","production",[29,28],[210],[],[259],{"url":260,"title":261,"publisher":248},"https://www.jbhunt.com/our-company/newsroom/2026/07/overroute-launches-to-streamline-freight","Overroute Launches with J.B. Hunt To Streamline Freight Execution for Enterprise Carriers",{"level":240,"checkedAt":189},"jb-hunt-overroute-agentic-freight-execution",{"title":265,"useCases":266,"organization":267,"vendors":269,"summary":271,"stage":272,"year":273,"channels":274,"languages":276,"metrics":277,"outcomeDisclosed":226,"sources":282,"verification":286,"grade":241,"id":287,"organizationSlug":243},"Uber Freight: AI load recommendations for carriers",[193],{"name":268,"anonymized":200,"country":201,"region":202,"industry":18},"Uber Freight",[270],{"name":268,"role":205},"Uber Freight replaced the default search page in its digital freight brokerage with a recommendations system that ranks loads for each carrier, using candidate generation from saved, clicked and previously booked loads and an XGBoost ranking model with lead time, repeat lane and distance preference as features, instead of leaving carriers to search for loads themselves.","pilot",2023,[275],"mobile-app",[210],[278],{"kpi":51,"value":80,"unit":214,"qualifier":222,"baseline":279,"claimant":217,"quote":280,"sourceUrl":281},"Default search page, same carrier population, active users segment only","We tested our new recommendations system with a user-level A/B experiment, to positive results. Lifts occurred throughout the carrier booking funnel, most notably an increase of 12% in bookings for active users and an overall increase of 3% in bookings and 5% in clicks.","https://www.uberfreight.com/en-US/blog/better-load-matching-with-ai",[283],{"url":281,"title":284,"publisher":268,"date":285},"Achieving Better Load Matching with AI","2023-09-24",{"level":240,"checkedAt":189},"uber-freight-ai-load-recommendations",0,[290,296,301],{"kpi":51,"label":291,"unit":214,"aggregate":226,"higherIsBetter":226,"n":292,"nUpTo":288,"median":80,"min":80,"max":80,"byClaimant":293,"vendorOnly":200,"points":294},"Conversion uplift",1,{"organization":292,"vendor":288,"regulator":288,"independent":288},[295],{"evidenceId":287,"organization":268,"value":80,"qualifier":222,"claimant":217,"grade":241,"pooled":226},{"kpi":50,"label":297,"unit":221,"aggregate":200,"higherIsBetter":226,"n":292,"nUpTo":288,"median":220,"min":220,"max":220,"byClaimant":298,"vendorOnly":200,"points":299},"Interactions handled",{"organization":292,"vendor":288,"regulator":288,"independent":288},[300],{"evidenceId":242,"organization":199,"value":220,"qualifier":222,"claimant":217,"grade":241,"pooled":226},{"kpi":48,"label":302,"unit":214,"aggregate":226,"higherIsBetter":226,"n":292,"nUpTo":288,"median":213,"min":213,"max":213,"byClaimant":303,"vendorOnly":200,"points":304},"Productivity gain",{"organization":292,"vendor":288,"regulator":288,"independent":288},[305],{"evidenceId":242,"organization":199,"value":213,"qualifier":215,"claimant":217,"grade":241,"pooled":226},{"low":307,"high":308},234375,4500000,[310,340,363,382],{"slug":311,"title":312,"shortTitle":313,"definition":314,"status":9,"industries":315,"functions":317,"patterns":319,"audience":322,"autonomy":31,"adoptionStage":32,"segment":318,"evidenceCount":323,"publicEvidenceCount":324,"organizations":325,"bestGrade":241,"headline":333,"lastVerified":339,"indexable":226},"claims-triage-and-straight-through-processing","AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[316],"insurance",[318,20],"claims",[320,25,24,22,321],"classification-and-routing","summarization","back-office",9,7,[326,327,328,329,330,331,332],"Admiral Seguros","Allianz Partners","Hiscox","Lemonade","Sedgwick","Tokio Marine & Nichido Fire Insurance","Travelers",{"kpi":334,"label":335,"unit":214,"n":292,"nUpTo":292,"kind":336,"value":337,"qualifier":338,"claimant":217,"organization":329,"vendorReported":200},"automation-rate","Automation rate","reported",55,"approximately","2026-09-26",{"slug":341,"title":342,"shortTitle":343,"definition":344,"status":9,"industries":345,"functions":346,"patterns":348,"audience":322,"autonomy":31,"adoptionStage":32,"segment":347,"evidenceCount":323,"publicEvidenceCount":323,"organizations":349,"bestGrade":241,"headline":358,"lastVerified":362,"indexable":226},"commercial-underwriting-submission-triage","AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI that reads incoming broker submissions for commercial insurance (emails, applications, schedules of values, loss runs and supplements), extracts the risk data into a structured record, checks clearance and appetite, enriches the risk with internal and third party data and ranks it, so underwriters open a complete, prioritized file instead of an inbox.",[316],[347,20],"underwriting",[24,320,25,22],[350,351,352,353,328,354,355,356,357],"American International Group","AXIS Capital","CNA Financial","Generali Global Corporate & Commercial","Kinsale Capital Group","Markel","Paragon Insurance Group","Skyward Specialty Insurance Group",{"kpi":359,"label":360,"unit":214,"n":292,"nUpTo":288,"kind":336,"value":361,"qualifier":338,"claimant":217,"organization":356,"vendorReported":200},"accuracy","Accuracy",98,"2026-09-27",{"slug":364,"title":365,"shortTitle":366,"definition":367,"status":9,"industries":368,"functions":371,"patterns":373,"audience":322,"autonomy":31,"adoptionStage":32,"evidenceCount":375,"publicEvidenceCount":375,"organizations":376,"bestGrade":241,"headline":380,"lastVerified":362,"indexable":226},"customs-classification-and-declaration","AI for customs classification and declaration preparation","Customs classification and declarations","AI that reads what is being shipped (the commercial invoice, the product data and sometimes a photo), proposes the tariff classification code with its reasoning and a confidence score, drafts the customs declaration with value, origin and parties, and sends only uncertain or high risk entries to a licensed customs specialist before filing.",[18,369,370],"retail-and-ecommerce","cross-industry",[20,372],"regulatory-compliance",[320,24,22,374],"computer-vision",3,[377,378,379],"DHL Express","United Parcel Service","ZLS Zoll und Logistikservice GmbH",{"kpi":334,"label":335,"unit":214,"n":292,"nUpTo":288,"kind":336,"value":381,"qualifier":222,"claimant":217,"organization":378,"vendorReported":200},90,{"slug":383,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":390,"patterns":393,"audience":396,"autonomy":31,"adoptionStage":32,"segment":397,"evidenceCount":398,"publicEvidenceCount":375,"organizations":399,"bestGrade":241,"headline":243,"lastVerified":362,"indexable":226},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[388,389],"banking","payments",[391,392,20],"customer-service","fraud-prevention",[394,395,320,24,22],"conversational-agent","voice-agent","customer-facing","front-office",4,[400,401,402],"Commonwealth Bank of Australia","Klarna","Visa",{"indexable":226,"reasons":404},[],[406,410,415,423,430,436,443,450,458,464,471,477,484,491,497,502,509,514,520,526,532,538,544,549,554,560,567,572,578,585,591,597,603,608],{"id":159,"label":407,"issuer":164,"region":165,"url":166,"description":408,"useCases":409,"indexable":226},"EU AI Act","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":160,"label":411,"issuer":164,"region":165,"url":412,"description":413,"useCases":414,"indexable":226},"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":416,"label":417,"issuer":418,"region":419,"url":420,"description":421,"useCases":422,"indexable":226},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":424,"label":425,"issuer":426,"region":202,"url":427,"description":428,"useCases":429,"indexable":226},"nist-ai-rmf","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":431,"label":432,"issuer":164,"region":165,"url":433,"description":434,"useCases":435,"indexable":226},"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":437,"label":438,"issuer":439,"region":165,"url":440,"description":441,"useCases":442,"indexable":226},"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":444,"label":445,"issuer":446,"region":165,"url":447,"description":448,"useCases":449,"indexable":226},"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":451,"label":452,"issuer":453,"region":454,"url":455,"description":456,"useCases":457,"indexable":226},"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":459,"label":460,"issuer":461,"region":454,"url":462,"description":463,"useCases":86,"indexable":226},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":465,"label":466,"issuer":467,"region":419,"url":468,"description":469,"useCases":470,"indexable":226},"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":472,"label":473,"issuer":474,"region":202,"url":475,"description":476,"useCases":470,"indexable":226},"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":478,"label":479,"issuer":480,"region":165,"url":481,"description":482,"useCases":483,"indexable":226},"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":485,"label":486,"issuer":487,"region":419,"url":488,"description":489,"useCases":490,"indexable":226},"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":492,"label":493,"issuer":164,"region":165,"url":494,"description":495,"useCases":496,"indexable":226},"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":498,"label":499,"issuer":164,"region":165,"url":500,"description":501,"useCases":496,"indexable":226},"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":503,"label":504,"issuer":505,"region":202,"url":506,"description":507,"useCases":508,"indexable":226},"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":510,"label":511,"issuer":164,"region":165,"url":512,"description":513,"useCases":80,"indexable":226},"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":515,"label":516,"issuer":517,"region":202,"url":518,"description":519,"useCases":80,"indexable":226},"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":521,"label":522,"issuer":523,"region":419,"url":524,"description":525,"useCases":80,"indexable":226},"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":527,"label":528,"issuer":164,"region":165,"url":529,"description":530,"useCases":531,"indexable":226},"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":533,"label":534,"issuer":535,"region":202,"url":536,"description":537,"useCases":531,"indexable":226},"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":539,"label":540,"issuer":453,"region":454,"url":541,"description":542,"useCases":543,"indexable":226},"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":545,"label":546,"issuer":164,"region":165,"url":547,"description":548,"useCases":543,"indexable":226},"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":550,"label":551,"issuer":164,"region":165,"url":552,"description":553,"useCases":543,"indexable":226},"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":555,"label":556,"issuer":557,"region":165,"url":558,"description":559,"useCases":323,"indexable":226},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":561,"label":562,"issuer":563,"region":202,"url":564,"description":565,"useCases":566,"indexable":226},"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":568,"label":569,"issuer":164,"region":165,"url":570,"description":571,"useCases":566,"indexable":226},"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":573,"label":574,"issuer":164,"region":165,"url":575,"description":576,"useCases":577,"indexable":226},"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":579,"label":580,"issuer":581,"region":582,"url":583,"description":584,"useCases":79,"indexable":226},"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":586,"label":587,"issuer":588,"region":165,"url":589,"description":590,"useCases":398,"indexable":226},"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":592,"label":593,"issuer":594,"region":165,"url":595,"description":596,"useCases":398,"indexable":226},"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":598,"label":599,"issuer":600,"region":454,"url":601,"description":602,"useCases":375,"indexable":226},"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":604,"label":605,"issuer":164,"region":165,"url":606,"description":607,"useCases":375,"indexable":226},"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":609,"label":610,"issuer":611,"region":202,"url":612,"description":613,"useCases":375,"indexable":226},"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.",1790598295112]