[{"data":1,"prerenderedAt":705},["ShallowReactive",2],{"uc-correspondence-triage-and-routing":3,"uc-regulations":500},{"useCase":4,"evidence":185,"blitsAiDeployments":363,"benchmarks":364,"indicative":383,"related":386,"indexability":498,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":21,"patterns":25,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":99,"risk":137,"blitsAi":161,"faq":163,"related":173,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI correspondence triage for banks and insurers","AI sorts inbound letters and emails and routes each to the right team. Travelers reached 91% accuracy in testing; the VA ingests up to 40,000 packets a day.","published","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[12,13,14,15],"intelligent mailroom","email triage AI","inbound document classification","digital mailroom automation",[17,18,19,20],"cross-industry","banking","insurance","government",[22,23,24],"operations","customer-service","case-management",[26,27,28],"classification-and-routing","document-processing","summarization",[30,31,32],"email","internal-tools","api","back-office","supervised-agent","mainstream","Banks, insurers and public bodies still receive a large share of their work as unstructured\ncorrespondence: scanned letters, emails with attachments, portal uploads and secure messages.\nSomeone has to open each item, decide what it is (a complaint, a power of attorney, a\nbereavement notice, a change of address, a payment instruction), find the customer and send it\nto the right queue. That sorting step adds delay before anyone starts the real work.\n\nManual sorting is also where risk hides. A complaint filed as a general enquiry can miss its\nregulatory deadline, a bereavement letter can sit in the wrong queue, and a fraud warning can be\nread days late. Rule based keyword routing helps with obvious cases but struggles with free text\nand mixed documents.",[],"1. **One intake.** Post is scanned; emails, uploads and secure messages land in the same queue\n   with their attachments.\n2. **Classify.** The AI identifies the document or request type, the language and any urgency\n   signal (complaint, vulnerability, fraud, legal deadline), with a confidence score.\n3. **Extract and link.** It extracts the key fields (names, account numbers, dates, amounts,\n   reference numbers) and matches the item to the customer and account in the core systems.\n4. **Route or trigger.** It sets the priority and service level, routes the item to the right\n   team, or starts the downstream workflow directly (for example an address change or a\n   bereavement case), with a short summary for the receiving officer.\n5. **Fall back to people.** Low confidence items, unmatched customers and anything sensitive go to\n   a human review queue, and every correction becomes training data.",[40,41,42,43],"cost-to-serve","speed","compliance","employee-productivity",[45,46,47,48,49],"automation-rate","processing-time-reduction","accuracy","hours-saved","interactions-handled",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A bank or insurer that receives 1 million inbound letters and emails a year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"items","Inbound items per year",1000000,"items per year","The reference organization. Replace with your own mailroom and mailbox volume.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"minutesPerItem","Minutes to read, classify, index and route one item manually",2,4,"minutes per item","Editorial assumption, replace with your own time study.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"automatedShare","Share of items routed without human touch",0.5,0.8,"fraction of items","Editorial assumption, replace with your own. None of the evidence on this page reports a share of correspondence routed with no human touch; measure it on your own labelled sample before relying on it.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerHour","Fully loaded cost per hour of intake staff",30,50,"USD per hour","Editorial assumption, replace with your own.","items * minutesPerItem / 60 * automatedShare * costPerHour","USD","per year","Manual sorting effort avoided","Sorting labour only. It leaves out the value of faster downstream handling, fewer missed complaint deadlines and the cost of scanning, the platform and integration.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"medium","Classification and extraction are mature. The effort is in the taxonomy of request types, the customer matching against core data and the connections to every downstream queue.",[90,91,92],"A labelled sample of historical correspondence per request type","An agreed taxonomy of request types with owner teams and service levels","Customer and account data reachable for matching",[94,95,96,97,98],"Scanning and mailroom capture","Shared mailboxes and secure messaging","Core banking or policy administration for customer matching","Case management and workflow tools for routing","Complaint management system",{"steps":100,"guardrails":116,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":127},[101,104,107,110,113],{"title":102,"detail":103},"Build the request taxonomy with the receiving teams","List the request types, their owner team, priority and service level. Keep it short at first; a catch all class routed to people is better than twenty rare classes.",{"title":105,"detail":106},"Label a real sample","Label a few thousand recent items, including the messy ones (handwritten, multi topic, forwarded chains), and use them as the test set for every model change.",{"title":108,"detail":109},"Start with classification and summaries only","Let the AI propose the class and a summary while people still route. Measure accuracy per class before switching routing on.",{"title":111,"detail":112},"Route automatically per class above a confidence threshold","Switch on automatic routing class by class, with a review queue below the threshold and mandatory human review for complaints, vulnerability and legal documents.",{"title":114,"detail":115},"Trigger downstream workflows","For simple requests, start the fulfilment workflow directly from the extracted fields instead of dropping a task in a queue.",[117,118,119,120],"Complaints, vulnerability signals and fraud warnings are always flagged and never auto closed","Items below the confidence threshold go to a human review queue","Customer matching requires at least two strong identifiers before an item is linked","Personal data in documents is masked in logs and model prompts","People review low confidence items and every item flagged as a complaint, vulnerability or legal matter. A quality team samples automatically routed items weekly and feeds corrections back into the labelled set.",[123,68,124,125,126],"Classification accuracy per class on a weekly sample","Time from receipt to arrival in the right queue","Misrouted items reported by receiving teams","Complaints identified at intake versus later",[128,131,134],{"title":129,"detail":130},"A complaint routed as an enquiry","The regulatory clock runs while the item sits in the wrong queue. Treat complaint detection as its own high recall check.",{"title":132,"detail":133},"Linked to the wrong customer","A document attached to the wrong account is a data breach. Require strong identifiers and route ambiguous matches to people.",{"title":135,"detail":136},"Taxonomy drift","New products and campaigns create request types the model never saw. Review the catch all class monthly.",{"euAiAct":138,"regulations":141,"guidance":148,"controls":155,"incidents":160},{"tier":139,"basis":140},"context-dependent","It depends on where the system runs. Classifying and routing a bank's or insurer's correspondence is not a use listed in Annex III, so it is minimal risk: the AI literacy duty of Article 4 applies, and the Article 50 duty to tell people they are dealing with AI does not, because the system does not interact with the sender. Used by or for a public authority in a benefits process covered by Annex III point 5(a), the provider can treat it as not high risk only while it performs a narrow procedural or preparatory task under Article 6(3); the provider must then document that assessment before it goes live (Article 6(4)) and register the system in the EU database (Article 49(2)). If the system evaluates eligibility for benefits or profiles the people who write in, it is high risk, so those judgements stay with people.",[142,143,144,145,146,147],"eu-ai-act","gdpr","uk-gdpr","dora","uk-consumer-duty","apra-cps-230",[149],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"DISP 1.6 Complaints time limit rules","Financial Conduct Authority","europe","https://www.handbook.fca.org.uk/handbook/DISP/1/6.html","The response deadlines (eight weeks for most complaints, 15 business days for payment services and electronic money complaints) run from the firm's receipt of the complaint, so intake must recognise complaints wherever they arrive.",[156,157,158,159],"Misclassification rate tracked as a model health metric, per class","Log of every classification, extracted field and routing decision","Separate high recall check for complaints and vulnerability","Retention and access controls on scanned documents in line with the records policy",[],{"howToBuild":162},"On Blits.ai the **email channel** and the REST API take inbound items into an **agentic\nworkflow**. An **AI agent** with **structured output** returns the class, the extracted fields\nand a summary for each item, and **custom functions** look up the customer and create the task\nor case in the downstream system. Scanned post enters through the same API once the capture\nsystem has turned it into text.\n\nRules in **flows** force complaints and vulnerability signals to a person, a **human review step**\nin the workflow takes low confidence items, and **PII\nmasking** keeps account and card numbers out of model prompts. **Test suites** run the labelled\nset on every change, and the workflow run history keeps an audit trail of every classification.\nData can stay in the EU or UAE region.",[164,167,170],{"question":165,"answer":166},"How accurate is AI at classifying inbound correspondence?","Good enough to route most items, not all. In a post written by AWS and Travelers staff, the Travelers policy service email classifier reached 68% accuracy in initial testing and 91% after prompt engineering and condensed categories; the ground truth set had over 4,000 labelled emails in 13 classes. These are test results, not production figures, which is why the design keeps a human review queue below a confidence threshold.",{"question":168,"answer":169},"Does this work for scanned paper as well as email?","Yes, once the capture step has turned the scan into text. The US Department of Veterans Affairs reports that its Mail Automation Services platform combines OCR, handwriting recognition and language processing, ingests 25,000 to 40,000 packets a day, mostly from its Centralized Mail Portal, and sends each submission to the right business line.",{"question":171,"answer":172},"What should never be routed automatically?","Complaints, signs of customer vulnerability, fraud warnings and legal documents such as court orders and powers of attorney should always be flagged for a person, even when the classifier is confident.",[174,175,176,177,178,179],"email-and-ticket-reply-drafting","intelligent-document-processing","complaints-handling-agent","account-servicing-execution","outbound-notice-drafting","payment-investigations-and-exceptions","2026-09-27",[182],{"date":180,"note":183},"First published","correspondence-triage-and-routing",[186,229,256,282,313,342],{"title":187,"useCases":188,"organization":189,"vendors":194,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":215,"sources":216,"verification":224,"grade":226,"id":227,"organizationSlug":228},"US Department of Veterans Affairs: Mail Automation Services for claims intake",[184],{"name":190,"anonymized":191,"country":192,"region":193,"industry":20},"U.S. Department of Veterans Affairs",false,"US","north-america",[195,198],{"name":196,"role":197},"IBM","integrator",{"name":190,"role":199},"in-house","The Veterans Benefits Administration runs Mail Automation Services, an intake platform for claims material and other submissions from veterans and their representatives, mostly arriving through the Centralized Mail Portal. It combines form recognition, OCR, handwriting recognition and natural language processing to extract an average of 95 fields from more than 1,500 form layouts and to establish the claim and send each submission to the right business line. The agency reports that it ingests 25,000 to 40,000 packets a day; it has been in operation since 2020.","scaled",2020,[32,31],[205],"en",[207],{"kpi":49,"value":208,"unit":209,"qualifier":210,"period":211,"claimant":212,"quote":213,"sourceUrl":214},25000,"count","at-least","packets per day (25,000 to 40,000)","organization","The platform ingests and reviews 25k to 40k packets of information per day, primarily derived from the Centralized Mail Portal.","https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv",true,[217,221],{"url":214,"title":218,"publisher":219,"date":220},"2024 consolidated AI use case inventory (raw data, version 2)","Office of Management and Budget (GitHub)","2025-01-23",{"url":222,"title":223,"publisher":219},"https://github.com/ombegov/2024-Federal-AI-Use-Case-Inventory","2024 Federal Agency AI Use Case Inventory",{"level":225,"checkedAt":180},"source-verified","B","us-department-of-veterans-affairs-mail-automation-services","u-s-department-of-veterans-affairs",{"title":230,"useCases":231,"organization":232,"vendors":235,"summary":239,"stage":240,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":215,"sources":245,"verification":251,"grade":253,"id":254,"organizationSlug":255},"Loadsure: document classification and extraction for cargo insurance claims",[184],{"name":233,"anonymized":191,"country":234,"region":152,"industry":19},"Loadsure","GB",[236],{"name":237,"role":238},"Google Cloud","platform","Loadsure, a London based insurtech for freight insurance, automated the intake of claim documents such as bills of lading, invoices and shipping documents with Google Cloud Document AI. Each incoming document is first classified and then sent to an extractor built for its type, which feeds the claims verification process; Gemini was later used for a similar extraction workflow elsewhere in the business. The blog post, written by Google Cloud and Loadsure staff, says work that took 30 to 60 minutes per claim now happens in near real time.","production",2024,[32],[205],[],[246],{"url":247,"title":248,"publisher":249,"date":250},"https://cloud.google.com/blog/topics/financial-services/loadsure-data-drive-insurance-claims-AI-eliminates-manual-processing","Can AI eliminate manual processing for insurance claims? Loadsure built a solution to find out","Google Cloud Blog","2024-11-05",{"level":225,"checkedAt":252},"2026-09-26","C","loadsure-claims-document-classification",null,{"title":257,"useCases":258,"organization":259,"vendors":261,"summary":264,"stage":201,"year":265,"channels":266,"languages":267,"metrics":268,"outcomeDisclosed":215,"sources":277,"verification":280,"grade":253,"id":281,"organizationSlug":255},"Encova Insurance: AI document understanding for claims invoice intake",[184],{"name":260,"anonymized":191,"country":192,"region":193,"industry":19},"Encova Insurance",[262],{"name":263,"role":238},"UiPath","Encova Insurance, a US mutual insurer, replaced traditional OCR with UiPath Document Understanding in its claims invoice process, so incoming invoices are read, their data extracted and passed to automated processing, with exceptions fixed by staff in UiPath Action Center. Its solution architect says that traditional OCR got 40% of documents through without issues and that the new process has a 99% success rate, documents processed through without issues, which the vendor headline separately calls 99% accuracy. The vendor also reports that manual data entry time in the wider policy intake automation programme was cut by over 99% over the year.",2023,[31,32],[205],[269],{"kpi":45,"value":270,"unit":271,"qualifier":272,"period":273,"baseline":274,"claimant":212,"quote":275,"sourceUrl":276},99,"percent","exact","document understanding success rate on claims invoices, processed through without issues","40% of documents through without issues with traditional OCR","With this new [UiPath] process, the success rate is 99%.","https://www.uipath.com/resources/automation-case-studies/encova-insurance-scales-automation",[278],{"url":276,"title":279,"publisher":263},"Encova Insurance Scales Automation",{"level":225,"checkedAt":180},"encova-insurance-document-intake-automation",{"title":283,"useCases":284,"organization":285,"vendors":289,"summary":292,"stage":201,"year":265,"channels":293,"languages":294,"metrics":296,"outcomeDisclosed":215,"sources":308,"verification":311,"grade":253,"id":312,"organizationSlug":255},"Master Trust Bank of Japan: AI data capture for inbound financial documents",[184],{"name":286,"anonymized":191,"country":287,"region":288,"industry":18},"The Master Trust Bank of Japan","JP","asia-pacific",[290],{"name":291,"role":238},"Rossum","The Master Trust Bank of Japan, a trust bank specialising in asset servicing, uses Rossum's intelligent document processing to read inbound Japanese financial documents such as trade instructions, dividend notices and tax returns, and pass the extracted data to its own robotic process automation. The deployment grew to more than 90 document types used by 10 to 15 departments and 100,000 documents a year. The vendor reports that manual workload fell by 75% and that full processing now takes under 10 minutes instead of up to 1.5 hours per document.",[31,32],[295],"ja",[297,304],{"kpi":298,"value":299,"unit":271,"qualifier":272,"period":300,"claimant":301,"quote":302,"sourceUrl":303},"productivity-gain",75,"manual data capture workload","vendor","The Rossum IDP platform has reduced the manual workload by 75% and improved the time spent verifying and validating documents.","https://rossum.ai/customer-stories/master-trust-bank-of-japan/",{"kpi":49,"value":305,"unit":209,"qualifier":272,"period":306,"claimant":301,"quote":307,"sourceUrl":303},100000,"documents per year","To date, Rossum has scaled to handle 100,000 documents per year for MTBJ.",[309],{"url":303,"title":310,"publisher":291},"Customer Story - Master Trust Bank of Japan - Rossum.ai",{"level":225,"checkedAt":252},"master-trust-bank-of-japan-financial-document-capture",{"title":314,"useCases":315,"organization":316,"vendors":318,"summary":324,"stage":325,"year":265,"channels":326,"languages":327,"metrics":328,"outcomeDisclosed":215,"sources":335,"verification":340,"grade":253,"id":341,"organizationSlug":255},"Travelers: generative AI classification of policy service emails",[184],{"name":317,"anonymized":191,"country":192,"region":193,"industry":19},"Travelers",[319,321],{"name":320,"role":238},"Amazon Web Services",{"name":322,"role":323},"Anthropic","model-provider","Travelers receives millions of emails a year from agents and customers asking for policy service. With AWS it built a classifier that reads each email and its attachments (Amazon Textract turns PDF attachments into text) and assigns one of 13 service categories with a prompted foundation model (Anthropic's Claude) on Amazon Bedrock; the post says the classifier powers an automation system for these requests but does not confirm that it runs in production. Initial testing without prompt engineering gave 68% accuracy; prompt engineering, condensed categories and better instructions raised it to 91%. The ground truth set held over 4,000 labelled emails.","announced",[30,32],[205],[329],{"kpi":47,"value":330,"unit":271,"qualifier":272,"period":331,"baseline":332,"claimant":301,"quote":333,"sourceUrl":334},91,"evaluation accuracy after prompt engineering (testing, not production)","68% in initial testing without prompt engineering","After using a variety of techniques with Anthropic’s Claude v2, such as prompt engineering, condensing categories, adjusting document processing process, and improving instructions, accuracy increased to 91%.","https://aws.amazon.com/blogs/machine-learning/how-travelers-insurance-classified-emails-with-amazon-bedrock-and-prompt-engineering/",[336],{"url":334,"title":337,"publisher":338,"date":339},"How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering","AWS Artificial Intelligence Blog","2025-01-31",{"level":225,"checkedAt":180},"travelers-policy-service-email-classification",{"title":343,"useCases":344,"organization":345,"vendors":348,"summary":351,"stage":240,"year":352,"channels":353,"languages":354,"metrics":356,"outcomeDisclosed":191,"sources":357,"verification":361,"grade":253,"id":362,"organizationSlug":255},"Ecclesia Group: automatic matching and routing of claims correspondence",[184],{"name":346,"anonymized":191,"country":347,"region":152,"industry":19},"Ecclesia Group","DE",[349],{"name":350,"role":238},"ABBYY","Ecclesia Group, a German insurance broker, uses ABBYY to process incoming claims correspondence. The platform extracts key data such as case numbers and licence plates from scanned documents, matches each document to the right record in the customer database and routes it to the responsible claims manager, replacing manual sorting. No outcome figures could be verified.",2022,[31],[355],"de",[],[358],{"url":359,"title":360,"publisher":350},"https://www.abbyy.com/customer-stories/ecclesia-group-streamlines-correspondence-management-with-abbyy","How Ecclesia Group Streamlined Insurance Claims Processing",{"level":225,"checkedAt":252},"ecclesia-group-claims-correspondence-routing",0,[365,373,378],{"kpi":49,"label":366,"unit":209,"aggregate":191,"higherIsBetter":215,"n":62,"nUpTo":363,"median":367,"min":208,"max":305,"byClaimant":368,"vendorOnly":191,"points":370},"Interactions handled",62500,{"organization":369,"vendor":369,"regulator":363,"independent":363},1,[371,372],{"evidenceId":312,"organization":286,"value":305,"qualifier":272,"claimant":301,"grade":253,"pooled":215},{"evidenceId":227,"organization":190,"value":208,"qualifier":210,"claimant":212,"grade":226,"pooled":215},{"kpi":47,"label":374,"unit":271,"aggregate":215,"higherIsBetter":215,"n":369,"nUpTo":363,"median":330,"min":330,"max":330,"byClaimant":375,"vendorOnly":215,"points":376},"Accuracy",{"organization":363,"vendor":369,"regulator":363,"independent":363},[377],{"evidenceId":341,"organization":317,"value":330,"qualifier":272,"claimant":301,"grade":253,"pooled":215},{"kpi":45,"label":379,"unit":271,"aggregate":215,"higherIsBetter":215,"n":369,"nUpTo":363,"median":270,"min":270,"max":270,"byClaimant":380,"vendorOnly":191,"points":381},"Automation rate",{"organization":369,"vendor":363,"regulator":363,"independent":363},[382],{"evidenceId":281,"organization":260,"value":270,"qualifier":272,"claimant":212,"grade":253,"pooled":215},{"low":384,"high":385},500000.00000000006,2666666.666666667,[387,412,433,452,467,485],{"slug":174,"title":388,"shortTitle":389,"definition":390,"status":9,"industries":391,"functions":394,"patterns":395,"audience":398,"autonomy":399,"adoptionStage":35,"evidenceCount":400,"publicEvidenceCount":400,"organizations":401,"bestGrade":226,"headline":408,"lastVerified":180,"indexable":215},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[17,20,18,392,393],"telecommunications","technology",[23,22],[396,28,397,26],"content-generation","rag-knowledge-assistant","employee-facing","copilot",6,[402,403,404,405,406,407],"Centers for Disease Control and Prevention","First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":46,"label":409,"unit":271,"n":62,"nUpTo":363,"kind":410,"value":77,"qualifier":411,"claimant":301,"organization":404,"vendorReported":215},"Cycle time reduction","reported","approximately",{"slug":175,"title":413,"shortTitle":414,"definition":415,"status":9,"industries":416,"functions":419,"patterns":421,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":423,"publicEvidenceCount":424,"organizations":425,"bestGrade":226,"headline":431,"lastVerified":180,"indexable":215},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[17,20,417,418],"automotive","manufacturing",[22,24,420],"finance-and-accounting",[27,422,26],"computer-vision",7,5,[426,427,428,429,430],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":47,"label":374,"unit":271,"n":369,"nUpTo":363,"kind":410,"value":432,"qualifier":210,"claimant":301,"organization":426,"vendorReported":215},90,{"slug":176,"title":434,"shortTitle":435,"definition":436,"status":9,"industries":437,"functions":439,"patterns":441,"audience":398,"autonomy":399,"adoptionStage":443,"segment":444,"evidenceCount":62,"publicEvidenceCount":62,"organizations":445,"bestGrade":226,"headline":448,"lastVerified":180,"indexable":215},"AI agent for complaints recognition, investigation and response","Complaints handling","An AI agent that recognizes when a customer interaction is a complaint, logs it against the regulatory definition, classifies its root cause and severity, gathers the evidence, drafts the acknowledgement and the response for a human handler to approve, and tracks every statutory deadline until the case is closed.",[17,18,438,19,392],"payments",[24,23,440],"regulatory-compliance",[26,28,396,442,397],"agentic-workflow","early-adopters","middle-office",[446,447],"Lloyds Banking Group","NatWest Group",{"kpi":449,"label":450,"unit":451,"n":369,"nUpTo":363,"kind":410,"value":424,"qualifier":411,"claimant":212,"organization":446,"vendorReported":191},"time-saved-per-task","Time saved per task","minutes",{"slug":177,"title":453,"shortTitle":454,"definition":455,"status":9,"industries":456,"functions":458,"patterns":460,"audience":33,"autonomy":34,"adoptionStage":443,"segment":33,"evidenceCount":461,"publicEvidenceCount":62,"organizations":462,"bestGrade":253,"headline":465,"lastVerified":180,"indexable":215},"AI for back office account servicing execution","Account servicing execution","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[18,19,457],"wealth-and-asset-management",[22,459],"lending-and-credit",[442,27,26],3,[463,464],"Banco Supervielle","SS&C Technologies",{"kpi":46,"label":409,"unit":271,"n":62,"nUpTo":363,"kind":410,"value":466,"qualifier":272,"claimant":301,"organization":464,"vendorReported":215},95,{"slug":178,"title":468,"shortTitle":469,"definition":470,"status":9,"industries":471,"functions":473,"patterns":476,"audience":398,"autonomy":399,"adoptionStage":443,"segment":33,"evidenceCount":424,"publicEvidenceCount":424,"organizations":478,"bestGrade":226,"headline":482,"lastVerified":252,"indexable":215},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[17,18,19,20,472,457],"healthcare",[22,23,474,440,475],"collections-and-recovery","claims",[396,397,477],"translation",[479,480,481,464],"Acentra Health","Hiscox","Health Resources and Services Administration",{"kpi":46,"label":409,"unit":271,"n":369,"nUpTo":363,"kind":410,"value":483,"qualifier":272,"claimant":301,"organization":484,"vendorReported":215},25,"SS&C GIDS and RS",{"slug":179,"title":486,"shortTitle":487,"definition":488,"status":9,"industries":489,"functions":490,"patterns":491,"audience":33,"autonomy":34,"adoptionStage":492,"segment":33,"evidenceCount":62,"publicEvidenceCount":62,"organizations":493,"bestGrade":226,"headline":496,"lastVerified":180,"indexable":215},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[18,438],[22,23],[442,27,26,396],"emerging",[494,495],"BNY","JPMorgan Chase",{"kpi":45,"label":379,"unit":271,"n":369,"nUpTo":363,"kind":410,"value":497,"qualifier":210,"claimant":212,"organization":494,"vendorReported":191},10,{"indexable":215,"reasons":499},[],[501,507,512,520,527,532,538,543,550,555,562,568,575,582,588,593,600,606,612,618,624,630,635,640,645,652,659,664,669,676,682,688,694,699],{"id":142,"label":502,"issuer":503,"region":152,"url":504,"description":505,"useCases":506,"indexable":215},"EU AI Act","European Union","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":143,"label":508,"issuer":503,"region":152,"url":509,"description":510,"useCases":511,"indexable":215},"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":513,"label":514,"issuer":515,"region":516,"url":517,"description":518,"useCases":519,"indexable":215},"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":521,"label":522,"issuer":523,"region":193,"url":524,"description":525,"useCases":526,"indexable":215},"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":145,"label":528,"issuer":503,"region":152,"url":529,"description":530,"useCases":531,"indexable":215},"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":144,"label":533,"issuer":534,"region":152,"url":535,"description":536,"useCases":537,"indexable":215},"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":146,"label":539,"issuer":151,"region":152,"url":540,"description":541,"useCases":542,"indexable":215},"FCA Consumer Duty","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":544,"label":545,"issuer":546,"region":288,"url":547,"description":548,"useCases":549,"indexable":215},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":147,"label":551,"issuer":552,"region":288,"url":553,"description":554,"useCases":483,"indexable":215},"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":556,"label":557,"issuer":558,"region":516,"url":559,"description":560,"useCases":561,"indexable":215},"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":563,"label":564,"issuer":565,"region":193,"url":566,"description":567,"useCases":561,"indexable":215},"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":569,"label":570,"issuer":571,"region":152,"url":572,"description":573,"useCases":574,"indexable":215},"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":576,"label":577,"issuer":578,"region":516,"url":579,"description":580,"useCases":581,"indexable":215},"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":583,"label":584,"issuer":503,"region":152,"url":585,"description":586,"useCases":587,"indexable":215},"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":589,"label":590,"issuer":503,"region":152,"url":591,"description":592,"useCases":587,"indexable":215},"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":594,"label":595,"issuer":596,"region":193,"url":597,"description":598,"useCases":599,"indexable":215},"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":601,"label":602,"issuer":503,"region":152,"url":603,"description":604,"useCases":605,"indexable":215},"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":607,"label":608,"issuer":609,"region":193,"url":610,"description":611,"useCases":605,"indexable":215},"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":613,"label":614,"issuer":615,"region":516,"url":616,"description":617,"useCases":605,"indexable":215},"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":619,"label":620,"issuer":503,"region":152,"url":621,"description":622,"useCases":623,"indexable":215},"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":625,"label":626,"issuer":627,"region":193,"url":628,"description":629,"useCases":623,"indexable":215},"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":631,"label":632,"issuer":546,"region":288,"url":633,"description":634,"useCases":497,"indexable":215},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":636,"label":637,"issuer":503,"region":152,"url":638,"description":639,"useCases":497,"indexable":215},"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":641,"label":642,"issuer":503,"region":152,"url":643,"description":644,"useCases":497,"indexable":215},"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":646,"label":647,"issuer":648,"region":152,"url":649,"description":650,"useCases":651,"indexable":215},"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":653,"label":654,"issuer":655,"region":193,"url":656,"description":657,"useCases":658,"indexable":215},"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":660,"label":661,"issuer":503,"region":152,"url":662,"description":663,"useCases":658,"indexable":215},"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":665,"label":666,"issuer":503,"region":152,"url":667,"description":668,"useCases":400,"indexable":215},"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":670,"label":671,"issuer":672,"region":673,"url":674,"description":675,"useCases":424,"indexable":215},"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":677,"label":678,"issuer":679,"region":152,"url":680,"description":681,"useCases":63,"indexable":215},"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":683,"label":684,"issuer":685,"region":152,"url":686,"description":687,"useCases":63,"indexable":215},"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":689,"label":690,"issuer":691,"region":288,"url":692,"description":693,"useCases":461,"indexable":215},"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":695,"label":696,"issuer":503,"region":152,"url":697,"description":698,"useCases":461,"indexable":215},"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":700,"label":701,"issuer":702,"region":193,"url":703,"description":704,"useCases":461,"indexable":215},"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.",1790598300155]