[{"data":1,"prerenderedAt":688},["ShallowReactive",2],{"uc-intelligent-document-processing":3,"uc-regulations":481},{"useCase":4,"evidence":194,"blitsAiDeployments":339,"benchmarks":340,"indicative":358,"related":360,"indexability":479,"includeUnpublished":200},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":22,"patterns":26,"channels":30,"audience":34,"autonomy":35,"adoptionStage":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":144,"blitsAi":170,"faq":172,"related":182,"datePublished":189,"dateModified":189,"lastVerified":189,"changelog":190,"slug":193},"AI document intelligence for unstructured forms and documents","Intelligent document processing","Intelligent document processing (IDP) with AI","AI classifies scanned forms and PDFs, extracts fields with confidence scores and routes doubtful cases to staff. Deployments include Volvo Group, USCIS and Ancine.","published","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.",[12,13,14,15,16],"IDP","document intelligence","AI form processing","AI data extraction from documents","document classification and extraction",[18,19,20,21],"cross-industry","government","automotive","manufacturing",[23,24,25],"operations","case-management","finance-and-accounting",[27,28,29],"document-processing","computer-vision","classification-and-routing",[31,32,33],"api","email","internal-tools","back-office","supervised-agent","mainstream","Many organizations still run on documents that were designed for people: application forms,\nclaims, certificates, supporting evidence, delivery notes, tax documents and letters, arriving as\nscans, phone photos, PDFs and email attachments. Staff open each one, work out what it is, retype\nthe fields into a system and check them against other records. It is slow, error prone and hard to\nscale when volumes spike, and the backlog delays decisions that matter to citizens and customers.\n\nEarlier OCR and template tools worked well for fixed layouts and struggled with anything else.\nCurrent document AI combines layout aware extraction, handwriting recognition and language models,\nso it can handle varied layouts, stamps, handwritten notes, tables across pages and several\nlanguages, as the Volvo Group deployment on this page shows. The design question\nis no longer whether AI can read the document but where it may act alone: straight through\nprocessing for confident, validated extractions, and human review for the rest, with every value\ntraceable to the place on the page it came from.",[],"1. **Ingest from every channel.** Uploads, scans, email attachments and portal submissions land in\n   one intake, where images are cleaned, rotated and split.\n2. **Classify and separate.** Each page or bundle is classified by document type (form, identity\n   document, certificate, statement, invoice) and split into individual documents.\n3. **Extract with confidence.** Fields, tables, checkboxes and signatures are extracted, with the\n   location on the page and a confidence score for each value; text in other languages can be\n   translated.\n4. **Validate.** Values are checked against business rules (formats, totals, dates) and against\n   source systems (the customer, case or supplier record).\n5. **Route by confidence.** Confident, valid documents flow straight into the downstream system;\n   the rest go to a reviewer who sees the page and the extracted value side by side.\n6. **Learn from corrections.** Reviewer corrections are logged to improve extraction and to show\n   which document types or sources cause errors.",[41,42,43,44],"cost-to-serve","speed","employee-productivity","risk-reduction",[46,47,48,49,50],"automation-rate","accuracy","hours-saved","productivity-gain","processing-time-reduction",{"referenceOrg":52,"inputs":53,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"An organization that processes 500,000 forms and supporting documents a year",[54,60,67,74],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"documents","Documents processed per year",500000,"documents per year","The reference organization.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"minutesPerDocument","Manual handling time per document today",4,8,"minutes per document","Editorial assumption for classifying, keying and checking a document. Google Cloud reports that Pupuk Indonesia's data extraction took 5 to 10 minutes before AI.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"timeSaved","Share of handling time removed, including review of uncertain cases",0.5,0.8,"fraction of handling time","Editorial assumption; review of low confidence documents stays with people.",{"key":75,"label":76,"low":70,"high":71,"unit":77,"note":78},"costPerMinute","Fully loaded processing staff cost","USD per minute","Editorial assumption, replace with your own.","documents * minutesPerDocument * timeSaved * costPerMinute","USD","per year","Manual document handling cost avoided","Counts only handling time. It leaves out faster decisions for customers and citizens, fewer keying errors, the cost of the platform and integration, and the reviewer capacity needed at peaks.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Extraction from common document types works out of the box. The effort goes into the long tail of layouts and poor scans, validation against source systems, confidence thresholds per field and a review interface that staff can work in quickly.",[89,90,91,92],"A catalogue of document types with volumes and the fields each process needs","A labelled sample per document type to measure field level accuracy","Business rules and reference data for validation","Records rules for how originals and extracted data are kept",[94,95,96,97],"Scanning, mailroom, email and portal intake","Case management, ERP or line of business systems that receive the data","Reference data and customer or supplier master data for validation","Records and content management for the originals",{"steps":99,"guardrails":118,"humanInTheLoop":124,"kpisToInstrument":125,"failureModes":131},[100,103,106,109,112,115],{"title":101,"detail":102},"Start with volume and pain","Pick the document types with the highest volume and clearest fields, and measure today's handling time and error rate so the baseline is real.",{"title":104,"detail":105},"Measure accuracy per field","Build a labelled test set per document type and measure accuracy per field, not per document. An average field accuracy of 95% can hide a date field that is wrong half the time.",{"title":107,"detail":108},"Set confidence thresholds per field","Decide per field what confidence and which validation checks allow straight through processing, and start conservatively with more human review.",{"title":110,"detail":111},"Design the review screen","Show the page region next to each extracted value and let reviewers correct with one click. Review speed decides most of the business case.",{"title":113,"detail":114},"Validate against systems of record","Check names, numbers and totals against the case, customer or supplier record before data is accepted, and flag mismatches rather than overwrite.",{"title":116,"detail":117},"Watch for drift","Track corrections by document type and source, and retest when forms, suppliers or scanning change.",[119,120,121,122,123],"Straight through processing only above field level confidence thresholds and after validation checks","Every extracted value linked to its location in the source document","Unrecognized pages and documents always go to a person","Personal data in documents processed and stored under the same controls as the source system","Extraction informs decisions; eligibility, benefit or credit decisions stay with the owning process and people","Reviewers handle every document below the confidence threshold or failing validation, and their corrections are logged. Process owners set thresholds and approve changes to them, and quality teams sample straight through documents regularly to confirm accuracy holds.",[126,127,128,129,130],"Field level accuracy per document type on a labelled sample","Straight through processing rate per document type","Reviewer time per document and correction rate","End to end time from receipt to data available in the downstream system","Errors found downstream that originated in extraction",[132,135,138,141],{"title":133,"detail":134},"High average, weak critical field","Overall accuracy looks good while one field that drives decisions is often wrong. Measure and threshold per field.",{"title":136,"detail":137},"Silent errors in straight through processing","Confident but wrong values enter systems unchecked. Validate against source systems and sample straight through documents.",{"title":139,"detail":140},"The long tail stalls the program","Rare layouts consume the project. Route them to people and automate by volume.",{"title":142,"detail":143},"Extraction becomes the decision","A missing field triggers an automatic rejection of an application. Keep decisions in the owning process with human review.",{"euAiAct":145,"regulations":148,"guidance":152,"controls":163,"incidents":169},{"tier":146,"basis":147},"context-dependent","Classifying documents and extracting data for a person or process to use is usually minimal risk. Even inside an Annex III area, a system that only performs a narrow procedural task, such as splitting and classifying documents, can fall outside the high risk category under Article 6(3); the provider must document that assessment and register the system (Article 6(4) and Article 49(2)). The picture changes when extraction materially influences decisions in Annex III areas, such as eligibility for public assistance benefits (point 5(a)), creditworthiness (point 5(b)) or asylum, visa and residence permit applications (point 7), where the whole system must be assessed as potentially high risk. The Article 6(3) exception never applies when the system performs profiling of natural persons.",[149,150,151],"eu-ai-act","gdpr","iso-42001",[153,159],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Points 5 and 7 cover public benefits, credit, and migration and asylum decisions that document processing often feeds.",{"title":160,"issuer":155,"region":156,"url":161,"note":162},"Article 22 GDPR, automated individual decision making, including profiling","https://gdpr-info.eu/art-22-gdpr/","Relevant when extraction results trigger automatic decisions with significant effects on people.",[164,165,166,167,168],"Documented accuracy per field and document type before go live and after changes","Threshold and routing rules under change control","Audit trail from each extracted value to the source document and reviewer action","Retention of originals and extracted data aligned with records rules","Assessment of whether downstream decisions fall in an Annex III area",[],{"howToBuild":171},"On Blits.ai document intake runs as an **agentic workflow**: documents arrive through a **receive\nattachment** block in a conversation or through the **REST API**, and the workflow is started\nfrom a flow or through the API. **Custom functions** call the OCR or document AI service you\nchoose through its REST API, and an **AI agent** with **structured output** classifies the\ndocument, normalizes the fields and marks values it is unsure of for review. Further custom\nfunctions validate the values against systems of record through REST or SQL queries and write\naccepted data to the case or ERP system (the integration catalog includes SAP, Salesforce and\nServiceNow).\n\n**Human in the loop** confirmation, with approve and reject controls and a threshold you\nconfigure, lets a reviewer check extracted data before it is written, and the workflow's **run\nhistory with a full audit trail** records each run. Automatic **PII masking** at the gateway\nprotects personal data, and **test suites** evaluate the agents and workflow against expected results\nafter each change. The platform is model agnostic, so the model behind each agent can be chosen\nand changed, and it can run in the EU or UAE region.",[173,176,179],{"question":174,"answer":175},"How accurate is AI document extraction?","It depends on the document type and the field, so measure it per field on your own documents. Google Cloud reports that Ancine, Brazil's cinema industry regulator, reached over 90% data extraction accuracy on digitized tax documents and a tenfold increase in analysts' daily processing capacity. Set confidence thresholds per field and keep people on the uncertain cases.",{"question":177,"answer":178},"What volumes and savings do organizations report?","Microsoft reports that Volvo Group's solution for invoices, credit notes and claims documents has saved 10,000 manual hours since launch, about 850 a month. Google Cloud reports that Pupuk Indonesia cut data extraction time from 5 to 10 minutes to 40 to 70 seconds, with one employee validating the results. USCIS splits and classifies I-539 applications so that adjudicators find each supporting document faster; it publishes no figures.",{"question":180,"answer":181},"How is this different from invoice processing or correspondence triage?","Invoice processing is one specialized use of document AI, with purchase order matching and posting. Correspondence triage is about routing incoming mail. This page covers the general capability for forms, applications and supporting documents in any process.",[183,184,185,186,187,188],"supplier-invoice-processing","correspondence-triage-and-routing","commercial-underwriting-submission-triage","application-and-identity-fraud-detection","trade-document-examination","account-servicing-execution","2026-09-27",[191],{"date":189,"note":192},"First published","intelligent-document-processing",[195,234,257,294,314],{"title":196,"useCases":197,"organization":198,"vendors":203,"summary":210,"stage":211,"year":212,"channels":213,"languages":214,"metrics":216,"outcomeDisclosed":200,"sources":217,"verification":228,"grade":231,"id":232,"organizationSlug":233},"US Citizenship and Immigration Services: intelligent document processing for I-539 applications",[193],{"name":199,"anonymized":200,"country":201,"region":202,"industry":19},"U.S. Citizenship and Immigration Services",false,"US","north-america",[204,207],{"name":205,"role":206},"Hyperscience","platform",{"name":208,"role":209},"CGI Federal","integrator","Before this system, every page of an I-539 application (a request to extend or change nonimmigrant status) was scanned and stored as one document, which slowed adjudication and did not meet National Archives records standards. USCIS now uses an intelligent document processing tool to identify, classify and split each application into its component documents, such as the form itself, other USCIS forms, passports, driving licences, marriage certificates and bank statements. Pages the tool cannot identify go to a person. It is listed as deployed since November 2024; no outcome figures are published.","production",2024,[31],[215],"en",[],[218,222,225],{"url":219,"title":220,"publisher":221},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory","Office of Management and Budget (GitHub)",{"url":223,"title":224,"publisher":221},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (entry DHS-2385, Intelligent Document Processing (IDP) for I-539 Form Digitization)",{"url":226,"title":227,"publisher":199},"https://www.uscis.gov/i-539","I-539, Application to Extend/Change Nonimmigrant Status",{"level":229,"checkedAt":230},"source-verified","2026-09-26","B","uscis-i-539-intelligent-document-processing",null,{"title":235,"useCases":236,"organization":237,"vendors":239,"summary":244,"stage":211,"year":245,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":200,"sources":249,"verification":254,"grade":231,"id":255,"organizationSlug":256},"US Immigration and Customs Enforcement: intelligent document processing for invoices and forms",[183,193],{"name":238,"anonymized":200,"country":201,"region":202,"industry":19},"U.S. Immigration and Customs Enforcement",[240,242],{"name":241,"role":206},"UiPath",{"name":243,"role":206},"Microsoft","Business units at ICE, part of the Department of Homeland Security, use an intelligent document processing platform (UiPath Suite and Azure AI Document Intelligence) with OCR and machine learning models to verify, extract and classify information from forms, automating repeatable work such as invoice processing and form entry validation. The agency lists it in operation since 2019 and says it saves staff significant time while improving data quality. No figures are published.",2019,[33],[215],[],[250],{"url":251,"title":252,"publisher":221,"date":253},"https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv","2024 consolidated AI use case inventory (raw data, version 2)","2025-01-23",{"level":229,"checkedAt":230},"us-immigration-and-customs-enforcement-intelligent-document-processing","u-s-immigration-and-customs-enforcement",{"title":258,"useCases":259,"organization":260,"vendors":264,"summary":267,"stage":211,"year":268,"channels":269,"languages":270,"metrics":272,"outcomeDisclosed":286,"sources":287,"verification":291,"grade":292,"id":293,"organizationSlug":233},"Ancine: AI extraction from digitized tax documents for accountability analysis",[193],{"name":261,"anonymized":200,"country":262,"region":263,"industry":19},"Ancine","BR","latin-america",[265],{"name":266,"role":206},"Google Cloud","Ancine, which Google Cloud describes as the Brazilian cinema industry regulator, uses Google Cloud AI to extract and structure data from digitized tax documents to automate the accountability analysis of subsidized projects. Google Cloud reports extraction accuracy above 90% and a tenfold increase in analysts' daily processing capacity.",2026,[31],[271],"pt",[273,281],{"kpi":47,"value":274,"unit":275,"qualifier":276,"period":277,"claimant":278,"quote":279,"sourceUrl":280},90,"percent","at-least","data extraction accuracy","vendor","This AI implementation achieved over 90% data extraction accuracy, boosting analysts' daily processing capacity by 10x.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",{"kpi":49,"value":282,"unit":283,"qualifier":284,"period":285,"claimant":278,"quote":279,"sourceUrl":280},10,"multiplier","exact","analysts' daily processing capacity",true,[288],{"url":280,"title":289,"publisher":266,"date":290},"Real world gen AI use cases from the world's leading organizations","2026-04-22",{"level":229,"checkedAt":189},"C","ancine-tax-document-extraction",{"title":295,"useCases":296,"organization":297,"vendors":301,"summary":305,"stage":211,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":286,"sources":310,"verification":312,"grade":292,"id":313,"organizationSlug":233},"Pupuk Indonesia: AI extraction from business documents with single person validation",[193],{"name":298,"anonymized":200,"country":299,"region":300,"industry":21},"Pupuk Indonesia","ID","asia-pacific",[302,303],{"name":266,"role":206},{"name":304,"role":209},"Devoteam","Pupuk Indonesia, which Google Cloud describes as Asia's largest fertilizer producer, automated its document processing workflows with Vision AI and Gemini, working with Devoteam. Google Cloud reports that data extraction time fell from 5 to 10 minutes to 40 to 70 seconds, and that one employee now validates the results.",2025,[31],[],[],[311],{"url":280,"title":289,"publisher":266,"date":290},{"level":229,"checkedAt":189},"pupuk-indonesia-document-processing",{"title":315,"useCases":316,"organization":317,"vendors":320,"summary":322,"stage":211,"year":323,"channels":324,"languages":325,"metrics":326,"outcomeDisclosed":286,"sources":333,"verification":337,"grade":292,"id":338,"organizationSlug":233},"Volvo Group: AI document processing for invoices, credit notes and claims in service and financing",[193],{"name":318,"anonymized":200,"country":319,"region":156,"industry":20},"Volvo Group","SE",[321],{"name":243,"role":206},"Volvo Group's automation team built a document processing solution on Azure AI Document Intelligence for its service and financing businesses. It reads emails, digital and scanned PDFs and written bills, including stamps, photographs, handwritten notes over printed text and tables across pages, translates content between languages and outputs XML or CSV for the receiving division, with processing time and success rate tracked in a dashboard. Microsoft reports that the solution has saved 10,000 manual hours since launch, about 850 hours a month.",2023,[32,31],[],[327],{"kpi":48,"value":328,"unit":329,"qualifier":284,"period":330,"claimant":278,"quote":331,"sourceUrl":332},10000,"hours","since launch, about 850 hours per month","Since launch, the company has saved 10,000 manual hours—about 850-plus manual hours per month.","https://www.microsoft.com/en/customers/story/1703814256939529124-volvo-group-automotive-azure-ai-services",[334],{"url":332,"title":335,"publisher":336},"Volvo Group streamlines invoice and claims processing with Azure AI and AI Document Intelligence","Microsoft Customer Stories",{"level":229,"checkedAt":230},"volvo-group-document-intelligence",2,[341,348,353],{"kpi":47,"label":342,"unit":275,"aggregate":286,"higherIsBetter":286,"n":343,"nUpTo":344,"median":274,"min":274,"max":274,"byClaimant":345,"vendorOnly":286,"points":346},"Accuracy",1,0,{"organization":344,"vendor":343,"regulator":344,"independent":344},[347],{"evidenceId":293,"organization":261,"value":274,"qualifier":276,"claimant":278,"grade":292,"pooled":286},{"kpi":48,"label":349,"unit":329,"aggregate":200,"higherIsBetter":286,"n":343,"nUpTo":344,"median":328,"min":328,"max":328,"byClaimant":350,"vendorOnly":286,"points":351},"Hours saved",{"organization":344,"vendor":343,"regulator":344,"independent":344},[352],{"evidenceId":338,"organization":318,"value":328,"qualifier":284,"claimant":278,"grade":292,"pooled":286},{"kpi":49,"label":354,"unit":283,"aggregate":286,"higherIsBetter":286,"n":343,"nUpTo":344,"median":282,"min":282,"max":282,"byClaimant":355,"vendorOnly":286,"points":356},"Productivity gain",{"organization":344,"vendor":343,"regulator":344,"independent":344},[357],{"evidenceId":293,"organization":261,"value":282,"qualifier":284,"claimant":278,"grade":292,"pooled":286},{"low":57,"high":359},2560000,[361,383,403,427,451,465],{"slug":183,"title":362,"shortTitle":363,"definition":364,"status":9,"industries":365,"functions":369,"patterns":371,"audience":34,"autonomy":35,"adoptionStage":36,"segment":34,"evidenceCount":374,"publicEvidenceCount":63,"organizations":375,"bestGrade":231,"headline":379,"lastVerified":189,"indexable":286},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI that captures supplier invoices from any format, extracts header and line data, matches them to purchase orders and goods receipts, proposes tax and cost centre coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.",[18,366,19,367,368],"banking","retail-and-ecommerce","energy-and-utilities",[25,370],"procurement",[27,372,373,29],"agentic-workflow","anomaly-detection",5,[376,377,238,378],"Federal Deposit Insurance Corporation","Kingfisher","Veolia",{"kpi":49,"label":354,"unit":275,"n":343,"nUpTo":343,"kind":380,"value":381,"qualifier":284,"claimant":382,"organization":377,"vendorReported":200},"reported",80,"organization",{"slug":184,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":389,"patterns":391,"audience":34,"autonomy":35,"adoptionStage":36,"segment":34,"evidenceCount":393,"publicEvidenceCount":393,"organizations":394,"bestGrade":231,"headline":401,"lastVerified":189,"indexable":286},"AI for inbound correspondence triage and routing","Correspondence triage and routing","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.",[18,366,388,19],"insurance",[23,390,24],"customer-service",[29,27,392],"summarization",6,[395,396,397,398,399,400],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":47,"label":342,"unit":275,"n":343,"nUpTo":344,"kind":380,"value":402,"qualifier":284,"claimant":278,"organization":399,"vendorReported":286},91,{"slug":185,"title":404,"shortTitle":405,"definition":406,"status":9,"industries":407,"functions":408,"patterns":410,"audience":34,"autonomy":35,"adoptionStage":412,"segment":409,"evidenceCount":413,"publicEvidenceCount":413,"organizations":414,"bestGrade":231,"headline":424,"lastVerified":189,"indexable":286},"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.",[388],[409,23],"underwriting",[27,29,411,372],"prediction-and-scoring","early-adopters",9,[415,416,417,418,419,420,421,422,423],"American International Group","AXIS Capital","CNA Financial","Generali Global Corporate & Commercial","Hiscox","Kinsale Capital Group","Markel","Paragon Insurance Group","Skyward Specialty Insurance Group",{"kpi":47,"label":342,"unit":275,"n":343,"nUpTo":344,"kind":380,"value":425,"qualifier":426,"claimant":382,"organization":422,"vendorReported":200},98,"approximately",{"slug":186,"title":428,"shortTitle":429,"definition":430,"status":9,"industries":431,"functions":434,"patterns":438,"audience":34,"autonomy":35,"adoptionStage":412,"segment":439,"evidenceCount":393,"publicEvidenceCount":393,"organizations":440,"bestGrade":231,"headline":447,"lastVerified":230,"indexable":286},"AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[366,432,18,19,433],"payments","telecommunications",[435,436,437],"fraud-prevention","onboarding-and-kyc","lending-and-credit",[27,373,28,411],"front-office",[441,442,443,444,445,446],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":448,"label":449,"unit":283,"n":343,"nUpTo":344,"kind":380,"value":450,"qualifier":284,"claimant":382,"organization":444,"vendorReported":200},"detection-rate-improvement","Detection improvement",2.5,{"slug":187,"title":452,"shortTitle":453,"definition":454,"status":9,"industries":455,"functions":456,"patterns":458,"audience":34,"autonomy":35,"adoptionStage":412,"segment":459,"evidenceCount":460,"publicEvidenceCount":460,"organizations":461,"bestGrade":231,"headline":233,"lastVerified":189,"indexable":286},"AI examination of trade documents under letters of credit and collections","Trade document examination","AI that reads the full document presentation under a letter of credit or collection (bill of lading, commercial invoice, packing list, certificates), extracts and cross checks the data, tests it against the instructions and the ICC rules (for letters of credit, the credit terms, UCP 600 and ISBP), and lists discrepancies by severity with the rule cited, so qualified examiners focus on the genuine exceptions.",[366],[23,457],"regulatory-compliance",[27,29,372,392],"specialized-businesses",3,[462,463,464],"ANZ, HSBC and Lloyds Banking Group","Rand Merchant Bank","Stanbic Bank Uganda",{"slug":188,"title":466,"shortTitle":467,"definition":468,"status":9,"industries":469,"functions":471,"patterns":472,"audience":34,"autonomy":35,"adoptionStage":412,"segment":34,"evidenceCount":460,"publicEvidenceCount":339,"organizations":473,"bestGrade":292,"headline":476,"lastVerified":189,"indexable":286},"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.",[366,388,470],"wealth-and-asset-management",[23,437],[372,27,29],[474,475],"Banco Supervielle","SS&C Technologies",{"kpi":50,"label":477,"unit":275,"n":339,"nUpTo":344,"kind":380,"value":478,"qualifier":284,"claimant":278,"organization":475,"vendorReported":286},"Cycle time reduction",95,{"indexable":286,"reasons":480},[],[482,487,492,499,506,512,519,526,533,540,547,553,560,567,573,578,585,591,597,603,609,615,620,625,630,636,642,647,652,659,665,671,677,682],{"id":149,"label":483,"issuer":155,"region":156,"url":484,"description":485,"useCases":486,"indexable":286},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":150,"label":488,"issuer":155,"region":156,"url":489,"description":490,"useCases":491,"indexable":286},"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":151,"label":493,"issuer":494,"region":495,"url":496,"description":497,"useCases":498,"indexable":286},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":500,"label":501,"issuer":502,"region":202,"url":503,"description":504,"useCases":505,"indexable":286},"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":507,"label":508,"issuer":155,"region":156,"url":509,"description":510,"useCases":511,"indexable":286},"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":513,"label":514,"issuer":515,"region":156,"url":516,"description":517,"useCases":518,"indexable":286},"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":520,"label":521,"issuer":522,"region":156,"url":523,"description":524,"useCases":525,"indexable":286},"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":527,"label":528,"issuer":529,"region":300,"url":530,"description":531,"useCases":532,"indexable":286},"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":534,"label":535,"issuer":536,"region":300,"url":537,"description":538,"useCases":539,"indexable":286},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":541,"label":542,"issuer":543,"region":495,"url":544,"description":545,"useCases":546,"indexable":286},"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":548,"label":549,"issuer":550,"region":202,"url":551,"description":552,"useCases":546,"indexable":286},"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":554,"label":555,"issuer":556,"region":156,"url":557,"description":558,"useCases":559,"indexable":286},"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":561,"label":562,"issuer":563,"region":495,"url":564,"description":565,"useCases":566,"indexable":286},"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":568,"label":569,"issuer":155,"region":156,"url":570,"description":571,"useCases":572,"indexable":286},"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":574,"label":575,"issuer":155,"region":156,"url":576,"description":577,"useCases":572,"indexable":286},"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":579,"label":580,"issuer":581,"region":202,"url":582,"description":583,"useCases":584,"indexable":286},"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":586,"label":587,"issuer":155,"region":156,"url":588,"description":589,"useCases":590,"indexable":286},"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":592,"label":593,"issuer":594,"region":202,"url":595,"description":596,"useCases":590,"indexable":286},"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":598,"label":599,"issuer":600,"region":495,"url":601,"description":602,"useCases":590,"indexable":286},"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":604,"label":605,"issuer":155,"region":156,"url":606,"description":607,"useCases":608,"indexable":286},"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":610,"label":611,"issuer":612,"region":202,"url":613,"description":614,"useCases":608,"indexable":286},"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":616,"label":617,"issuer":529,"region":300,"url":618,"description":619,"useCases":282,"indexable":286},"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":621,"label":622,"issuer":155,"region":156,"url":623,"description":624,"useCases":282,"indexable":286},"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":626,"label":627,"issuer":155,"region":156,"url":628,"description":629,"useCases":282,"indexable":286},"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":631,"label":632,"issuer":633,"region":156,"url":634,"description":635,"useCases":413,"indexable":286},"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":637,"label":638,"issuer":639,"region":202,"url":640,"description":641,"useCases":64,"indexable":286},"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.",{"id":643,"label":644,"issuer":155,"region":156,"url":645,"description":646,"useCases":64,"indexable":286},"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":648,"label":649,"issuer":155,"region":156,"url":650,"description":651,"useCases":393,"indexable":286},"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":653,"label":654,"issuer":655,"region":656,"url":657,"description":658,"useCases":374,"indexable":286},"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":660,"label":661,"issuer":662,"region":156,"url":663,"description":664,"useCases":63,"indexable":286},"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":666,"label":667,"issuer":668,"region":156,"url":669,"description":670,"useCases":63,"indexable":286},"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":672,"label":673,"issuer":674,"region":300,"url":675,"description":676,"useCases":460,"indexable":286},"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":678,"label":679,"issuer":155,"region":156,"url":680,"description":681,"useCases":460,"indexable":286},"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":683,"label":684,"issuer":685,"region":202,"url":686,"description":687,"useCases":460,"indexable":286},"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.",1790598298560]