[{"data":1,"prerenderedAt":639},["ShallowReactive",2],{"uc-business-onboarding-and-ubo-discovery":3,"uc-regulations":442},{"useCase":4,"evidence":201,"blitsAiDeployments":309,"benchmarks":310,"indicative":332,"related":335,"indexability":440,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":23,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":138,"blitsAi":177,"faq":179,"related":189,"datePublished":196,"dateModified":196,"lastVerified":196,"changelog":197,"slug":200},"AI for business onboarding (KYB) and beneficial ownership discovery","Business onboarding and UBO","AI for KYB onboarding and UBO discovery","AI agents build the KYB file for corporate clients and trace ownership to the ultimate beneficial owners. Google Cloud reports 30 times productivity at M-DAQ Global.","published","An AI agent that builds the know your business (KYB) due diligence file for a new or reviewed corporate client, before any account is opened: it collects registry, incorporation and ownership documents, resolves the entity across sources, maps the ownership chain through holding companies, nominees and trusts to the ultimate beneficial owners, screens the entity and its owners, and presents a risk scored case for a compliance analyst to decide.",[12,13,14,15],"KYB automation","UBO discovery","corporate KYC agent","beneficial ownership mapping",[17,18,19],"banking","payments","capital-markets",[21,22],"onboarding-and-kyc","financial-crime-compliance",[24,25,26,27],"document-processing","agentic-workflow","classification-and-routing","summarization",[29,30],"internal-tools","api","back-office","supervised-agent","emerging","specialized-businesses","Onboarding a company is slower and harder than onboarding a person. The bank has to establish who\nthe company is, who controls it and who ultimately owns it, which means pulling filings from\nregistries in several countries, reading articles of association and trust deeds, following\nownership through layers of holding companies, and screening every entity and person found. Much\nof the data is in PDFs, in other languages, or missing from registries with no digital access.\n\nAnalysts can spend more of their time building the file than judging it, clients receive\nrepeated document requests, and cases wait while documents are chased. Opaque structures are exactly where the\nfinancial crime risk sits, so shortcuts are not an option. AI can build the file and the\nownership graph quickly and consistently, as long as every link in the chain stays traceable to a\nsource and a person makes the decision.",[],"1. **Fetch.** From the company name or registration number, the agent pulls registry records,\n   filings and ownership data from the available registers and data providers.\n2. **Read the documents.** Document AI extracts officers, shareholders, percentages and control\n   rights from articles, share registers, trust deeds and client submissions.\n3. **Resolve and map.** Entity resolution links the same company or person across sources, and a\n   graph of ownership and control is built up to the ultimate beneficial owners, with the\n   calculated effective ownership per person.\n4. **Screen.** Every entity and person is screened for sanctions, politically exposed persons and\n   adverse media, and gaps (no registry, missing documents) are listed.\n5. **Score and summarise.** The case is risk scored against the bank's policy and summarised in\n   plain language, with every finding linked to its source.\n6. **Decide.** A compliance analyst reviews, overrides where needed and decides; low risk cases\n   that meet the policy still get a person's lighter review before the decision is confirmed.",[39,40,41,42],"speed","compliance","cost-to-serve","risk-reduction",[44,45,46,47,48],"processing-time-reduction","productivity-gain","automation-rate","cycle-time-days","accuracy",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A bank onboarding 3,000 business clients a year",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"cases","Business onboarding cases per year",3000,"cases per year","The reference bank.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"hoursPerCase","Analyst hours to build and review a KYB file",4,8,"hours per case","Editorial assumption, replace with your own time study. Complex structures take far longer.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"reduction","Share of analyst time saved",0.3,0.5,"fraction of time","Editorial assumption, replace with your own pilot results.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"hourlyCost","Loaded cost of an analyst hour",50,80,"USD per hour","Editorial assumption, replace with your own loaded cost.","cases * hoursPerCase * reduction * hourlyCost","USD","per year","Analyst time released, valued at loaded cost","Values analyst time only. It leaves out data subscription costs, revenue from clients onboarded sooner, fewer clients lost to slow onboarding, and the reduced risk of missing a hidden owner.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"high","Registry coverage and quality vary widely by country, ownership calculations through circular or layered structures are tricky, and the output feeds a regulated decision that examiners review.",[89,90,91],"Access to company registries and commercial ownership data for the bank's markets","The bank's KYB policy, risk model and beneficial ownership thresholds per jurisdiction","Screening lists for sanctions, politically exposed persons and adverse media",[93,94,95,96,97],"Client lifecycle management or onboarding case system","Registry and data provider APIs","Screening engine","Client portal for document requests","CRM",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Codify the policy first","Write down, per jurisdiction and client type, which documents are required, the ownership threshold, and what makes a case low, medium or high risk, before any automation.",{"title":104,"detail":105},"Build the file, not the decision","Start with the agent assembling the case file and ownership graph for analysts, and measure time and quality, before letting any case move forward with lighter review.",{"title":107,"detail":108},"Show confidence and gaps","Make the agent state where registry data is missing or weak, and route those cases to manual research instead of filling the gap.",{"title":110,"detail":111},"Keep the chain examinable","Store for every ownership link the document or record it came from, so an examiner can follow the chain from the client to each beneficial owner.",{"title":113,"detail":114},"Tune on real cases","Compare agent built files with analyst built files on a sample of past cases, including complex structures, and fix systematic misses before scaling.",[116,117,118,119,120],"Every ownership link and screening hit is traceable to a source document or record","Gaps and low confidence are surfaced, never silently filled","A compliance officer decides onboarding, exits and enhanced due diligence","Low risk cases that meet written criteria still get lighter review, with a person confirming the onboarding decision and quality assurance sampling the outcomes","Personal data of owners and directors is used only for the compliance purpose and retained per policy","Analysts review every case above the low risk threshold and can override any finding. Low risk cases still get a lighter review, and a compliance officer decides onboarding and exits in every case. Quality assurance samples cases that passed with lighter review, and the model owner reviews screening and scoring performance.",[123,124,125,126,127],"Elapsed days from application to decision, by risk level","Analyst hours per case, by structure complexity","Share of cases with complete files on first review","Ownership errors and missed owners found in quality assurance","Document requests sent to clients per case",[129,132,135],{"title":130,"detail":131},"False completeness","The graph looks complete because a registry returned nothing about a layer. Show coverage per jurisdiction and flag layers without data.",{"title":133,"detail":134},"Wrong entity match","Two companies or people with similar names are merged. Use identifiers where available and send low confidence matches to an analyst.",{"title":136,"detail":137},"Opaque scoring","Analysts cannot explain why a case scored high. Show the factors behind each score and keep the model documented and validated.",{"euAiAct":139,"regulations":142,"guidance":152,"controls":171,"incidents":176},{"tier":140,"basis":141},"context-dependent","Customer due diligence on legal entities is not listed in Annex III, and an internal analyst tool usually carries no Article 50 transparency duty, so the system is usually minimal risk. The design decides the rest: biometric verification that only confirms a director is who they claim to be is excluded from Annex III point 1(a), but remote biometric identification (one to many matching) is high risk, and so is any use of the output to assess the creditworthiness of the natural persons involved (point 5(b)). GDPR applies to the personal data of owners and directors throughout. Keep biometric and credit steps in separately assessed components.",[143,144,145,146,147,148,149,150,151],"eu-ai-act","gdpr","fatf-recommendations","mas-ai-risk-management","dora","iso-42001","eu-amlr","us-bsa","mas-notice-626",[153,159,165],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"Regulation (EU) 2024/1624 on the prevention of the use of the financial system for money laundering or terrorist financing","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","The EU Anti Money Laundering Regulation applies from 10 July 2027. It sets the customer due diligence and beneficial ownership rules the case file must meet, including the ownership and control tests. Article 76(5) requires meaningful human intervention in every decision to enter, refuse or maintain a business relationship with a customer, and in every decision to raise or lower the customer due diligence measures applied, so no such decision can be left to straight through automation.",{"title":160,"issuer":161,"region":162,"url":163,"note":164},"CDD Final Rule","FinCEN","north-america","https://www.fincen.gov/resources/statutes-and-regulations/cdd-final-rule","The Bank Secrecy Act rule that requires US banks and other covered institutions to identify and verify the beneficial owners of legal entity customers when those companies open accounts. FinCEN ruling FIN-2026-R001 grants exceptive relief from repeating this at each new account opening.",{"title":166,"issuer":167,"region":168,"url":169,"note":170},"MAS Guidelines for Artificial Intelligence (AI) Risk Management","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Consultation paper of 13 November 2025 proposing supervisory expectations on AI oversight, AI inventories, risk materiality, human oversight, testing and monitoring, including for AI agents. Relevant where AI output supports financial crime decisions.",[172,173,174,175],"Written KYB policy per jurisdiction that the agent is configured and tested against","Source record for every ownership link and screening result, retained with the case","Model inventory, validation and monitoring of entity resolution and risk scoring","Quality assurance sampling, with higher sampling for straight through cases",[],{"howToBuild":178},"On Blits.ai this is an **agentic workflow**: an agent loop that calls registry and data provider\nAPIs through **custom functions** or **MCP**, reads the client documents received through a\nreceive attachment step or uploaded to the document library, and uses a **knowledge base** with the bank's KYB\npolicy per jurisdiction. **Structured output**\nproduces the ownership table, the list of gaps and the case summary in a fixed format for the\nonboarding system.\n\nThe **tool execution policy** limits which systems the agent may call, and **human in the loop\napproval** is configured so the analyst approves before anything is written back. The per run **audit trail** records each source consulted, **PII masking**\nprotects personal data in prompts, **guardrails** check the output, and **test suites** replay past cases with known\noutcomes. Data can stay in the EU or UAE region, and the model is chosen per agent.",[180,183,186],{"question":181,"answer":182},"Can AI decide whether to onboard a company?","It should not. AI can build the file, map ownership and score risk, but a compliance officer decides onboarding and exits, and a person confirms every decision. Low risk cases that meet written criteria only get a lighter review, with sampling.",{"question":184,"answer":185},"How does AI find hidden beneficial owners?","By extracting owners from filings and documents, linking the same entities and people across sources, and calculating effective ownership through every layer. It still depends on registry coverage, so gaps must be flagged rather than guessed.",{"question":187,"answer":188},"Are there published results?","Two vendor case studies name a real deployment. Google Cloud lists M-DAQ Global, a fintech group in foreign exchange and cross border payments, whose KYB compliance system on Vertex AI improves productivity by 30 times. Kyndryl reports that a proof of concept it ran with Incore Bank, a Swiss bank whose customers are other banks, financial intermediaries and corporates, and Google Cloud reached up to 99 percent accuracy extracting data from onboarding documents, with a further, unmeasured claim that onboarding time could fall from months to days. Both are vendor claims, one from a fintech and one from a bank, and neither has independent oversight behind it. Treat such figures as a starting point and test them in a pilot.",[190,191,192,193,194,195],"corporate-account-onboarding-orchestration","perpetual-kyc","pep-and-adverse-media-screening","sanctions-screening-adjudication","dynamic-customer-risk-rating","digital-onboarding-assistant","2026-09-27",[198],{"date":196,"note":199},"First published","business-onboarding-and-ubo-discovery",[202,242,278],{"title":203,"useCases":204,"organization":205,"vendors":209,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":221,"outcomeDisclosed":233,"sources":234,"verification":237,"grade":239,"id":240,"organizationSlug":241},"BNY: Eliza AI platform speeds up client onboarding research",[200],{"name":206,"anonymized":207,"country":208,"region":162,"industry":19},"BNY",false,"US",[210,212],{"name":206,"role":211},"in-house",{"name":213,"role":214},"Microsoft","platform","BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry. In a Microsoft customer story, Saed Shonnar, Head of AI Enablement at BNY, says twenty five percent of the bank's new client onboardings this year were assisted with AI, resulting in a twenty percent faster onboarding process on average. A second BNY executive describes the research element of onboarding, the document processing and decision making behind verifying a new client, as the common challenge the AI addresses. The same story describes a digital employee that repairs incomplete payment instructions and faster processing of client settlement inquiries, which are reported as separate use cases.","production",2026,[29],[220],"en",[222,230],{"kpi":46,"value":223,"unit":224,"qualifier":225,"period":226,"claimant":227,"quote":228,"sourceUrl":229},25,"percent","exact","new client onboardings this year","organization","Twenty-five percent of all of our new onboardings were assisted with AI this year and that has resulted in a twenty percent faster onboarding process on average for these clients,","https://www.microsoft.com/en/customers/story/27322-bny-microsoft-365-copilot",{"kpi":44,"value":231,"unit":224,"qualifier":225,"period":232,"claimant":227,"quote":228,"sourceUrl":229},20,"new client onboardings this year, average",true,[235],{"url":229,"title":236,"publisher":213},"Frontier Firm BNY resolves client inquires 80% faster with Microsoft AI powered Eliza",{"level":238,"checkedAt":196},"source-verified","C","bny-eliza-onboarding-research","bny",{"title":243,"useCases":244,"organization":245,"vendors":248,"summary":254,"stage":255,"year":217,"channels":256,"languages":257,"metrics":258,"outcomeDisclosed":233,"sources":266,"verification":275,"grade":239,"id":276,"organizationSlug":277},"Incore Bank: agentic AI proof of concept for business customer onboarding with Kyndryl and Google Cloud",[200],{"name":246,"anonymized":207,"country":247,"region":156,"industry":17},"Incore Bank","CH",[249,252],{"name":250,"role":251},"Kyndryl","integrator",{"name":253,"role":214},"Google Cloud","Incore Bank, a Swiss bank that serves other banks, financial intermediaries and corporates rather than retail customers, completed a proof of concept with Kyndryl and Google Cloud that applies agentic AI, built on Kyndryl's Agentic AI Framework and Google's Gemini models, to the know your customer checks it runs on prospective and existing business clients. Several AI agents extract and validate customer information from documents, internal systems and external sources, identify risk factors, produce an explainable risk score and create an auditable decision record for compliance staff to review. Kyndryl reports the proof of concept reached up to 99 percent accuracy extracting data from onboarding documents; the further claim that the approach could cut onboarding time from months to days is stated as a demonstrated potential, not a measured result.","announced",[29],[],[259],{"kpi":48,"value":260,"unit":224,"qualifier":261,"period":262,"claimant":263,"quote":264,"sourceUrl":265},99,"up-to","automated extraction of data from customer onboarding documentation, during the proof of concept","vendor","During the proof of concept, the solution achieved up to 99% accuracy in the automated extraction of data from customer onboarding documentation and demonstrated potential to reduce onboarding time from months to days.","https://www.kyndryl.com/in/en/about-us/news/2026/08/agentic-ai-incore-bank",[267,270],{"url":265,"title":268,"publisher":250,"date":269},"Kyndryl and Google Cloud advance agentic AI at Incore Bank","2026-08-31",{"url":271,"title":272,"publisher":273,"date":274},"https://thepaypers.com/fraud-and-fincrime/news/kyndryl-incore-bank-google-cloud-test-agentic-ai-onboarding","Kyndryl, Incore Bank, Google Cloud test agentic AI onboarding","The Paypers","2026-09-01",{"level":238,"checkedAt":196},"incore-bank-agentic-kyc-onboarding",null,{"title":279,"useCases":280,"organization":281,"vendors":284,"summary":286,"stage":216,"year":287,"channels":288,"languages":289,"metrics":290,"outcomeDisclosed":233,"sources":297,"verification":307,"grade":239,"id":308,"organizationSlug":277},"M-DAQ Global: AI driven Know Your Business checks for business customer onboarding",[195,200],{"name":282,"anonymized":207,"country":283,"region":168,"industry":18},"M-DAQ Global","SG",[285],{"name":253,"role":214},"M-DAQ Global, a fintech group headquartered in Singapore that specialises in foreign exchange and cross border payments, runs a Know Your Business compliance solution on Vertex AI and Google Kubernetes Engine that uses natural language processing to automate the verification work behind onboarding business customers. The vendor reports a productivity gain of 30 times and shorter onboarding times.",2024,[29],[],[291],{"kpi":45,"value":292,"unit":293,"qualifier":225,"period":294,"claimant":263,"quote":295,"sourceUrl":296},30,"multiplier","compliance tasks, as reported by the vendor","The natural language processing-based system automates compliance tasks and improves productivity by 30 times, reducing onboarding times and eliminating manual bottlenecks in customer verification.","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",[298,301,304],{"url":296,"title":299,"publisher":253,"date":300},"Real-world gen AI use cases from the world's leading organizations","2026-04-22",{"url":302,"title":303,"publisher":282},"https://www.m-daq.com/about-us","About Us",{"url":305,"title":306,"publisher":253},"https://cloud.google.com/customers/mdaq","Improve productivity by 30x with Vertex AI automation and data analysis",{"level":238,"checkedAt":196},"m-daq-global-kyb-onboarding",0,[311,317,322,327],{"kpi":46,"label":312,"unit":224,"aggregate":233,"higherIsBetter":233,"n":313,"nUpTo":309,"median":223,"min":223,"max":223,"byClaimant":314,"vendorOnly":207,"points":315},"Automation rate",1,{"organization":313,"vendor":309,"regulator":309,"independent":309},[316],{"evidenceId":240,"organization":206,"value":223,"qualifier":225,"claimant":227,"grade":239,"pooled":233},{"kpi":44,"label":318,"unit":224,"aggregate":233,"higherIsBetter":233,"n":313,"nUpTo":309,"median":231,"min":231,"max":231,"byClaimant":319,"vendorOnly":207,"points":320},"Cycle time reduction",{"organization":313,"vendor":309,"regulator":309,"independent":309},[321],{"evidenceId":240,"organization":206,"value":231,"qualifier":225,"claimant":227,"grade":239,"pooled":233},{"kpi":45,"label":323,"unit":293,"aggregate":233,"higherIsBetter":233,"n":313,"nUpTo":309,"median":292,"min":292,"max":292,"byClaimant":324,"vendorOnly":233,"points":325},"Productivity gain",{"organization":309,"vendor":313,"regulator":309,"independent":309},[326],{"evidenceId":308,"organization":282,"value":292,"qualifier":225,"claimant":263,"grade":239,"pooled":233},{"kpi":48,"label":328,"unit":224,"aggregate":233,"higherIsBetter":233,"n":309,"nUpTo":313,"median":277,"min":277,"max":277,"byClaimant":329,"vendorOnly":207,"points":330},"Accuracy",{"organization":309,"vendor":309,"regulator":309,"independent":309},[331],{"evidenceId":276,"organization":246,"value":260,"qualifier":261,"claimant":263,"grade":239,"pooled":207},{"low":333,"high":334},180000,960000,[336,353,376,398,413,424],{"slug":190,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":341,"patterns":343,"audience":346,"autonomy":347,"adoptionStage":33,"segment":34,"evidenceCount":348,"publicEvidenceCount":348,"organizations":349,"bestGrade":352,"headline":277,"lastVerified":196,"indexable":233},"AI orchestration of corporate account opening and channel setup","Corporate onboarding operations","An AI agent that runs the operational setup of a corporate client after the due diligence has been approved: it reads mandates, board resolutions and signatory documents, prepares accounts, users, roles and payment entitlements for approval, configures channel access, and chases outstanding items with the client, turning a manual setup that passes between several teams into a tracked, guided flow.",[17],[21,342],"operations",[25,24,344,345],"conversational-agent","content-generation","customer-facing","copilot",2,[350,351],"Citi","Standard Chartered","B",{"slug":191,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":359,"patterns":360,"audience":31,"autonomy":32,"adoptionStage":33,"segment":362,"evidenceCount":363,"publicEvidenceCount":363,"organizations":364,"bestGrade":352,"headline":370,"lastVerified":375,"indexable":233},"AI for perpetual KYC and event driven customer due diligence","Perpetual KYC","AI that keeps each customer's due diligence file current by replacing calendar driven KYC reviews with continuous, event driven refreshes: it watches for trigger events such as a change of ownership, address, behaviour or a new adverse finding, refreshes the file automatically where it can, and involves an analyst only when something material has changed. The risk rating itself and the first file for a new business client are separate use cases.",[17,18,358],"wealth-and-asset-management",[21,22],[25,24,361,27],"rag-knowledge-assistant","middle-office",5,[365,366,367,368,369],"Deutsche Bank","First National Bank of Omaha (FNBO)","JPMorgan Chase","OCBC","Origin Bank",{"kpi":371,"label":372,"unit":224,"n":313,"nUpTo":309,"kind":373,"value":374,"qualifier":225,"claimant":227,"organization":367,"vendorReported":207},"cost-reduction","Cost reduction","reported",40,"2026-09-26",{"slug":192,"title":377,"shortTitle":378,"definition":379,"status":9,"industries":380,"functions":381,"patterns":382,"audience":384,"autonomy":347,"adoptionStage":385,"segment":362,"evidenceCount":386,"publicEvidenceCount":386,"organizations":387,"bestGrade":352,"headline":393,"lastVerified":196,"indexable":233},"AI for PEP and adverse media screening","PEP and adverse media screening","AI that continuously scans news, court records, registries and other open sources in many languages for negative information and political exposure linked to customers, counterparties and beneficial owners, discards look alikes, and summarises credible risk for the analyst with the sources attached.",[17,18,358],[22,21],[361,27,26,383],"translation","employee-facing","early-adopters",7,[365,388,389,368,390,391,392],"HSBC","Mashreq","Santander UK","Save the Children","Scotiabank",{"kpi":394,"label":395,"unit":224,"n":313,"nUpTo":313,"kind":373,"value":396,"qualifier":397,"claimant":263,"organization":391,"vendorReported":233},"handling-time-reduction","Handling time reduction",60,"at-least",{"slug":193,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":403,"patterns":404,"audience":31,"autonomy":32,"adoptionStage":385,"segment":362,"evidenceCount":386,"publicEvidenceCount":386,"organizations":406,"bestGrade":352,"headline":410,"lastVerified":375,"indexable":233},"AI for sanctions screening alert adjudication","Sanctions screening adjudication","AI that works the alerts raised when customer, counterparty or payment names match sanctions and watchlists: it resolves fuzzy matches across transliterations, aliases and naming conventions, clears clear non matches with a documented reason, and escalates true or uncertain hits with the evidence attached.",[17,18],[22],[26,405,25],"prediction-and-scoring",[407,366,388,389,408,351,409],"AJ Bell","Ratepay","United Overseas Bank (UOB)",{"kpi":411,"label":412,"unit":224,"n":313,"nUpTo":309,"kind":373,"value":396,"qualifier":225,"claimant":227,"organization":409,"vendorReported":207},"false-positive-reduction","False positive reduction",{"slug":194,"title":414,"shortTitle":415,"definition":416,"status":9,"industries":417,"functions":418,"patterns":420,"audience":31,"autonomy":32,"adoptionStage":385,"segment":362,"evidenceCount":313,"publicEvidenceCount":313,"organizations":422,"bestGrade":352,"headline":277,"lastVerified":196,"indexable":233},"Dynamic AML customer risk rating with machine learning","Dynamic customer risk rating","Explainable machine learning that produces the money laundering risk rating itself: it computes and continuously updates each customer's rating from due diligence data, products, geography, behaviour and screening results, and shows which factors drive the rating and when enhanced due diligence is warranted.",[17,18,358],[22,419],"risk-management",[405,421],"anomaly-detection",[423],"bunq",{"slug":195,"title":425,"shortTitle":426,"definition":427,"status":9,"industries":428,"functions":429,"patterns":432,"audience":346,"autonomy":32,"adoptionStage":385,"segment":434,"evidenceCount":435,"publicEvidenceCount":436,"organizations":437,"bestGrade":239,"headline":439,"lastVerified":196,"indexable":233},"AI assistant for digital account onboarding and KYC","Digital onboarding","A customer facing AI assistant that guides a new applicant, a person or a small merchant, through a digital account, card or relationship application: it collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[17,18,358],[21,430,431],"sales","customer-service",[344,24,433,25],"computer-vision","front-office",6,3,[438,365,282],"Albo",{"kpi":45,"label":323,"unit":293,"n":313,"nUpTo":309,"kind":373,"value":292,"qualifier":225,"claimant":263,"organization":282,"vendorReported":233},{"indexable":233,"reasons":441},[],[443,448,453,460,467,472,479,486,490,496,502,508,515,521,525,530,535,541,547,553,559,565,570,575,580,587,593,598,603,610,616,622,628,633],{"id":143,"label":444,"issuer":155,"region":156,"url":445,"description":446,"useCases":447,"indexable":233},"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":144,"label":449,"issuer":155,"region":156,"url":450,"description":451,"useCases":452,"indexable":233},"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":148,"label":454,"issuer":455,"region":456,"url":457,"description":458,"useCases":459,"indexable":233},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":461,"label":462,"issuer":463,"region":162,"url":464,"description":465,"useCases":466,"indexable":233},"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":147,"label":468,"issuer":155,"region":156,"url":469,"description":470,"useCases":471,"indexable":233},"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":473,"label":474,"issuer":475,"region":156,"url":476,"description":477,"useCases":478,"indexable":233},"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":480,"label":481,"issuer":482,"region":156,"url":483,"description":484,"useCases":485,"indexable":233},"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":146,"label":487,"issuer":167,"region":168,"url":169,"description":488,"useCases":489,"indexable":233},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":491,"label":492,"issuer":493,"region":168,"url":494,"description":495,"useCases":223,"indexable":233},"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":497,"label":498,"issuer":499,"region":456,"url":500,"description":501,"useCases":231,"indexable":233},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":503,"label":504,"issuer":505,"region":162,"url":506,"description":507,"useCases":231,"indexable":233},"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":509,"label":510,"issuer":511,"region":156,"url":512,"description":513,"useCases":514,"indexable":233},"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":145,"label":516,"issuer":517,"region":456,"url":518,"description":519,"useCases":520,"indexable":233},"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":149,"label":522,"issuer":155,"region":156,"url":157,"description":523,"useCases":524,"indexable":233},"EU Anti Money Laundering Regulation","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":526,"label":527,"issuer":155,"region":156,"url":528,"description":529,"useCases":524,"indexable":233},"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":150,"label":531,"issuer":161,"region":162,"url":532,"description":533,"useCases":534,"indexable":233},"Bank Secrecy Act","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":536,"label":537,"issuer":155,"region":156,"url":538,"description":539,"useCases":540,"indexable":233},"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":542,"label":543,"issuer":544,"region":162,"url":545,"description":546,"useCases":540,"indexable":233},"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":548,"label":549,"issuer":550,"region":456,"url":551,"description":552,"useCases":540,"indexable":233},"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":554,"label":555,"issuer":155,"region":156,"url":556,"description":557,"useCases":558,"indexable":233},"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":560,"label":561,"issuer":562,"region":162,"url":563,"description":564,"useCases":558,"indexable":233},"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":151,"label":566,"issuer":167,"region":168,"url":567,"description":568,"useCases":569,"indexable":233},"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":571,"label":572,"issuer":155,"region":156,"url":573,"description":574,"useCases":569,"indexable":233},"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":576,"label":577,"issuer":155,"region":156,"url":578,"description":579,"useCases":569,"indexable":233},"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":581,"label":582,"issuer":583,"region":156,"url":584,"description":585,"useCases":586,"indexable":233},"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":588,"label":589,"issuer":590,"region":162,"url":591,"description":592,"useCases":62,"indexable":233},"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":594,"label":595,"issuer":155,"region":156,"url":596,"description":597,"useCases":62,"indexable":233},"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":599,"label":600,"issuer":155,"region":156,"url":601,"description":602,"useCases":435,"indexable":233},"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":604,"label":605,"issuer":606,"region":607,"url":608,"description":609,"useCases":363,"indexable":233},"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":611,"label":612,"issuer":613,"region":156,"url":614,"description":615,"useCases":61,"indexable":233},"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":617,"label":618,"issuer":619,"region":156,"url":620,"description":621,"useCases":61,"indexable":233},"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":623,"label":624,"issuer":625,"region":168,"url":626,"description":627,"useCases":436,"indexable":233},"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":629,"label":630,"issuer":155,"region":156,"url":631,"description":632,"useCases":436,"indexable":233},"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":634,"label":635,"issuer":636,"region":162,"url":637,"description":638,"useCases":436,"indexable":233},"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.",1790598299304]