[{"data":1,"prerenderedAt":656},["ShallowReactive",2],{"uc-perpetual-kyc":3,"uc-regulations":454},{"useCase":4,"evidence":205,"blitsAiDeployments":342,"benchmarks":343,"indicative":350,"related":353,"indexability":452,"includeUnpublished":211},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":56,"macroEstimates":85,"feasibility":86,"implementation":100,"risk":140,"blitsAi":180,"faq":182,"related":192,"datePublished":199,"dateModified":199,"lastVerified":200,"changelog":201,"slug":204},"AI for perpetual KYC and event driven customer due diligence","Perpetual KYC","AI for perpetual KYC and event driven reviews","AI keeps KYC files current by reviewing on trigger events, not the calendar. JPMorgan Chase reports a 40% lower KYC unit cost since 2022 from AI and technology.","published","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.",[12,13,14,15,16],"pKYC","continuous KYC","event driven KYC review","KYC refresh automation","periodic review automation",[18,19,20],"banking","payments","wealth-and-asset-management",[22,23],"onboarding-and-kyc","financial-crime-compliance",[25,26,27,28],"agentic-workflow","document-processing","rag-knowledge-assistant","summarization",[30,31,32],"internal-tools","email","mobile-app","back-office","supervised-agent","emerging","middle-office","Banks review each customer's due diligence file on a fixed cycle; Fenergo describes periodic KYC\nas checks at set intervals, typically every year or every two years. Each review means re\ncollecting documents, checking registries and ownership structures, rescreening and writing a\nconclusion, and for corporate clients it can take weeks of back and forth.\n\nA review where nothing material has changed costs much the same effort as one that finds\nsomething, and it annoys customers with repeated requests for information the bank already has.\nAt the same time, a real change, such as a new beneficial owner or a sudden shift in activity,\ncan go unnoticed until the next scheduled review. When reviews fall behind schedule, the backlog\nof overdue files becomes a compliance risk in its own right.",[39],{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"A Fenergo study found that more than half of financial institutions spend between 61 and 150 days on client KYC reviews, at an average cost of USD 2,200 per review.","Ongoing Customer Due Diligence with Perpetual KYC","https://resources.fenergo.com/blogs/perpetual-kyc-pkyc",2026,"1. **Watch for triggers.** The system monitors company registries, ownership changes, screening\n   results, adverse media, transaction behaviour, contact detail changes and document expiry\n   for every customer.\n2. **Assess materiality.** Each event is classified against the bank's trigger policy: ignore,\n   refresh automatically, or open a review.\n3. **Refresh straight through.** Low risk changes (a new registry filing that confirms existing\n   data, a renewed identity document) update the file automatically, with the source recorded.\n4. **Prepare the review.** Material events open a review with the file already assembled: what\n   changed, current documents, registry extracts, screening results and a drafted summary with\n   sources.\n5. **Reach out only when needed.** When information is missing, the customer gets one targeted\n   request through the channel they use, and the analyst concludes and signs off the review.",[46,47,48,49],"compliance","cost-to-serve","customer-experience","risk-reduction",[51,52,53,54,55],"automation-rate","processing-time-reduction","cost-reduction","productivity-gain","cycle-time-days",{"referenceOrg":57,"inputs":58,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A bank with 20,000 corporate and business clients under periodic review",[59,66,73],{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"reviews","Periodic KYC reviews per year",6000,8000,"reviews per year","Editorial assumption for a mix of one, three and five year review cycles on 20,000 clients.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"costPerReview","Cost of a periodic review",800,2200,"USD per review","The high value is the average cost per client KYC review reported in a Fenergo study cited on this page; the low value is an editorial assumption for simpler files.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"avoidedShare","Share of review effort avoided by straight through refresh and prepared files",0.2,0.4,"fraction of review cost","Conservative against the benchmarks on this page (JPMorgan Chase reports a 40% reduction in KYC unit cost since 2022 from AI and technology). Replace with results from your own pilot.","reviews * costPerReview * avoidedShare","USD","per year","KYC review cost avoided","Leaves out the value of detecting material changes earlier, the effect on customer experience and attrition, backlog reduction, data costs and the cost of building the trigger monitoring.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"high","Needs reliable external data feeds, a trigger policy agreed with compliance and sometimes the regulator, and a customer lifecycle system that can take automated updates with an audit trail. AML rules such as MAS Notice 626 still expect regular account reviews, so the design needs a lighter backstop cycle alongside the triggers.",[90,91,92,93],"Structured KYC files with data lineage per attribute","Company registry, ownership and document expiry data feeds","Screening, adverse media and transaction monitoring signals per customer","A written trigger policy that defines material events",[95,96,97,98,99],"Client lifecycle management or KYC platform","Company registries and data providers","Screening and transaction monitoring systems","Customer channels for information requests (portal, app, email)","Document management",{"steps":101,"guardrails":117,"humanInTheLoop":123,"kpisToInstrument":124,"failureModes":130},[102,105,108,111,114],{"title":103,"detail":104},"Write the trigger policy","Agree with compliance which events matter, for which customer types, and what each should cause. Check whether your regulator still expects fixed review cycles and design around it.",{"title":106,"detail":107},"Fix the file first","Structure KYC data by attribute with its source and date. Event driven review is impossible when the file is a folder of PDFs.",{"title":109,"detail":110},"Automate the assembly of reviews","Start by preparing scheduled reviews automatically (registry extracts, screening, draft summary). This saves time before any policy change.",{"title":112,"detail":113},"Switch on triggers for one segment","Enable event driven reviews for one segment, run them in parallel with the calendar, and compare what each approach finds.",{"title":115,"detail":116},"Extend straight through refresh","Allow automatic updates for low risk, well sourced changes and measure the error rate on a sample before widening.",[118,119,120,121,122],"Material changes and every risk rating change need analyst sign off","Automatic updates only from approved sources, with source and date recorded per attribute","Logged reason why each trigger did or did not open a review","Customer outreach limited to information the bank does not already hold","A fallback to scheduled review for customers whose data feeds are incomplete","Analysts sign off every review opened by a material trigger and every change in risk rating. Compliance owns the trigger policy and reviews samples of events that were ignored or refreshed automatically.",[125,126,127,128,129],"Share of trigger events refreshed straight through, and the error rate in sampling","Time from trigger to completed review","Overdue review backlog","Customer outreach requests per review","Material findings per review, compared with calendar reviews",[131,134,137],{"title":132,"detail":133},"Trigger storms","Noisy feeds open thousands of trivial reviews. Tune materiality and measure the share of triggered reviews with a finding.",{"title":135,"detail":136},"Silent gaps","A customer with no data feed never triggers and never gets reviewed. Keep a backstop review cycle.",{"title":138,"detail":139},"Regulatory mismatch","The regulator still expects fixed cycles. Agree the approach and document it before switching off calendar reviews.",{"euAiAct":141,"regulations":144,"guidance":154,"controls":173,"incidents":179},{"tier":142,"basis":143},"context-dependent","Keeping customer due diligence files current is not listed in Annex III, so a back office system that assembles reviews for an analyst to decide is usually minimal risk. The design decides the rest: a conversational agent that asks customers for missing information must tell them they are interacting with an AI system (Article 50(1)); biometric verification that only confirms a person is who they claim to be is excluded from Annex III point 1(a), while remote biometric identification is high risk; and Article 5(1)(d) prohibits assessing the risk that a person will commit a criminal offence based solely on profiling, so behavioural triggers should open a review for a human rather than score the customer. GDPR applies to the collection and retention of KYC data, including Article 22 if an automated refresh leads to a decision with legal or similarly significant effect, such as closing an account.",[145,146,147,148,149,150,151,152,153],"eu-ai-act","gdpr","fatf-recommendations","mas-ai-risk-management","cbuae-ai-guidance","nist-ai-rmf","eu-amlr","us-bsa","mas-notice-626",[155,161,167],{"title":156,"issuer":157,"region":158,"url":159,"note":160},"Guidelines on the use of remote customer onboarding solutions","European Banking Authority","europe","https://www.eba.europa.eu/regulation-and-policy/anti-money-laundering-and-countering-financing-terrorism/guidelines-use-remote-customer-onboarding-solutions","Sets expectations for remote identity verification and data collection when customers are onboarded remotely; the guidelines cover onboarding, but they are a useful reference when KYC data is refreshed through remote channels.",{"title":162,"issuer":163,"region":164,"url":165,"note":166},"Notice 626 Prevention of Money Laundering and Countering the Financing of Terrorism, Banks","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's AML and CFT rules for banks, covering customer due diligence, regular account reviews and the monitoring and reporting of suspicious transactions.",{"title":168,"issuer":169,"region":170,"url":171,"note":172},"Principles for Using Artificial Intelligence and Machine Learning in Financial Crime Compliance","Wolfsberg Group","global","https://wolfsberg-group.org/resources/202/93","Industry principles from 2022 for the accountable use of AI and machine learning in financial crime compliance programmes, covering legitimate purpose, proportionate use, design and technical expertise, accountability and oversight, and openness and transparency.",[174,175,176,177,178],"Approved trigger policy under change control","Attribute level source and date for every automated update","Sampling of automatically refreshed files and ignored triggers","Backstop review cycle for customers without reliable data feeds","Audit trail of every review conclusion and sign off",[],{"howToBuild":181},"On Blits.ai trigger handling runs as **agentic tasks** (\"when this condition is met, do this\")\nand **agentic workflows** triggered on a schedule or through the API. The agent calls **custom\nfunctions** for registries, screening and the KYC platform, reads documents from the **knowledge\nbase** (PDF, Office files and Outlook email files), compares them with the file and drafts a\nreview summary with sources as **structured output**. Updates to the file and risk rating\nchanges go through **human in the loop approval**, and every run keeps a full audit trail.\n\nWhen the bank needs something from the client, a **conversational agent** on **email or\nWhatsApp**, or in the bank's own app through the **REST or WebSocket API channel**, asks for exactly the missing document or confirmation and accepts\n**attachments**, with **PII masking** and a **human handover** to the relationship team.\n**Test suites** and **monitors** keep the workflow honest, and the platform is model agnostic\nwith EU and UAE data residency.",[183,186,189],{"question":184,"answer":185},"Does perpetual KYC replace periodic reviews?","Partly. Event driven reviews catch change sooner and let the bank skip work where nothing changed, but AML rules such as MAS Notice 626 still expect regular account reviews. A sound design keeps a lighter backstop cycle alongside the triggers, agreed with the regulator.",{"question":187,"answer":188},"What triggers a review in perpetual KYC?","Typical triggers are changes of ownership or directors, new adverse media or sanctions results, unusual transaction behaviour, changes of address or country, and expiring documents. The bank's written trigger policy defines which ones matter for which customer types.",{"question":190,"answer":191},"Where does AI help most?","In assembling the file and judging materiality: reading registry filings and documents, comparing them with what the bank holds, and drafting a summary so that the analyst decides rather than collects. JPMorgan Chase reports a 40% reduction in KYC unit cost since 2022 from AI and technology, and Nasdaq Verafin describes an agent that automates a bank's periodic enhanced due diligence review process, closing low risk cases itself and escalating the rest.",[193,194,195,196,197,198],"dynamic-customer-risk-rating","pep-and-adverse-media-screening","business-onboarding-and-ubo-discovery","sanctions-screening-adjudication","digital-onboarding-assistant","source-of-wealth-diligence","2026-09-27","2026-09-26",[202],{"date":199,"note":203},"First published","perpetual-kyc",[206,247,267,293,315],{"title":207,"useCases":208,"organization":209,"vendors":214,"summary":218,"stage":219,"year":43,"channels":220,"languages":221,"metrics":223,"outcomeDisclosed":233,"sources":234,"verification":242,"grade":244,"id":245,"organizationSlug":246},"FNBO: agentic AI for enhanced due diligence and sanctions alerts with Nasdaq Verafin",[204,196],{"name":210,"anonymized":211,"country":212,"region":213,"industry":18},"First National Bank of Omaha (FNBO)",false,"US","north-america",[215],{"name":216,"role":217},"Nasdaq Verafin","platform","FNBO deployed Nasdaq Verafin's Agentic EDD Analyst and Agentic Sanctions Analyst, which remove manual information gathering across multiple systems for enhanced due diligence cases and sanctions alerts. The vendor reports that the bank spent 50% less time on these reviews and alerts and redirected investigator capacity to deeper analysis.","production",[30],[222],"en",[224],{"kpi":225,"value":226,"unit":227,"qualifier":228,"period":229,"claimant":230,"quote":231,"sourceUrl":232},"handling-time-reduction",50,"percent","exact","per case, enhanced due diligence and sanctions alert reviews","independent","At First National Bank of Omaha, AI agents have begun taking on some of the work of human financial crime investigators, reducing the time that people spend on each case by 50%, according to bank executives.","https://www.americanbanker.com/news/how-fnbo-uses-agentic-ai-to-investigate-financial-crime",true,[235,238],{"url":236,"title":237,"publisher":216},"https://verafin.com/resource/fnbo-seizes-the-agentic-ai-advantage/","FNBO Seizes the Agentic AI Advantage",{"url":232,"title":239,"publisher":240,"date":241},"How FNBO uses agentic AI to investigate financial crime","American Banker","2026-06-24",{"level":243,"checkedAt":199},"source-verified","B","fnbo-verafin-agentic-edd-and-sanctions",null,{"title":248,"useCases":249,"organization":250,"vendors":253,"summary":256,"stage":219,"year":43,"channels":257,"languages":258,"metrics":259,"outcomeDisclosed":211,"sources":260,"verification":265,"grade":244,"id":266,"organizationSlug":246},"OCBC and Bank of Singapore: HELIOS agentic AI for customer due diligence in private banking",[194,204],{"name":251,"anonymized":211,"country":252,"region":164,"industry":18},"OCBC","SG",[254],{"name":251,"role":255},"in-house","OCBC launched HELIOS in July 2026, an agentic AI platform that gathers intelligence on prospective private banking clients and completes most of the customer due diligence before a relationship manager engages them. OCBC says private banking accounts can now be opened in 15 business days, against an industry median of about six weeks, while relationship managers and review teams keep accountability for judgment and decisions. OCBC plans to extend HELIOS to ongoing monitoring of customer activity to detect changes in risk profiles. Bank of Singapore relationship managers use it in Singapore, Hong Kong and Dubai, with the rollout due to finish in the third quarter of 2026.",[30],[222],[],[261],{"url":262,"title":263,"publisher":251,"date":264},"https://www.ocbc.com/group/media/release/2026/ocbc-harnesses-agentic-ai-to-quicken-onboarding-customers.page","OCBC harnesses agentic AI to sharpen and quicken onboarding of wealthy customers","2026-07-29",{"level":243,"checkedAt":199},"ocbc-helios-agentic-due-diligence",{"title":268,"useCases":269,"organization":270,"vendors":272,"summary":274,"stage":275,"year":276,"channels":277,"languages":278,"metrics":279,"outcomeDisclosed":233,"sources":286,"verification":290,"grade":244,"id":291,"organizationSlug":292},"JPMorgan Chase: AI and technology in KYC processing for commercial and investment banking clients",[204],{"name":271,"anonymized":211,"country":212,"region":170,"industry":18},"JPMorgan Chase",[273],{"name":271,"role":255},"At its 2025 Investor Day, JPMorgan Chase's Commercial and Investment Bank said it was using AI and technology across the client journey, including onboarding and know your customer processing, and reported a substantial fall in the unit cost of KYC since 2022. The disclosure covers KYC processing in general rather than event driven review specifically.","scaled",2025,[30],[222],[280],{"kpi":53,"value":281,"unit":227,"qualifier":228,"period":282,"claimant":283,"quote":284,"sourceUrl":285},40,"KYC unit cost, 2022 to 2025","organization","In KYC, for instance, we've seen a 40% reduction in unit cost since 2022 due to AI and technology enhancements.","https://www.jpmorganchase.com/content/dam/jpmc/jpmorgan-chase-and-co/investor-relations/documents/events/2025/jpmc-2025-investor-day/cib.pdf",[287],{"url":285,"title":288,"publisher":271,"date":289},"2025 Investor Day, Commercial and Investment Bank transcript","2025-05-19",{"level":243,"checkedAt":200},"jpmorgan-chase-kyc-unit-cost","jpmorgan-chase",{"title":294,"useCases":295,"organization":296,"vendors":298,"summary":300,"stage":219,"year":276,"channels":301,"languages":302,"metrics":303,"outcomeDisclosed":211,"sources":304,"verification":312,"grade":313,"id":314,"organizationSlug":246},"Origin Bank: agentic AI for enhanced due diligence reviews with Nasdaq Verafin",[204],{"name":297,"anonymized":211,"country":212,"region":213,"industry":18},"Origin Bank",[299],{"name":216,"role":217},"Origin Bank, a US bank with about USD 10 billion in assets, uses Nasdaq Verafin's agentic AI workforce for its enhanced due diligence reviews of high risk customers. Nasdaq Verafin's Digital EDD Analyst automates the bank's periodic EDD review process, closing low risk cases itself and escalating the rest. The vendor reports a large increase in the number of EDD reviews completed.",[30],[222],[],[305,308],{"url":306,"title":307,"publisher":216},"https://verafin.com/resource/origin-bank-proves-the-business-case-for-the-agentic-ai-workforce/","Origin Bank Proves the Business Case for the Agentic AI Workforce",{"url":309,"title":310,"publisher":216,"date":311},"https://verafin.com/news/nasdaq-verafin-announces-launch-of-its-agentic-ai-workforce-delivering-a-step-change-in-aml-compliance-efficiency/","Nasdaq Verafin Announces Launch of its Agentic AI Workforce","2025-07-21",{"level":243,"checkedAt":200},"C","origin-bank-verafin-agentic-edd-reviews",{"title":316,"useCases":317,"organization":318,"vendors":321,"summary":324,"stage":219,"year":325,"channels":326,"languages":327,"metrics":328,"outcomeDisclosed":233,"sources":335,"verification":339,"grade":313,"id":340,"organizationSlug":341},"Deutsche Bank: automated adverse media and PEP screening for new accounts and refreshes with WorkFusion",[194,204],{"name":319,"anonymized":211,"country":320,"region":170,"industry":18},"Deutsche Bank","DE",[322],{"name":323,"role":217},"WorkFusion","Deutsche Bank used WorkFusion's AI automation for screening work in anti money laundering, including adverse media monitoring and PEP checks for new accounts and refresh screenings, which had required large teams to scan news reports manually. For the KYC programme as a whole, the vendor reports shorter handling times, about 25,000 cases handled per quarter and tens of thousands of hours saved each year.",2020,[30],[222],[329],{"kpi":225,"value":226,"unit":227,"qualifier":330,"period":331,"claimant":332,"quote":333,"sourceUrl":334},"up-to","range of 25 to 50%, across the whole KYC programme (screening and document processing)","vendor","25–50% reduction in handling time","https://www.workfusion.com/customer-stories/deutsche-bank/",[336],{"url":334,"title":337,"publisher":323,"date":338},"Deutsche Bank Customer Story","2020-11-05",{"level":243,"checkedAt":199},"deutsche-bank-workfusion-screening-automation","deutsche-bank",0,[344],{"kpi":53,"label":345,"unit":227,"aggregate":233,"higherIsBetter":233,"n":346,"nUpTo":342,"median":281,"min":281,"max":281,"byClaimant":347,"vendorOnly":211,"points":348},"Cost reduction",1,{"organization":346,"vendor":342,"regulator":342,"independent":342},[349],{"evidenceId":291,"organization":271,"value":281,"qualifier":228,"claimant":283,"grade":244,"pooled":233},{"low":351,"high":352},960000,7040000,[354,367,390,407,422,442],{"slug":193,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":361,"audience":33,"autonomy":34,"adoptionStage":364,"segment":36,"evidenceCount":346,"publicEvidenceCount":346,"organizations":365,"bestGrade":244,"headline":246,"lastVerified":199,"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.",[18,19,20],[23,360],"risk-management",[362,363],"prediction-and-scoring","anomaly-detection","early-adopters",[366],"bunq",{"slug":194,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":373,"audience":376,"autonomy":377,"adoptionStage":364,"segment":36,"evidenceCount":378,"publicEvidenceCount":378,"organizations":379,"bestGrade":244,"headline":385,"lastVerified":199,"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.",[18,19,20],[23,22],[27,28,374,375],"classification-and-routing","translation","employee-facing","copilot",7,[319,380,381,251,382,383,384],"HSBC","Mashreq","Santander UK","Save the Children","Scotiabank",{"kpi":225,"label":386,"unit":227,"n":346,"nUpTo":346,"kind":387,"value":388,"qualifier":389,"claimant":332,"organization":383,"vendorReported":233},"Handling time reduction","reported",60,"at-least",{"slug":195,"title":391,"shortTitle":392,"definition":393,"status":9,"industries":394,"functions":396,"patterns":397,"audience":33,"autonomy":34,"adoptionStage":35,"segment":398,"evidenceCount":399,"publicEvidenceCount":399,"organizations":400,"bestGrade":313,"headline":404,"lastVerified":199,"indexable":233},"AI for business onboarding (KYB) and beneficial ownership discovery","Business onboarding and UBO","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.",[18,19,395],"capital-markets",[22,23],[26,25,374,28],"specialized-businesses",3,[401,402,403],"BNY","Incore Bank","M-DAQ Global",{"kpi":51,"label":405,"unit":227,"n":346,"nUpTo":342,"kind":387,"value":406,"qualifier":228,"claimant":283,"organization":401,"vendorReported":211},"Automation rate",25,{"slug":196,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":413,"audience":33,"autonomy":34,"adoptionStage":364,"segment":36,"evidenceCount":378,"publicEvidenceCount":378,"organizations":414,"bestGrade":244,"headline":419,"lastVerified":200,"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.",[18,19],[23],[374,362,25],[415,210,380,381,416,417,418],"AJ Bell","Ratepay","Standard Chartered","United Overseas Bank (UOB)",{"kpi":420,"label":421,"unit":227,"n":346,"nUpTo":342,"kind":387,"value":388,"qualifier":228,"claimant":283,"organization":418,"vendorReported":211},"false-positive-reduction","False positive reduction",{"slug":197,"title":423,"shortTitle":424,"definition":425,"status":9,"industries":426,"functions":427,"patterns":430,"audience":433,"autonomy":34,"adoptionStage":364,"segment":434,"evidenceCount":435,"publicEvidenceCount":399,"organizations":436,"bestGrade":313,"headline":438,"lastVerified":199,"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.",[18,19,20],[22,428,429],"sales","customer-service",[431,26,432,25],"conversational-agent","computer-vision","customer-facing","front-office",6,[437,319,403],"Albo",{"kpi":54,"label":439,"unit":440,"n":346,"nUpTo":342,"kind":387,"value":441,"qualifier":228,"claimant":332,"organization":403,"vendorReported":233},"Productivity gain","multiplier",30,{"slug":198,"title":443,"shortTitle":444,"definition":445,"status":9,"industries":446,"functions":447,"patterns":448,"audience":376,"autonomy":377,"adoptionStage":364,"segment":434,"evidenceCount":399,"publicEvidenceCount":399,"organizations":450,"bestGrade":244,"headline":246,"lastVerified":200,"indexable":233},"AI agent for source of wealth due diligence in private banking","Source of wealth diligence","An AI agent that reads a prospective private client's documents, extracts and corroborates how their wealth was built, checks plausibility against benchmarks and external sources, and drafts the source of wealth and enhanced due diligence narrative for the relationship manager and compliance analyst, who decide on the risk rating and the relationship.",[20,18],[22,23],[26,25,449,28],"content-generation",[451,319],"Bank of Singapore",{"indexable":233,"reasons":453},[],[455,461,466,473,479,485,492,499,504,510,517,523,530,536,541,546,552,558,564,570,576,582,586,591,596,602,609,614,619,626,633,639,645,650],{"id":145,"label":456,"issuer":457,"region":158,"url":458,"description":459,"useCases":460,"indexable":233},"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":146,"label":462,"issuer":457,"region":158,"url":463,"description":464,"useCases":465,"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":467,"label":468,"issuer":469,"region":170,"url":470,"description":471,"useCases":472,"indexable":233},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":150,"label":474,"issuer":475,"region":213,"url":476,"description":477,"useCases":478,"indexable":233},"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":480,"label":481,"issuer":457,"region":158,"url":482,"description":483,"useCases":484,"indexable":233},"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":486,"label":487,"issuer":488,"region":158,"url":489,"description":490,"useCases":491,"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":493,"label":494,"issuer":495,"region":158,"url":496,"description":497,"useCases":498,"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":148,"label":500,"issuer":163,"region":164,"url":501,"description":502,"useCases":503,"indexable":233},"MAS AI risk management guidelines","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":505,"label":506,"issuer":507,"region":164,"url":508,"description":509,"useCases":406,"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":511,"label":512,"issuer":513,"region":170,"url":514,"description":515,"useCases":516,"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.",20,{"id":518,"label":519,"issuer":520,"region":213,"url":521,"description":522,"useCases":516,"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":524,"label":525,"issuer":526,"region":158,"url":527,"description":528,"useCases":529,"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":147,"label":531,"issuer":532,"region":170,"url":533,"description":534,"useCases":535,"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":151,"label":537,"issuer":457,"region":158,"url":538,"description":539,"useCases":540,"indexable":233},"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":542,"label":543,"issuer":457,"region":158,"url":544,"description":545,"useCases":540,"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":152,"label":547,"issuer":548,"region":213,"url":549,"description":550,"useCases":551,"indexable":233},"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":553,"label":554,"issuer":457,"region":158,"url":555,"description":556,"useCases":557,"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":559,"label":560,"issuer":561,"region":213,"url":562,"description":563,"useCases":557,"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":565,"label":566,"issuer":567,"region":170,"url":568,"description":569,"useCases":557,"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":571,"label":572,"issuer":457,"region":158,"url":573,"description":574,"useCases":575,"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":577,"label":578,"issuer":579,"region":213,"url":580,"description":581,"useCases":575,"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":153,"label":583,"issuer":163,"region":164,"url":165,"description":584,"useCases":585,"indexable":233},"MAS Notice 626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":587,"label":588,"issuer":457,"region":158,"url":589,"description":590,"useCases":585,"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":592,"label":593,"issuer":457,"region":158,"url":594,"description":595,"useCases":585,"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":597,"label":598,"issuer":157,"region":158,"url":599,"description":600,"useCases":601,"indexable":233},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","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":603,"label":604,"issuer":605,"region":213,"url":606,"description":607,"useCases":608,"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.",8,{"id":610,"label":611,"issuer":457,"region":158,"url":612,"description":613,"useCases":608,"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":615,"label":616,"issuer":457,"region":158,"url":617,"description":618,"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":149,"label":620,"issuer":621,"region":622,"url":623,"description":624,"useCases":625,"indexable":233},"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.",5,{"id":627,"label":628,"issuer":629,"region":158,"url":630,"description":631,"useCases":632,"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.",4,{"id":634,"label":635,"issuer":636,"region":158,"url":637,"description":638,"useCases":632,"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":640,"label":641,"issuer":642,"region":164,"url":643,"description":644,"useCases":399,"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":646,"label":647,"issuer":457,"region":158,"url":648,"description":649,"useCases":399,"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":651,"label":652,"issuer":653,"region":213,"url":654,"description":655,"useCases":399,"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.",1790598300943]