[{"data":1,"prerenderedAt":127},["ShallowReactive",2],{"uc-org-bunq":3},{"organization":4,"includeUnpublished":12,"evidence":13},{"slug":5,"name":5,"country":6,"region":7,"industry":8,"wikidata":9,"records":10,"useCases":10,"indexable":11},"bunq","NL","europe","banking","Q24084541",3,true,false,[14,56,91],{"title":15,"useCases":16,"organization":18,"vendors":19,"summary":23,"stage":24,"year":25,"channels":26,"languages":28,"metrics":30,"outcomeDisclosed":11,"sources":44,"verification":48,"grade":51,"id":52,"useCaseTitles":53},"bunq: cutting fraudulent incoming funds with Sardine",[17],"mule-network-detection",{"name":5,"anonymized":12,"country":6,"region":7,"industry":8,"wikidata":9},[20],{"name":21,"role":22},"Sardine","platform","bunq, which its Sardine case study calls Europe's second largest neobank with over 20 million users, chose Sardine for a list of requirements that included behavioral analytics, phone risk scoring, a no code rule builder, machine learning and case management, to fight scams and the accounts that receive their proceeds. Sardine's case study says bunq achieved \"82% lower fraudulent incoming funds compared to similar neobanks\" and a fourfold reduction in false positives, and quotes bunq's Product Owner for Fraud Prevention saying the platform let the team \"instantly detect the threat, trace the scam across accounts and devices, and shut it down on the spot,\" moving from alert to resolution in under 20 minutes on one attack. The page does not use the words money mule or mule account.","production",2026,[27],"internal-tools",[29],"en",[31,39],{"kpi":32,"value":33,"unit":34,"qualifier":35,"claimant":36,"quote":37,"sourceUrl":38},"fraud-loss-reduction",82,"percent","exact","vendor","82% lower fraudulent incoming funds compared to similar neobanks","https:\u002F\u002Fsardine.ai\u002Fcustomers\u002Fbunq",{"kpi":40,"value":41,"unit":42,"qualifier":35,"claimant":36,"quote":43,"sourceUrl":38},"false-positive-reduction",4,"multiplier","4X reduction in false positives",[45],{"url":38,"title":46,"publisher":21,"archivedUrl":47},"How bunq protects over 20 million global customers with Sardine","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20260509230922\u002Fhttps:\u002F\u002Fwww.sardine.ai\u002Fcustomers\u002Fbunq",{"level":49,"checkedAt":50},"source-verified","2026-09-29","C","bunq-sardine-fraud-network-detection",[54],{"slug":17,"title":55},"AI for money mule account and network detection",{"title":57,"useCases":58,"organization":60,"vendors":61,"summary":67,"stage":68,"year":69,"channels":70,"languages":73,"metrics":74,"outcomeDisclosed":11,"sources":82,"verification":85,"grade":51,"id":87,"useCaseTitles":88},"bunq: Finn multi agent assistant automates most support tickets",[59],"first-line-contact-centre-agent",{"name":5,"anonymized":12,"country":6,"region":7,"industry":8,"wikidata":9},[62,65],{"name":63,"role":64},"Anthropic","model-provider",{"name":5,"role":66},"in-house","bunq, a Dutch neobank, launched its Finn assistant in 2023 to help users search their finances and has built it into a multi agent system that investigates issues, processes documents and carries out banking operations for users who ask. In 2024 bunq added real time speech to speech translation so users can talk to support in their own language. Anthropic's customer story says that after bunq moved to Claude models its automated resolution rate rose by 8%, to over 80% of support tickets.","scaled",2025,[71,72],"mobile-app","voice",[],[75],{"kpi":76,"value":77,"unit":34,"qualifier":78,"period":79,"claimant":36,"quote":80,"sourceUrl":81},"automation-rate",80,"at-least","support tickets","With Claude’s advanced reasoning, bunq’s automated resolution rate improved by 8%, bringing their total automation to over 80% of all support tickets.","https:\u002F\u002Fclaude.com\u002Fcustomers\u002Fbunq",[83],{"url":81,"title":84,"publisher":63},"bunq automates about 80% of support tickets",{"level":49,"checkedAt":86},"2026-11-11","bunq-finn-support-automation",[89],{"slug":59,"title":90},"AI agent for first line contact center service",{"title":92,"useCases":93,"organization":95,"vendors":96,"summary":98,"stage":24,"year":99,"channels":100,"languages":101,"metrics":103,"outcomeDisclosed":12,"sources":104,"verification":120,"grade":122,"id":123,"useCaseTitles":124},"bunq: data analysis and machine learning in place of customer self reporting in due diligence",[94],"dynamic-customer-risk-rating",{"name":5,"anonymized":12,"country":6,"region":7,"industry":8,"wikidata":9},[97],{"name":5,"role":66},"As described in a 2022 ruling, Dutch neobank bunq assigned new private customers a \"regular user profile\" derived from data analysis of its customer base, instead of asking each customer about the purpose of the account, and then monitored transaction behavior to adjust that profile and raise the customer's risk scores when needed. bunq says it favors technology such as machine learning; the ruling mentions a machine learning model in transaction monitoring but does not show that machine learning produces the rating itself. In October 2022 the Dutch Trade and Industry Appeals Tribunal (CBb) ruled largely in bunq's favor in its dispute with De Nederlandsche Bank over this approach. In May 2025 De Nederlandsche Bank fined bunq EUR 2.6 million because it had not sufficiently followed up signals in four customer files it had itself identified as high risk; bunq has appealed, and the notice does not attribute the shortcomings to the data driven approach.",2022,[27],[29,102],"nl",[],[105,109,114],{"url":106,"title":107,"publisher":5,"date":108},"https:\u002F\u002Fpress.bunq.com\u002F219361-bunq-wins-appeal-against-dutch-central-bank-dnb\u002F","bunq wins appeal against Dutch Central Bank (DNB)","2022-10-18",{"url":110,"title":111,"publisher":112,"date":108,"archivedUrl":113},"https:\u002F\u002Fuitspraken.rechtspraak.nl\u002Fdetails?id=ECLI:NL:CBB:2022:707","ECLI:NL:CBB:2022:707, College van Beroep voor het bedrijfsleven, 18-10-2022, 21\u002F323 en 21\u002F1108","College van Beroep voor het bedrijfsleven","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20251011000746\u002Fhttps:\u002F\u002Fuitspraken.rechtspraak.nl\u002Fdetails?id=ECLI:NL:CBB:2022:707",{"url":115,"title":116,"publisher":117,"date":118,"archivedUrl":119},"https:\u002F\u002Fwww.dnb.nl\u002Fen\u002Fgeneral-news\u002Fenforcement-measures-2025\u002Ffine-for-bunq-b-v-for-insufficient-customer-due-diligence\u002F","Fine for bunq B.V. for insufficient customer due diligence","De Nederlandsche Bank","2025-08-25","https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20260608145106\u002Fhttps:\u002F\u002Fwww.dnb.nl\u002Fen\u002Fgeneral-news\u002Fenforcement-measures-2025\u002Ffine-for-bunq-b-v-for-insufficient-customer-due-diligence\u002F",{"level":49,"checkedAt":121},"2026-09-27","B","bunq-machine-learning-customer-due-diligence",[125],{"slug":94,"title":126},"Dynamic AML customer risk rating with machine learning",1794496906032]