How bunq uses AI

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.

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.

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.