Dubbed “the bank of the free,” bunq offers online banking anytime, anywhere. Through the bunq app, users can handle all their financial needs exclusively online, without needing to visit a physical bank. With more than 12 million customers and 8 billion euros’ worth of deposits made to date, bunq has become one of the largest neobanks in the European Union. Founded in 2012, it was the first bank to obtain a European banking license in over three decades.

To meet growing customer needs, bunq turned to generative AI to help detect fraud and money laundering. Its automated transaction-monitoring system, powered by NVIDIA accelerated computing, greatly improved its training speed.

“AI has enormous potential to help humanity in so many ways, and this is a great example of how human intelligence can be coupled with AI,” said Ali el Hassouni, head of data and AI at bunq.

Faster Fraud Detection

Financial fraud is more prevalent than ever, el Hassouni said in a recent talk at NVIDIA GTC. Traditional transaction-monitoring systems are rules based, meaning algorithms flag suspicious transactions according to a set of criteria that determine if an activity presents risk of fraud or money laundering. These criteria must be manually set, resulting in high false-positive rates and making such systems labor intensive and difficult to scale. Instead, using supervised and unsupervised learning, bunq’s AI-powered transaction-monitoring system is completely automated and easily scalable.

Bunq achieved this using NVIDIA GPUs, which accelerated its data processing pipeline more than 5x. In addition, compared with previous methods, bunq trained its fraud-detection model nearly 100x faster using the open-source NVIDIA RAPIDS suite of GPU-accelerated data science libraries. RAPIDS is part of the NVIDIA AI Enterprise software platform, which accelerates data science pipelines and streamlines the development and deployment of production-grade generative AI applications.

“We chose NVIDIA’s advanced, GPU-optimized software, as it enables us to use larger datasets and speed the training of new models — sometimes by an order of magnitude — resulting in improved model accuracy and reduced false positives,” said el Hassouni.

AI Across the Bank

Bunq is seeking to tap AI’s potential across its operations.

“We’re constantly looking for new ways to apply AI for the benefit of our users,” el Hassouni said. “More than half of our user tickets are handled automatically. We also use AI to spot fake IDs when onboarding new users, automate our marketing efforts and much more.”

Finn, a personal AI assistant available to bunq customers, is powered by the company’s proprietary large language model and generative AI. It can answer user questions like, “How much did I spend on groceries last month?” and “What’s the name of the Indian restaurant I ate at last week?”

The company is exploring NVIDIA NeMo Retriever, a collection of generative AI microservices available in early access, to further improve Finn’s accuracy. NeMo Retriever is a part of NVIDIA NIM inference microservices, which provide models as optimized containers, available with NVIDIA AI Enterprise.

“Our initial testing of NeMo Retriever embedding NIM has been extremely positive, and our collaboration with NVIDIA on LLMs is poised to help us to take Finn to the next level and enhance customer experience,” el Hassouni said.

Plus, for the digital bank’s marketing efforts, AI helps analyze consumer engagement metrics to inform future campaigns.

“We’re creating a borderless banking experience for our users, always keeping them at the heart of everything we do,” el Hassouni said.