European neobank bunq has turned to generative AI to help detect fraud and money laundering. Specifically, the bank’s automated transaction-monitoring system, powered by NVIDIA accelerated computing, is greatly improving its training speed.
“AI has enormous potential to help humanity in so many ways. 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.
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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. This means 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. This results in high false-positive rates and makes such systems labour 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. This accelerates data science pipelines and streamlines the development and deployment of production-grade generative AI applications.
“We chose NVIDIA’s advanced, GPU-optimised software as it enables us to use larger datasets. It speeds the training of new models, sometimes by an order of magnitude. This results in improved model accuracy and reduced false positives,” said el Hassouni.
AI across bunq
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. 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.”
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By GlobalDataFinn, a personal AI assistant available to bunq customers, is powered by the company’s proprietary large language model and generative AI.
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 optimised containers, available with NVIDIA AI Enterprise.
“Our initial testing of NeMo Retriever embedding NIM has been extremely positive. Our collaboration with NVIDIA on LLMs is poised to help us to take Finn to the next level and enhance customer experience.”
AI also helps analyse 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.”
bunq reported its first full year of profitability in January. Customer numbers now exceed 12 million with deposits in excess of €8bn.
Last month, it revealed details of its upgraded user-facing AI assistant, Finn, my making it fully conversational. bunq has also rolled out its worldwide one-time activation travel insurance, in partnership with embedded insurance provider, Qover.
