In the fight against financial crime, scalable and reliable machine learning is paramount. Netlight has supported a Munich-based fintech in transforming its data science capabilities, advancing a unified modelling approach and optimising ML pipelines to enable safe, repeatable deployments across diverse international markets — shortening time to value and laying the foundation for sustained global growth.
The client is a Munich based company that empowers financial institutions such as banks and payment gateways to combat financial crime. It uses explainable machine learning techniques to boost operational efficiency with it's fraud detection models and is capable of processing large volumes of transactional data with precision and reliability.
As the client expanded internationally, the data science team needed to preserve the quality of their custom‑built machine learning solutions while shifting to a scalable, automated delivery model across diverse customer contexts. They had to support a wide range of use cases, data maturities, and regulatory constraints without compromising accuracy or explainability.
Netlight joined to enable the client’s data science team. The developer setup was hardened with comprehensive testing, reproducible environments, and extended monitoring to enable safe, repeatable deployments. We advanced a unified modelling design centred on a variational autoencoder (VAE) applicable to both anomaly detection and false‑positive reduction, building on the client’s existing concept to create a solution that generalises across diverse customer contexts. By introducing large-scale performance optimisations, such as GPU-enabled training and smarter dataset streaming, Netlight significantly reduced training time while maintaining model accuracy and stability. Proving this, we leveraged the enhanced ML pipeline for prospective customers, demonstrating scalability and shortening time to value. The work resulted in a comprehensive tech strategy and roadmap to sustain growth.
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