The impact that Data Science, Machine Learning and Artificial Intelligence are making on our everyday lives.

In this episode, David Sutton, Director of Innovation at Featurespace, delves into the journey of his career, which transitioned from Astrophysics to data science. He explores how his encounter with machine learning and its wide-ranging possibilities led him to become part of a London-based start-up called Faculty. This company had a unique focus on enlisting academics without prior experience in data science, matching them with enterprises seeking solutions for data-driven challenges. Following a placement with EasyJet, he eventually became a member of the Featurespace team.

Established in 2008 as a spin-out of the University of Cambridge, Featurespace maintains a steadfast commitment to making the world a safer place to transact, a mission that aims to benefit both its customers and their clients. Featurespace builds solutions for banks and financial institutions, empowering them to proactively address the risks of fraud and financial crime. At the heart of these solutions lies its cutting-edge machine learning proprietary technology— Adaptive Behavioral Analytics. Designed to comprehensively grasp an individual’s transactional patterns, Adaptive Behavioral Analytics is able to identify in real-time fraudulent activities, as these manifest as anomalies in the customer’s standard behavior.

David explores the evolution of his responsibilities over time, a transformation greatly influenced by the company’s remarkable annual growth rate of 50%. Presently positioned as the head of research and innovation, David oversees the research initiative, a role he fulfills by maintaining a strong connection to both customer needs and the engineering department’s activities. The research endeavors are strategically aimed at maintaining cost efficiency while concurrently enhancing the experiences of current customers.

David finally discusses three project:

  • The Automated Deep Behavioral Networks – now in production, which uses deep recurrent neural networks to build AI features.
  • The PETs challenge – To develop a privacy-preserving solution that is capable of efficiently generating high-utility machine learning models.
  • Generative AI – the latest AI development on which David and his team will focus on in the next few years.

And what makes Featurespace a great place to work.