AI for Large-Scale Banking Infrastructure

Overview

The “AI for large-scale banking infrastructures” axis aims to develop scalable and explainable artificial intelligence models for complex and large-scale banking systems.

The goal is to address big data challenges in critical environments by integrating AIOps approaches for monitoring, incident detection, and performance optimization.

The work also focuses on system resilience, real-time decision-making, and the sustainability of AI models in highly regulated environments.

Research Axis Leads

Albert Bifet

Co-Scientific Director of the FinAI Lab

Albert Bifet is a professor of AI and director of Te Ipu o te Mahara—The AI Institute at the University of Waikato, as well as a professor of Big Data at Télécom Paris. As a specialist in machine learning on data streams, he develops large-scale adaptive and responsible AI systems. An open-source pioneer, he co-founded Apache SAMOA and co-authored Machine Learning for Data Streams (MIT Press). An active member of the international scientific community, he also contributes to AI standardization initiatives.

Mariam Barry

Co-Director of Operations of the FinAI-Lab

Mariam Barry holds a Ph.D. in computer science from the Institut Polytechnique de Paris (2022), specializing in “online AI for data streams” as part of a CIFRE thesis at BNP Paribas. A graduate of ENSTA ParisTech and École Polytechnique, she is Head of AI Research and IT Innovation at the BNP Paribas Group. An expert in data science, machine learning, big data, and streaming data, she is also a member of the Board of Directors of the Institut Polytechnique de Paris and a lecturer on AI and Big Data at École Polytechnique. Her work aims to bridge the gap between scientific research and industrial innovation for the management of transaction flows on a global scale.