AI Solutions

In artificial intelligence, getting a prediction right or detecting complex patterns does not always mean understanding the underlying events that occurred to lead to that result. Understanding causal relationships is the core idea addressed by the doctoral thesis of Jesús Renero Quintero, who is part of the AI adoption team at the bank. The thesis was developed within the industrial PhD program in Data Science promoted by the University of Navarra and BBVA. The goal is to design AI that is more reliable and transparent.

In a previous article, we explored how BBVA is scaling its Machine Learning capabilities with a new architecture based on AWS technologies. This second article details how we have tested with four initial use cases to identify reusable ML patterns, standardize operational workflows, and design extensible templates that accelerate ML delivery while maintaining governance and flexibility across teams and business domains.

BBVA is continuing to scale its Machine Learning capabilities with a new technology architecture integrated into ADA, the bank’s global data and AI platform based on AWS technologies.