Around 40 percent of BBVA’s business customers in Spain have upgraded to the Android PoS terminal, which offers more functionalities than a traditional device. Its operation is intuitive and resembles that of a mobile phone. The main screen displays applications for business management and a payment option. Merchants can easily process returns and view their transaction history.

BBVA’s AI Factory, a center dedicated to developing artificial intelligence (AI) solutions, expands to Mexico and Türkiye in order to create products globally and leverage knowledge across the Group. With these new centers, BBVA aims to accelerate the adoption of AI-based solutions to improve customer experience and optimize internal processes. In addition, the bank has hired 230 AI experts, doubling the headcount of these ‘factories’ to more than 400 professionals.

Deep learning is a subset of IA within the broader concept of machine learning, so both concepts have a good deal in common, though also differences. Deep learning uses neural networks to draw complex conclusions in a human-like manner. Meanwhile, machine learning is able to make predictions from patterns, although it requires a higher level of human input.

David Puente has been heading up Client Solutions at BBVA for five years. It is an area which, in his words, “drives growth and innovation” at BBVA. He is tasked with developing the value proposition for private and corporate clients, as well as non-clients. His remit includes overall product design, customer experience, relationship and distribution model, as well as building new capabilities and exploring new business opportunities.

Machine learning has no end of business applications. Consumers and citizens stand to benefit from this technology, which lets them manage their financial health, streamlines their digital processes, or makes their online experience smoother across multiple platforms, among other uses. A basic premise has emerged from this boom: there is nothing mysterious or magical about artificial intelligence (AI), and nor should it seem so. For more complex algorithms, data scientists develop tools to help us understand how they make their decisions.

Artificial intelligence is increasingly mainstreamed in society and used to make decisions that affect people´s lives in areas such as  their workplace or  the justice system. This presents the challenge of biased algorithms, which are mainly caused by biases or distortions in the training data. Proposals are already in place that caution against bias and seek to mitigate its harmful impact on society.

Opening imagen created with Midjourney (AI)