We carry this video interview – with an accompanying outline of the conversation, with a fintech and investment industry business figure and experienced executive. 

In this episode of the Basis Point Channel, François Gilardoni, CTO of Globas Group, delivers a clear-eyed take on where AI is genuinely transforming finance - and where expectations still exceed reality. He is leading the way with Catalyst, a proprietary AI and agentic platform that changes how decisions in venture capital, mergers and innovation are made.

The use cases for AI is a topic that WealthBriefing has raised a number of times (see cases here and here). Judging the return from AI in terms of higher revenues, profit margins, client satisfaction and news business wins is clearly vital. Given the billions of dollars/equivalent being spent, getting strategy right is a topic that will keep C-suite executives awake at night. It is also necessary to understand how AI can improve user experiences in financial services, reduce risks, and handle labour-intensive chores and free up valuable time for other tasks.

The editors are pleased to share this interview and we hope readers find it interesting. As ever, if you want to comment and get into the debate, please email the editors at tom.burroughes@wealthbriefing.com and amanda.cheesley@clearviewpublishing.com


Drawing on his background as a scientist turned technology leader, Gilardoni explains that AI’s impact in finance is less about flashy innovation than about access to infrastructure, data quality, and execution.

While large financial institutions have already embraced generative AI at scale, smaller and regional banks lag behind - not out of denial, but because of legacy systems, limited expertise, and unclear success metrics. The result: many AI projects fail not for technical reasons, but due to poor understanding, governance, and training.

Gilardoni highlights concrete use cases already delivering value, from smarter call-centre routing to faster credit decisions, document analysis, and investment workflows that blend traditional models with AI-driven data aggregation. The real competitive edge, he argues, will not come from using the same large language models as everyone else, but from combining them with proprietary data and domain knowledge.

Looking ahead, he sees the rise of agentic AI systems - collections of specialized agents working together - as a turning point for strategy and decision-making, provided humans remain firmly “in the loop.” He closes with a reminder drawn from the example of Renaissance Technologies: innovation often comes not from financial orthodoxy, but from interdisciplinary thinking, experimentation, and a willingness to learn from failure.