In our second episode of 2026, we launch a new "quick take" series, delving deeper into key predictions flagged in our first episode of the year, "Banking Predictions 2026".

This first episode covers embedded finance, defining what we mean by the term, what has changed in recent years to suggest 2026 will drive increase activity, and what leading firms can (and should) do in response.

For our first episode of 2026, we've pulled together a select group of analysts from across the Global Data family to provide a taster or "amuse-bouche" following our long form predictions reports published at the end of last year.

We start with Doug Blakey, Editor of Retail Banker International (RBI), who will review some of the key events in 2025, and what that portends broadly for 2026.

We then move to Zil Bareisis, Head of Retail Banking at Celent, (now a Global Data company), to cover their top three predictions.

Blandina Szalay, a GD Banking & Payments analyst, will then delve a deeper into stablecoins specifically.

Last, and most definitely least, I, the host, Steve Walker, will comment further on what we expect within AI, Ecosystems, ESG and Digital Mortgages across 2026.

For our eleventh episode, we’re delighted to be joined by Giovanni Oppenheim, Director of Banking Solution, at Earnix. Earnix has a highly esteemed reputation in Insurance, and across the last seven years has made great strides in Banking,with a focus on pricing and personalization software.

Our conversation covers key challenges for incumbent lenders, what role different types of AI technology can play in addressing financial inclusion, decisioning,personalized pricing, competition from Buy Now, Pay Later (BNPL) and delivering key tenets within Consumer Duty in the UK. We also cover key forward lookingtrends around financial inclusion and fairness in lending.

Time stamps as follows:

0:00-05:00 – Introductions and Set Up

05:00 – Where is AI most effective in reshaping lending activities?

07:00 – What examples have we seen in Co-pilots Vs Gen AI for synthetic data?

08:30 – Examples of live Earnix case studies in Banking

09:30 – Metrics used to quantify AI improvements in lending

11:30 – How can AI assist financial inclusion

14:30 – How does incumbent bank credit approvals compare to BNPL providers

16:50 – Best examples of inclusive finance using Gen AI

20:00 – Best examples of Gen AI within Consumer Duty

23:30 – AI to manage credit risk amid increased uncertainty

27:00 – AI to increase agility and flexibility

28:00 – Conclusion and Wrap-Up