"There's this conversational aspect (to AI) that has been very attractive to many people to say, ‘Wouldn't it be great if I can talk with my bank?’ But I actually just want my bank to know me. We want our banks to understand what makes us, us. What are our goals, what are our ambitions, where we want to invest, who we want to invest with, how we want that investment to progress.”

While generative artificial intelligence (Gen AI) has dominated discourse in banking and finance tech over the past year, it is only a narrow slice of what banking data intelligence can achieve. With the technological strides we are witnessing with AI, there is an opportunity to democratize banking capabilities and knowledge to deliver much broader benefits to the customer.

AI as a dual force

At the recent FT Live Global Banking Summit, a pervasive topic was developing a better understanding of consumer data in the retail and commercial banking space. In this context, banks are viewing AI as an enabling tool.

There is some debate about whether AI’s maximum impact lies in creating cost and operational efficiencies or in fundamentally transforming customers’ relationship with banks. In our view, the two are not mutually exclusive and there is a tremendous opportunity here to get them both right.

AI can be harnessed to achieve operational efficiencies in areas such as credit decisioning, risk management and fraud. At the same time, by analyzing customer transaction data and spending history, banks can develop a deeper understanding of behavioral patterns and inferences about where they will spend their money next.

Banks and financial providers are now looking at putting transactional information into a broader cause-and-effect framework to create a richer, more engaging customer experience. Known as causal AI, this form of AI can help create a richer customer experience, while promoting greater transparency in how customer data is deployed.

The growing potential of causal AI

Causal AI focuses on analyzing transactional data to make inferences about underlying behavioral patterns and predicting future needs. While AI’s customer engagement potential via the conversational aspect of GenAI has attracted much attention, causal AI deploys an “under the hood” approach to understanding customer habits.

With causal AI capability, banks can get meaningful insights about a customer’s spending history, preferences and investment decisions, which in turn allows them to offer services, financial prompts and recommendations that will be the most beneficial to that specific customer.

There are simple applications of the technology that could greatly improve the financial wellbeing of customers. For example, a banking app could proactively offer alerts for the best easy-access savings rates in real time, giving customers the opportunity to move their money to an account offering higher interest rates.

Causal AI can also prove instrumental in resolving one of the key challenges in implementing AI in banking – ensuring transparent processes and building consumer confidence. Rather than operating in a “black box” environment that may leave customers wary or unaware of how their data is being used, banks can create a “glass pipe” around their decisioning and credit models and risk assessments.

By providing clear explanations on how decisions are made based on transactional data, causal AI can enhance transparency in processes, demystify the use of new technology and build trust among consumers that their data is being used effectively and responsibly.

Who stands to benefit most?

The question of who stands to benefit the most from the practical applications of AI is a complex one. Traditional banks have the advantage of vast historical datasets, but face challenges with legacy systems. Fintechs and neobanks on the other hand, lack this breadth of data but are able to leverage agile architectures and rapidly integrate multiple data sets from diverse sources.

In the end, success will depend not just on size or speed, but on the organization’s operating model and its ability to leverage insights and integrate them seamlessly into customer interactions.

Looking ahead

In conclusion, there is immense potential to transform both banking operations and customer engagement through the thoughtful use of AI. As some of these applications grow in adoption and become more attuned to customer interactions and behavior in the next 12-24 months, regulation will play a bigger part, so establishing trust will be crucial.

Ultimately, AI done right can help the industry get to a more transparent, financially inclusive and relevant banking model for individuals. Our report, "Are We There Yet? The Reality of AI Use in Banking and Financial Services" provides a deeper exploration of AI's current role and future potential in banking.

A&M Managing Director, Pierre Legrand joined Revolut UK CEO, Francesca Carlesi, and Zopa Bank CEO, Jaidev Janardana to discuss AI's dual role in enhancing operational efficiencies and transforming customer relationships at the FT Live Banking Summit. Watch the conversation below: