Just how can banks balance innovation with regulation and where might the greatest ROI come from as banks ramp up their adoption of AI? Those are just two of the hottest topics tackled by Olly Downs of Curinos when he sat down with RBI.
On ROI, he says that the most exciting returns are likely to come from AI solutions that solve operational challenges and drive sustainable growth. He joined Curinos in May 2023 as Chief Data Scientist to oversee the continued development of AI and machine learning within its solutions.
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41 US patents and 25+ years of AI expertise
Almost every press release relating to a senior hire in the sector claims that the appointment in question represents a strategic hire. But in Downs case, the release at the time was justified. When it comes to the merging sciences of machine learning, Downs has been the inventor on 41 US patents spanning machine learning, personalisation, location-based services and quantum computing.
Indeed, Downs published his first academic paper on generative AI as long ago as 1999 and so has had a ring side seat through multiple evolutionary stages of AI.
So, AI in the banking sector is far from a novelty and has been used to enhance fraud detection, in customer support and has been deployed in credit and insurance underwriting. The use of AI for chatbots is also not new but the quality of the technology has improved massively in recent years.
What is different this time is the challenge of bringing AI out of the lab and into a wider range of practical business environments while meeting the banking sector’s unique regulatory challenges. Another novelty dates back to around 2022 and the hype around ChatGPT. This served to heighten interest among consumers and reignited interest in AI from financial institutions. It also attracted the attention of regulators and governments.
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By GlobalDataCurinos’ mission is actually quite straightforward to summarise: it leverages proprietary data, decision tolls and AI to help its clients optimise their go-to-market decisions. Rather stating the obvious, the difficult bit is executing on that mission. And it does so for a huge number of prominent clients.
Curinos vast sector data visibility
Its 1,500+ clients globally include 46 of the top 50 US banks, the big six Canadian banks, over 800 US community banks and credit unions and 42 of the largest 50 mortgage lenders. That sort of range of clients gives Curinos insight into over $7trn in deposit data, $3trn in mortgage originations, and $9bn in marketing spend. Moreover, that volume of data allows Curinos to provide tools and insights that are uniquely representative of real-world financial behaviour.
Potential impact of regulation on AI
While most financial authorities have not issued AI regulations specific to banks as existing regulations address most of the possible risks, there remains the threat of unnecessary new regs.
“AI can be delivered without invoking the disruption of AI regulation. The biggest enablers as a result of AI in banking lie around service, around customer experience and customer engagement and in back-office automation. Most of the value lies in these areas, which don’t need to provoke the high or elevated risk levels of the EU AI act, for example,” says Downs.
“I think it’s an exciting future. We have the regulatory guardrails in place now, in Europe, the UK and in the US. we’ve been dealing with models and analytics based decisioning in banking for a very long time, and some of the structures already in place put some very healthy constraints and checks and balances around the type of decisioning and analytics that can generate bias. I actually believe we’re in quite good shape in the banking industry overall.
“Human relationships don’t scale very well. There’s a lot of cost in the bank, branch banker to customer relationship that can only serve today some of the highest value and most profitable customers. The real opportunity here in banking relationships with the customer is the ability to scale a highly engaged, mutually beneficial, high value relationship for the long term, using AI as an enabling technology.”
Optimising AI to maximise ROI
ROI from optimising AI strategy is most likely in three areas, says Downs. In the back-office, there remains scope to take friction out of operations in areas where there is manual and error prone processes. The second is in service and customer experience and taking the friction and cost out of customer engagement at scale, particularly digital customer service, online customer service, chat and voice customer service.
“And then thirdly, where you can really impact customer value lies in personalisation or in the commercial or business setting, sort of individualisation of experience and driving cross sell, upsell and long-term customer value.”
