How AI Will Turn the Back Office into a Strategic Driver for FinServ

Mention the transformative power of AI, and many people’s minds turn to futuristic scenarios where robots eventually run the world free from human influence. However, Nate Castro, GM of Financial Services at Invisible Technologies, says that for FinServ enterprises, the reality is more mundane and much more immediate. 

“AI will transform financial services very quickly, but not in the way many people expect,” he explains. “And the real potential for impact is likely in the back office.”

“While it may not be as sexy as many people think, those enterprises that get it right could transform everything about how they operate, turning what’s currently treated as a line item into a strategic driver for business success.” 

“But those who don’t and let others get the jump could soon find it difficult to compete at all.”

The Commonalities of the Global Financial Services Sector

Castro says it’s difficult to speak of a common financial services sector, given its sheer size. In 2023, the industry was worth $28.1 trillion or roughly 20% - 25% of the entire global economy. Within this, there is a range of businesses whose functions, customers, jurisdictions, and their missions seem to share very little in common.

“It’s difficult to compare a hedge fund’s priorities with those of a retail bank or an insurer’s priorities with an asset manager’s,” Castro notes. “They exist for different purposes, perform different functions, have different risk tolerances, and serve different audiences.”

However, Castro observes that, while there might be vast differences in form and function, most FinServ enterprises also share some common denominators. Most notably, they all tend to operate in a strongly regulated and ‘zero error’ environment, where decisions need to be made based on a high level of due diligence.    

“Whether it’s a retail bank assessing a home loan application, an insurer analyzing risk factors, or a hedge fund researching business fundamentals, financial services businesses need to make careful decisions that weigh up risk based on evidence, or data.”

“That means, by their very nature, they’re exceptionally process-driven,” he observes. “And that could give them a powerful advantage in the age of AI.”

The Low-Hanging Fruit for FinServ Companies

But while businesses in the financial services sector may be process-driven, that doesn’t mean their processes are efficient. 

In fact, a 2024 Deloitte Study into retail banks found that many were being hampered by reliance on legacy systems, manual processes and operational silos. These were serving as a drag on many businesses, leaving them vulnerable to competition from digital competitors. 

It is a similar scenario in other financial services fields. One comprehensive study found that insurers were plagued by inaccessible data, manual entry, and poorly conceived attempts at automation (many of which neglected the human elements). There has also been a lack of process innovation in investment banking firms, with analysts still building their financial models using spreadsheets.

Meanwhile, another Deloitte study found that financial services organizations’ compliance costs had been spiraling since the Global Financial Crisis of 2008, adding further processes and reducing capacity for innovation. 

Castro says that transforming these processes by taking them apart and working out which tasks are best performed by humans, and which could be completed more efficiently by AI, is an obvious and easy way for financial services companies to save serious money.

“The world has changed a lot in the past 20 years but the processes used in many financial services enterprises haven’t really,” Castro observed. “Even though we’ve seen practices such as outsourcing take hold, they haven’t yet fundamentally changed the processes themselves. They’ve just tended to look at ways of performing the process more cheaply.”

“When there is a lack of innovation in any function, it means organizations tend to see it as a fixed cost that needs to be managed,” he continues. “However, given advances in AI, this no longer should be true with many of their processes—if it ever was.”

Turning the Back Office into a Strategic Driver

Castro argues that when it comes to transforming the way back offices operate, simply making their processes more efficient is the tip of the iceberg. What excites him more is the potential for financial services organizations to turn their back offices into strategic business drivers. 

“By their very nature, financial services enterprises generate an enormous amount of proprietary data. Until recently, they haven’t really been able to do much with it because it has been so disparate and unwieldy and the juice hasn’t been worth the squeeze.”

Castro notes that recent developments in AI have changed this so that enterprises can actually tap into it relatively easily. In doing so, they could give themselves actionable insights to drive strategic decisions, optimize operational efficiency, and create new value propositions. 

“If harnessed properly, the data generated by the back office could be used to transform everything from customer service and marketing through to risk management, product development, and strategic decision-making,” he says.

For an example of how this could be done, Castro points to JP Morgan, which has integrated AI into many parts of its operations, including developing a customer-facing investment advisor known as IndexGPT. It has also deployed AI across functions ranging from risk management and fraud prevention through to customer service. 

JP Morgan’s embrace of AI has reportedly involved more than 900 data scientists, 600 machine learning engineers, and a $2 billion investment in cloud-based data centers to support AI deployment. In late 2023, the bank announced its AI tools were already generating revenue. 

In some jurisdictions, however, FinServ enterprises are even further down the AI path. In the Republic of Korea, the leading financial services organization by valuation is Kakao Bank, an AI-rich spin-off from social media platform Kako that offers users short-term deposits, loans, credit cards, stock trading, and overseas transfers.

In China, Ant Group-owned Alipay uses AI for almost all its operations, from detecting fraud to customer service and personalization. Founded in 2014, it now boasts 1.3 billion users around the globe and, by some measures, is the second-largest financial services corporation in the world, behind only Visa.

“This same thing will happen in other markets too,” Castro notes. “FinServ enterprises really have two options: they can use their proprietary data to be the ones driving disruption or they can become the victims of it.”

Getting From Today to an AI-First Future

Castro says the most challenging step for many financial services enterprises on this path is simply getting started.

“Many FinServ enterprise leaders think that, when introducing AI, you’re looking at overhauling the whole organization overnight.” 

The reality is that you can often get enormous gains by starting small and focusing on just one or two processes, optimizing them, and using the insights to inform other parts of the business”

In fact, an HBR article revealed that many discrete AI projects cost between just one and five million dollars. However, the impact of embarking on something of this size could have broad implications for the business as a whole.

Castro, also notes that one thing some organizations overlook is that AI is not a standalone solution, but one that must be complemented and maintained by humans.  

“When combined with humans, AI has the power to radically transform the way financial services businesses operate. What’s more, it can do it quickly, efficiently, and relatively cost-effectively - providing new ways of doing things almost immediately,” he concludes.