AI in Banking – Reduce Costs and Boost Efficiency

Costs keep rising, but the ability to absorb them has all but vanished, especially in bread-and-butter businesses like lending. Cost-to-serve ratios are under scrutiny, and firms are increasingly looking to consolidate vendors, streamline straight-through-process transactions and accelerate onboarding.

At a macro level, efficiency is becoming an expectation, and business leaders are feeling the heat. If you're not utilizing advanced technologies like AI to enhance your productivity and reduce your costs, the board of directors and the broader market will want to know why.

So, if you want to cut your costs, you need to invest in the latest tech. But which areas of spending should your firm target first?

Control at a premium

According to "The Harmony Gap," the top three costs faced by financial services organizations stem, broadly speaking, from a lack of control over cyberthreats, fraud and compliance. It's easy to see why, as these firms handle highly sensitive data that they can't afford to expose to malicious activity or human error.

Any gaps in control increase the likelihood of that exposure and can introduce all manner of financial, reputational and regulatory risks. Meanwhile, cybercriminals and fraudsters are becoming more sophisticated and using their own AI tools to penetrate traditional controls. Plus, new regulations from Basel IV to DORA make it more challenging to stay compliant.

The level of investment needed to strengthen control and reduce the risks of external threats and regulations keeps growing and eating into margins. But at the same time, there's that other major cost to consider: the price of inefficient internal operations.

Notably, "The Harmony Gap" survey has identified regional differences in the impact of inefficiencies on the balance sheet. In contrast with firms in the U.S. and the U.K., organizations in Singapore lose more money to operational inefficiencies than they do to cyberthreats, fraud or regulatory and compliance issues.¹

A likely reason for this disparity is the sheer speed of growth of the Asia Pacific (APAC) market. In lending, for example, firms in APAC operate at a fast pace but still on a relationship-driven basis with many manual workflows, particularly for trade finance, bilateral lending and regional syndications.

Although regulatory regimes across Asia are evolving, the emphasis in the region has historically been on serving customers quickly rather than driving deep automation or rigorous control. Firms tend to be smaller, too, and so typically less subject to scrutiny than top-tier organizations.

But as explicable as it may be, the $33 million annual cost of operational inefficiencies in Singapore - compared to $22 million in the U.S. and $21 million in the U.K. - has to come down. More to the point, it highlights a need for the radical transformation of systems and processes.

Barriers to transformation

For one in five organizations across the region, improving operational efficiency is the top priority for strategic investments in transformative technology. But in many cases, the complexity of longstanding processes - not just internally but across the financial services market - can make transformation easier said than done.

Again, let's take lending as an example. Loan onboarding and servicing operations remain document-driven with manual covenant tracking, bespoke borrower requests and fragmented deal terms. And in syndicated deals, a lack of standardization from one firm to the next makes processes harder to automate.

In the future, AI agents will help digitize credit agreements and process transactions in compliance with their terms. But given the heavily manual nature of existing operations, it's going to take time for firms to implement tools that will automate the lifecycle of complex loans.

Over the years, a large bank will have built hundreds of different processes and multiple integrations between legacy applications. There will be approval processes in place not only for compliance, but also to protect the organization.

Modernizing these frameworks and incorporating AI to increase automation can be a painful process in itself and, for a core business like lending, a challenge that's tempting to delay.

But despite the potential trials of transformation, can your organization afford the increasingly expensive status quo of running and maintaining legacy systems? In the FIS survey, 19% of firms identify high operational costs as the number one challenge they face with their current financial technology.

Impacts of technology

For financial services firms especially, transformation doesn't just hold the potential to slash overhead. In the survey, nearly half (49%) of financial institutions, compared to just 33% of nonfinancial institutions, invest in financial technology because of competitive pressure and market trends, as well as for cost reduction.

More than any other sector, the financial services industry appears to recognize that wholesale transformation is no longer optional.

Given the market's demand for speed, transparency and compliance, we see our clients not just replacing out-of-date systems, but also modernizing their entire operating model with the latest technology to improve scalability and ease cost pressures.

Clearly, there are risks to manage, too. When it comes to putting money to work, financial services firms need to strike a fine balance between protecting themselves and their customers and increasing their margins.

Financial transformation can help check both boxes, and as technology continues to advance, there will be greater opportunities to eliminate the manual processes that drive up costs and risk.

There are certain processes, such as the approval of high-value or complex loans, that banks have intentionally designed to be manual. But the better that AI agents become at making decisions, reviewing documents and completing other key tasks, the more of the lending lifecycle it will be possible to automate.

Effective AI-driven automation also relies on the ability to access and reuse data from across the lending lifecycle. Consequently, financial services firms are also utilizing technology like data lakes to consolidate their data, and AI tools to cleanse, reconcile and format it. That way, they can turn static information from credit agreements and agent notices into structured data for use in loan decision making and processing.

Integration between systems is equally critical to getting the best from technology and reducing operational overhead. A single platform for the lending lifecycle, from loan origination and credit assessment to loan servicing, improves automation and removes the costly, error-prone need to manually transfer data between systems.

Strategies for adoption

So, you need to automate and integrate to reduce your everyday expenditure. But what about the costs of adopting advanced technologies?