Rethinking ROI: Evaluating the value of AI in risk modelling

As more organisations start to consider AI-driven risk modelling, one of the biggest hurdles is quantifying the return on investment. Traditional ROI frameworks often fall short when it comes to capturing the wide-ranging value these solutions bring. This can leave stakeholders stuck between understanding AI’s strategic importance and needing to justify its cost with tangible metrics.

It doesn’t help that the benefits of AI go far beyond straightforward cost savings. Enhanced risk identification, stronger regulatory compliance, faster decision-making and improved customer outcomes are all part of the picture.

This piece outlines how to assess both the measurable financial returns and the less tangible—but no less important—additional advantages of AI in risk modelling.

Why standard ROI models fall short

The conventional ROI formula—gains minus costs, divided by costs—works well for predictable technology investments. But AI in risk modelling doesn’t follow such linear patterns.

Why not?

Its impact is often realised across multiple areas, in indirect ways, and over longer periods of time. This makes building business cases or assessing success more complex.

AI risk models can reduce credit losses, streamline operations, improve compliance, and lead to better customer experiences. Some benefits are easy to quantify. Others, like missed fraud or avoided regulatory issues, are harder to measure but still critical.

And sometimes, improvements arrive in leaps, rather than following a steady curve—making them even trickier to factor into standard ROI calculations.

Perhaps most challenging of all: how do you measure the value of something that didn’t happen, like a loss successfully avoided?

Quantifying the financial impact

Despite the complexity, there are clear, quantifiable gains that AI delivers.

One of the most direct: lower credit losses. Advanced risk models consistently outperform traditional approaches, helping lenders better distinguish between high- and low-risk applicants. One building society saw an 18% improvement on its buy-to-let models and a 4% improvement on residential ones—leading to significant potential reductions in bad debt.

Fraud prevention is another area of measurable impact. In one case, AI models identified over 86% of fraud cases by reviewing just 10% of applications—vastly reducing losses and manual effort. Another lender noted “significant reductions in bad debt” following implementation, with long-term improvements forecasted across the board.

Automation also plays a major role. By reducing the need for manual reviews, AI enables teams to process more applications without increasing headcount—supporting growth while keeping risk in check.

These tangible benefits form a strong foundation for ROI calculations—and in many cases, may be enough to justify the investment on their own.

Looking beyond the numbers

Still, there’s much more to the story.

Take regulatory compliance. With the FCA recently raising concerns that regulatory burden may be slowing AI adoption, the need for explainable, well-governed models is higher than ever. One bank highlighted the confidence it gained from detailed model reporting, helping it manage oversight expectations more easily and reduce the cost of compliance.

AI also supports faster, more consistent decision-making. One lender noted rapid benefits and the ability to “enforce intuitive behaviour on key variables,” speeding up risk decisions and reducing inconsistencies.

This can have knock-on effects for customer experience too—reducing false positives in fraud checks and enabling more personalised risk assessments. In one case, AI was used to identify customers at risk of switching, allowing the business to act earlier and retain more customers.

There’s also a cumulative benefit to early adoption. Institutions that build internal expertise through repeated model use can unlock efficiencies and insights over time. This transfer of knowledge becomes a long-term asset in itself.

The time factor: overlooked in ROI

When calculating ROI, time to value is often ignored. But in the context of AI, it’s essential.

Traditional model development can take months, even years. Each day in development delays potential benefits.

Some institutions have found ways around this. One building society deployed new AI models in hours instead of weeks, capturing value quickly while others remained in development. In real terms, shortening implementation by six months could add hundreds of thousands in returns—more than covering the initial cost.

It doesn’t end at deployment. AI models need to be maintained and refined, especially in fast-changing markets. Platforms that support rapid iteration give organisations a key advantage, allowing them to adapt quickly and retain performance.

As one senior modeller put it, the flexibility to adapt and collaborate with model developers helped ensure the benefits were realised as quickly as possible.

Common objections—and how to respond

Even with strong ROI arguments, some resistance is inevitable. Here are some common concerns and how to address them:

• “We can’t measure all the benefits.”
  True, but proxies and estimates can help. One lender found the actual value exceeded their expectations—sometimes the full impact isn’t visible until after implementation.

• “The investment is too large.”
  Framing the discussion in terms of returns, not costs, is key. A fivefold return over three years is a compelling business case—especially when the cost of doing nothing may be higher.

• “Our current systems work well enough.”
  That may be true now, but “good enough” quickly becomes outdated. One lender improved its models significantly despite recent enhancements, showing that AI can still deliver value on top of strong existing systems.

• “We don’t have the right skills in-house.”
  With today’s platforms, deep technical knowledge isn’t a barrier. One bank implemented AI models without expanding its data science team, thanks to tools designed for business users.

• “We’ve tried AI before and it didn’t work.”
  Technology has moved on. The difference often lies in the approach, not the tools. Explainability, ease of use, and integration all matter—and may not have been part of the earlier attempt.

• “Regulatory risks are too high.”
  On the contrary, regulators now expect more advanced risk modelling. Flexible, explainable AI solutions can help institutions meet these expectations—not fall short of them.

Final thoughts

AI risk modelling brings both immediate and long-term value. While the financial benefits are clear, the strategic and operational gains are just as important. Understanding all aspects—from faster implementation to improved regulatory confidence—gives organisations a fuller picture when assessing ROI.

The business case goes beyond the balance sheet. For those willing to invest wisely, AI offers significant, sustainable advantages in a competitive and fast-moving environment.