If you’re considering AI in risk modelling software like Archetype, you might be wondering how to quantify the return. ROI calculations often fail to capture the multi-faceted value that advanced AI risk modelling solutions deliver. This can leave decision-makers caught between recognising the strategic necessity of AI and needing to justify the investment in concrete terms.
The challenge is compounded by the fact that AI's benefits extend far beyond direct cost savings. Improved risk detection, enhanced regulatory compliance, accelerated decision-making, and better customer experiences all contribute to the value proposition.
With my CFO hat on, I’ll help you understand both the direct financial returns and the harder-to-quantify strategic advantages, giving you a comprehensive approach to evaluating and communicating the full value of these investments.
The challenge of measuring the value of AI in risk
ROI calculations follow a simple formula: gains minus costs, divided by costs. This works well for conventional technology investments where inputs, outputs, and timeframes are predictable.
AI in risk modelling, however, defies this straightforward calculation.
Why is that?
Its value creation is multi-dimensional, often indirect, and frequently realised over extended timeframes. This complexity creates challenges for those attempting to build business cases or assess implementation success.
The first challenge is capturing the full spectrum of benefits. An AI risk model might simultaneously reduce credit losses, improve operational efficiency, enhance regulatory compliance, and deliver better customer experiences. Some benefits translate directly to the bottom line, while others create value that traditional accounting might struggle to recognise.
Similarly, unlike traditional approaches where benefits follow a predictable curve, AI implementations can deliver sudden step-changes in performance after periods of seeming stagnation.
Perhaps the most challenging problem: how do you measure losses that never occurred? The absence of a negative event, while valuable, is inherently difficult to quantify with precision.
|
📖 Related article: No-code AI: what it means for your risk analytics team |
The direct financial benefits of AI in risk modelling
Let's start with what's easiest to measure: the direct financial benefits of AI risk modelling solutions. From my perspective as a CFO, I can tell you these are the tangible returns that will make your finance team sit up and take notice.
First up: reduced credit losses.
AI models consistently outperform traditional approaches in risk assessment accuracy. Organisations can significantly reduce default rates and associated losses by identifying high-risk applicants with greater precision. Take Newcastle Building Society, which achieved an impressive 18% uplift on its buy-to-let risk models and over 4% improvement on its residential models using Archetype. This equates to substantial potential reductions in bad debt for the same level of application throughput.
Fraud prevention is another substantial financial return. Virgin Money's implementation of Archetype for fraud detection achieved a predictive power of 93%, offering a relative uplift of 31% over their incumbent approach. This allowed them to identify over 86% of fraud cases by reviewing just the top 10% of applications, significantly reducing fraud losses and manual review costs.
Similarly, AI-powered automation reduces the need for manual reviews, allowing organisations to process more applications with fewer resources. Secure Trust Bank saw "significant reductions in bad debt" after becoming the first UK lender to deploy Archetype's models, with the implementation now projected to provide "substantial reductions in bad debt over the course of its deployment."
The result? Increased business volume without corresponding increases in risk. As Nick Martin, Head of Analytics at Virgin Money Credit Card puts it: "The Archetype fraud model is the best of the lot, giving us an exceptionally strong weapon in the fight against financial crime in the application space."
These direct financial benefits provide the foundation of your ROI calculation. They're quantifiable, defensible, and often substantial enough on their own to justify investment in AI risk solutions.
But they're only part of the story…
Beyond the financials
To truly understand the value of AI risk modelling solutions, we must look beyond the balance sheet.
Let's look at some of the hidden benefits.
Regulatory compliance continues to be complex and costly. The FCA also recently highlighted concerns that compliance requirements might be stifling AI investment in banking. Their March 2025 announcement of roundtable discussions with industry leaders shows regulators are working to balance innovation with compliance.
The good news is, AI solutions with strong governance and explainability features significantly reduce this burden. Secure Trust Bank discovered this advantage with Archetype, noting that "the in-depth model reporting gave us total confidence that the models are fully reliable and have the stability and robustness that we expect." This transparency translates to smoother regulatory interactions and reduced compliance costs.
Likewise, decision speed and consistency improve dramatically with AI. Traditionally, risk assessments involve significant judgment calls and manual reviews, creating inevitable inconsistencies and bottlenecks. One client experienced this first hand, noting they were "impressed by the ease of use, the rapidly-realised benefits and the ability to enforce intuitive behaviour on key variables." For customers, this means faster decisions without arbitrary variations in outcomes.
What about customer experience?
This might surprise you, but AI risk models significantly enhance customer satisfaction. Organisations create smoother customer journeys by reducing false positives in fraud detection and offering more personalised risk assessments. Virgin Money made the most of this capability to identify customers likely to switch providers, enabling targeted retention strategies that improved customer relationships.
There's more to consider here, too. Early adopters gain cumulative advantages through continuously improving models and institutional knowledge. As Nick Gibbs from Hitachi Capital Consumer Finance observed, Archetype "has given us food for thought in how we approach our modelling activity," – highlighting the advantage of remaining at the forefront of risk technology.
Knowledge development within your organisation shouldn't be overlooked, either. Teams working with Jaywing transfer valuable skills and insights. This intellectual capital is a lasting asset that continues to pay dividends long after the initial implementation.
These indirect benefits may not be easy to quantify, but they're no less real. In many cases, they deliver greater long-term value than direct financial returns.
The time value of AI in risk
When calculating ROI, most organisations focus exclusively on financial benefits versus costs. But they're missing something crucial: the time dimension.
Let me explain.
The speed at which you can implement and derive value from AI solutions dramatically impacts overall returns. Traditional linear modelling approaches typically require months—sometimes years—of development time before delivering tangible results. Each day spent in development represents lost opportunity cost.
This is where no-code AI platforms shine.
Newcastle Building Society discovered this advantage when they deployed Archetype, creating complete models in just hours rather than the weeks or months traditionally required. This meant that they could begin capturing benefits almost immediately while their competitors remained stuck in development cycles.
Consider the maths.
A traditional risk model development project might take 6-12 months before implementation. With no-code platforms, this timeline often shrinks to weeks. If a solution delivers £100,000 in monthly value, accelerating implementation by just six months represents £600,000 in additional returns—often exceeding the entire solution cost.
But there's another angle to consider.
AI risk models aren't static assets—they require ongoing monitoring and refinement. No-code platforms like Archetype significantly reduce these maintenance costs through automated processes and intuitive interfaces. Secure Trust Bank has experienced this benefit through their three-year engagement with Jaywing, where regular monitoring and calibration ensure "the models are always optimised."
And here's something that few consider: The speed of iteration creates compounding returns. When models can be refined quickly in response to changing conditions, organisations maintain optimal performance. This agility is particularly valuable in volatile markets where risk factors evolve rapidly.
Dr. J. Serradilla, Senior Data Modeller at Newcastle Building Society, summarised this advantage perfectly: "Archetype's flexibility provides us with a range of model implementation options... and we are working with Jaywing to create the most advantageous approach for us to ensure the benefits are realised ASAP."
The time value of AI implementation might be one of the most significant yet overlooked factors in ROI calculations.
Common objections and how to address them
When presenting the ROI of AI risk modelling solutions, you'll inevitably face skepticism. Let's tackle the most common objections:
"We can't measure all the benefits."
True, but that doesn't mean we should ignore them. While some benefits resist precise quantification, they're no less real. When Hitachi Capital Consumer Finance implemented Archetype, they discovered value beyond what they'd initially calculated. As Nick Gibbs put it:
"Not only did Jaywing promise uplifts through the Archetype software, they delivered them too."
The solution? Create reasonable proxy measurements for indirect benefits and clearly label them as estimates. This approach acknowledges uncertainty while still capturing the full value spectrum.
"The investment is too large."
This objection often stems from viewing AI as a pure technology cost rather than a business investment. Frame the discussion around return multiples rather than absolute cost.
For example, if a £200,000 investment delivers £1 million in value over three years, the focus shifts from expenditure to the 5x return. Virgin Money's experience demonstrates this principle—their implementation identified 86% of fraud cases by reviewing just 10% of applications, creating returns far exceeding the initial investment.
"Our current systems work well enough."
This might be the most dangerous objection of all.
Competition is ramping up. Today's "good enough" becomes tomorrow's competitive disadvantage. Newcastle Building Society recognised this when they sought to understand whether AI techniques could yield additional benefits despite having recently enhanced their customer risk scores.
The results? An 18% uplift on their Buy to Let models and 4% improvement on residential models—substantial improvements they would have missed by maintaining the status quo.
"We don't have the right skills in-house."
This concern has become less relevant with the rise of no-code AI platforms. Secure Trust Bank didn't need to build an extensive data science team to implement AI risk models. As Mike Abraham noted, Archetype's "in-depth model reporting" gave them "total confidence that the models are fully reliable"—without requiring specialised expertise.
The key is selecting solutions designed for business users rather than technical specialists. Look for platforms that empower your existing risk analysts to build and maintain sophisticated models.
"We tried AI before and didn't see results."
Previous disappointments often result from implementation approaches rather than the technology itself. Matthew Jones from Nationwide highlighted this distinction when impressed by "the ease of use, the rapidly-realised benefits and the ability to enforce intuitive behaviour" in Archetype—features that might have been missing in previous attempts.
When facing this objection, focus on how current approaches differ from past initiatives. Emphasise governance, explainability, and integration capabilities that address previous shortcomings.
"The regulatory risks are too high."
This concern flips the reality. The greater regulatory risk today lies in not implementing advanced analytics. Regulators increasingly expect sophisticated risk management capabilities that only AI can deliver.
Dr. J. Serradilla from Newcastle Building Society recognised this, noting Archetype's "flexibility provides us with a range of model implementation options" to ensure benefits while maintaining compliance.
By addressing these objections directly with evidence and real-world examples, you'll build a more compelling case for AI investment in risk modelling—one that acknowledges concerns while clearly demonstrating the overwhelming value proposition.
Making the business case for AI in risk modelling
When it comes to making the business case for AI in risk, Archetype offers a proven path forward—one that delivers substantial uplifts without requiring extensive technical expertise or lengthy implementation timelines. The technology addresses the key challenges that have historically limited AI adoption in risk functions: explainability, governance, and implementation complexity.
Your next step is straightforward.
Request a personalised ROI assessment for your organisation. We'll work with you to analyse your current risk operations, identify specific improvement opportunities, and build a comprehensive business case tailored to your unique situation.
Within weeks—not months—you could be deploying models that transform your risk capabilities while delivering measurable returns across multiple dimensions.
Contact us today at [email protected] to schedule your assessment and join forward-thinking institutions like Virgin Money, Nationwide, and Newcastle Building Society in realising the full potential of AI-powered risk modelling.