From rule decay to precision fraud detection: why banks need a smarter way to optimise

In summary:

• Outdated fraud rules are quietly weakening banks’ defences by failing to keep up with evolving criminal tactics and customer behaviours.
• Rule decay leads to increased false positives, missed fraud, and wasted analyst time, making fraud prevention less effective and more costly.
• Precision fraud detection uses data, AI, and continuous optimisation to improve rule performance without needing to replace existing systems.

How outdated fraud rules are quietly undermining your defences - and what to do about it

In the continual battle against fraud, the greatest threat we face may not be the fraudster - it could be your own outdated rules.

While criminals are continually innovating, too many institutions still rely on rulesets built on intuition, left unchanged for years and often never measured for performance. This quiet erosion, known as ‘rule decay’, drains budget, damaging customer experience and leaving gaps wide open for exploitation by the fraudsters.

The positive news is that you don’t need to rip everything out. However, you do need to be honest about what’s no longer working and take the appropriate actions.

What is ‘rule decay’?

Rule decay is the gradual decline in the effectiveness of your fraud rules, and subsequently your fraud control environment. It happens when rules are no longer reflecting real customer behaviours, emerging fraud patterns or current regulatory expectations.

Legacy rules are like anchors – they offer stability, but unchecked, they drag you down.

Industry best practice would suggest that fraud detection programmes conduct rule reviews monthly to stay aligned with evolving fraud patterns. However, the reality within financial institutions is that rulesets become ever more complicated. Reviewing the effectiveness of those rulesets on a regular basis is a significant challenge, and therefore erosion of effectiveness drives rule decay.

see transcription

This graphic is a line graph illustrating how the effectiveness of a rule changes over a 12-month period. The graph is titled "Example Rule Decay Timeline".

Horizontal axis (x-axis): represents time in months, labelled from 1 to 12.

Vertical axis (y-axis): represents performance as a percentage, ranging from 0% to 100%.

Lines on the Graph:

Red line – rule performance (%): starts high at around 95% in month 1. Gradually declines over time. Ends at approximately 10% by month 12. This shows that the rule becomes significantly less effective over time.

Orange line – false positives (%): b egins at about 5%. Increases steadily throughout the year. Reaches around 70% by month 12. Indicates that the rule increasingly flags legitimate activity as fraudulent.

Black line – missed fraud (%): s tarts near 0%. Rises gradually, surpassing the orange line. Ends slightly higher at around 75% by month 12. Suggests that more fraudulent activity goes undetected as the rule decays.

The graph demonstrates the deterioration of rule performance over time. As the rule ages:

• Its ability to correctly identify fraud decreases.
• The number of false positives and missed fraud cases both increase, highlighting the need for regular rule updates or replacements.

How did we get to this position?

Historically, fraud rules were written by experienced analysts using instinct, experience and limited data. But we are now in a much different era of real-time transactions and real-time fraud, with synthetic IDs and deepfakes introducing new threats. Also, the fraudsters’ use of AI is shifting the advantage back to them.

This position was highlighted in the case of a UK bank. The bank had an account velocity rule in place whereby transactions were blocked when more than 10 transactions occurred in 10 minutes. The outcome was that this rule was blocking 20% of genuine users because of their preference for mobile-first behaviour. At the same time, a fraud ring using 40 accounts in parallel was never in scope of their ruleset.

The hidden cost of stale rules

Outdated rules are actively working against your fraud strategy.

False positives

Globally, false positives cost online merchants an estimated $443bn annually, which far outpaces actual fraud-related losses. It’s clear that the operational, reputational and customer impacts of false positives carry huge hidden costs. This reinforces the need for making smarter, adaptive rule optimisation a strategic business focus.

Higher fraud losses

Rules that don’t evolve can’t react to new threats, or prevent tomorrow’s scams. The fraud of today is not always going to be caught with the thresholds that were built for yesterday’s threat.

Strained analyst resources

When performance in fraud detection starts to drop, analysts can be left to tune the rules manually, often without the data and tools to assess why something is failing.

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This chart is a square split into four boxes. It shows how well a fraud detection system works, and how often it wrongly flags good activity.


On the vertical axis we have fraud detection – from low at the bottom to high at the top. On the horizontal axis we have false positives – from low on the left to high on the right.


Four boxes are as follows:

• Top left box states target state. It has a high fraud detection and low false positives.
• Top right states overtuned: risky experience. It has high fraud detection, but also high false positives.
• Bottom left states legacy: blind spots. It has low fraud detection, low false positives.
• Bottom Right states untuned: costly inefficiency. I has low fraud detection, high false positives.

 

 

The solution isn’t to incrementally increase the number of rules in your systems. It’s smarter rules that are needed. Rules which are continually optimised, explainable and reactive to new threats.

“Fraud Detection is no longer about the rule itself. It is now about how often the rule is evaluated, optimised and aligned to Business Risk“

What does precision look like?

Precision fraud detection means moving beyond static rules to a dynamic data-driven approach.

• It critically involves the continuous analysis of rule performance, using real-time feedback loops to fine tune decision making.
• AI supports threshold tuning, adjusting the sensitivity and thresholds based on evolving fraud patterns and contextual risk.
• Advanced scenario simulation allows fraud teams to test and validate changes before deployment, reducing risk and operational noise.
• Crucially, every change is backed by full a audit trail and explainability, ensuring confidence for both internal governance and regulatory scrutiny.

see transcription

This graphic shows two different ways of managing fraud detection rules, side by side.

Top row – traditional rule lifecycle:

• Step 1: Rule Writing – A rule is created.
• Step 2: Deployment – The rule is put into use.
• Step 3: Forget – The rule is left running without updates or checks.

This process is basic and limited, with no review or improvement after deployment.

Bottom row – precision deployment:

• Step 1: Data – Start with good data.
• Step 2: Performance Insight – Understand how well the rule works.
• Step 3: Optimisation – Improve the rule based on insights.
• Step 4: Test – Try the rule before full use.
• Step 5: Deploy – Put the improved rule into action.

This process is smarter and more careful, aiming for better fraud detection and fewer mistakes.

Enter ODE – Your rules, made smarter

Sopra Steria’s Optimised Decision Engine (ODE) doesn’t replace your systems – it sits at the intersection of your current systems to enhance what you already have in your environment.

ODE works by intelligently identifying underperforming rules/thresholds, and recommending new rules or variables based on real-world outcomes from your data. It fine-tunes thresholds dynamically, considering all variables known to it (for instance channel, model risk score). All of the recommendations are fully explainable giving both analysts and regulators confidence in the decisioning process.

Crucially, ODE is system agnostic. It can work seamlessly with any fraud stack and decisioning systems, whether 3rd party vendor solutions or in-house platforms, with no-need for disruption or costly “rip and replace”. Designed with analysts in mind, ODE doesn’t replace human expertise – instead it enhances it. By delivering actionable data driven insights, it frees up analysts to focus on other activities to provide a holistically stronger fraud ecosystem.

Questions you should be asking

So how do you know if rule decay is a systemic problem in your organisation? Well, here are five key questions that you need to be asking yourselves and honestly answering:

• When were our critical fraud rules last updated?
• What is our current false positive ratio (FPR) – and is it going up or down?
• Do we measure rule performance over time?
• How long does it take to test and deploy a rule change?
• Is our fraud strategy aligned across operations, compliance and technical estate?

Why this matters at a Board level?

This isn’t just about stopping fraud. It is about enabling operational efficiency, regulatory defensibility and customer trust. Boards need to stop measuring success solely by fraud losses refunded. The absence of fraud and the absence of friction are KPIs in their own right.

Final thoughts

If your fraud rules aren't evolving, your defences are eroding. Fraud isn’t standing still, and neither should your strategy. Rule decay is silent but dangerous. But precision detection, powered by data, explainability and human insight, can put control back where it belongs.

You already have a ruleset. Let’s optimise it and turn it into your greatest fraud prevention asset.