False positives are a cost centre

In summary:

• False positives in fraud detection waste resources, frustrate customers, and increase compliance risk.
• Measuring fraud success should focus on maintaining customer trust, not just blocking transactions.
• Efficient fraud operations rely on data-driven, explainable systems that reduce noise and improve outcomes.

Time to redefine fraud ops efficiency

How do you measure fraud success in your organisation? Is it by what you stop, or by how many customers you frustrate?

Fraud prevention has too often been treated like a numbers game. More alerts = better protection, right? Well, not anymore.

When 90–95% of your alerts are false positives, you’re not just generating noise, you’re building cost. You’re frustrating customers. You’re stretching your analysts. And you’re risking regulatory exposure.

The question isn’t how many fraudsters you stop. It’s how many good customers you push away in the process.

The scale of the problem

While there’s no clear industry-wide data that focuses on what a good false positive rate looks like, it’s not unusual to see institutions struggling under false positive rates of 95% or more. That’s up to 19 wasted alerts for every one genuine threat. So, what’s the result?

• Lost analyst time
• Broken customer journeys
• Rising operational costs
• Compliance risk

This isn’t just inefficiency however, it’s a strategic liability. Let’s explore the three main costs in more detail:

Operational Drag

I spend more time proving people aren’t fraudsters than catching the ones that are - Senior Fraud Analyst, UK Bank

Fraud teams are overwhelmed. Fatigue sets in. Risk increases. Even with the risk in automation, over 70% of flagged cases still need some degree of manual intervention. This slows down response times and risks staff burnout.

Customer trust and attrition

Image this situation… A long-time customer of the bank, loyal and high value, books a holiday flight. Their payment is blocked, and they lose the flight at the price they wanted. No explanation provided to them. After 30 minutes on hold, they abandon the call. The next day, they move their account relationship to a competitor.

Anecdotally, fraud teams tell us that they’ve seen customers lose trust after blocked transactions. Sometimes switching to another credit card or even abandoning the bank altogether. It doesn’t take hard data to understand the damage done when that trust is broken.

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This graphic is a horizontal sequence of five purple circles, each containing white text.

These circles are connected by mathematical symbols: three plus signs and one equal sign.

The layout visually represents a cause-and-effect chain, leading to a final outcome.

The text in each circle are as follows:

Circle 1: Transaction attempted

Circle 2: Transaction flagged

Circle 3: Call abandonment due to wait times

Circle 4: Frustration with outcomes

Circle 5 (Final Outcome): Account churn

Regulatory Exposure

False positives are now a regulatory risk. Frameworks like Consumer Duty and PSD2/3 mean unjustified friction may be classed as customer harm. The regulators are now increasingly asking questions around:

• How many genuine customers are being disrupted in the name of fraud prevention?
• What processes are in place to assess and reduce false positive rates?
• Are the detection rules explainable, proportional and regularly optimised?

Not being able to answer these questions adequately and convincingly opens the risk of formal scrutiny or reputational damage.

So why does this matter?

We are at a crossroads in fraud operations. The threat landscape is intensifying, be that in real-time payment scams, or in AI-generated identity fraud. Internal capacity has never been more stretched, risking poor customer experience.

Against this backdrop, we see four important themes:

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The graphic is divided into four vertical sections, each structured as a two-part block. The left side consists of a dark blue background with a heading and an icon. The right side consists of a light grey background with a descriptive sentence and a right-pointing arrow, indicating progression or emphasis.

Section 1:

Heading (Dark Blue): Fraud volumes are increasing

Description (Light Grey): Faster payments and digital onboarding increase both speed and risk.

Section 2:

Heading (Dark Blue): Budgets are not keeping pace

Description (Light Grey): Fraud teams are being asked to do more with less.

Section 3:

Heading (Dark Blue): Boards demand ROI

Description (Light Grey): Leadership wants efficient, effective detection — without added customer friction.

Section 4:

Heading (Dark Blue): Regulators are watching

Description (Light Grey): Friction is now seen as a compliance and fairness issue — not just an operational one.

Put simply, the “alert on everything” mindset is no longer fit for purpose. Institutions must evolve from noise-driven detection, to precision-led prevention.

The smarter way forward

The good news is that you don’t have to rip and replace your entire fraud infrastructure. But you do need to take a serious look at how your rules are performing and whether those rules are aligned to your company’s needs, appetite and regulatory obligations. Here are four principles around what an efficient fraud operation should look like.

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This graphic is divided into four vertical sections each with a title on the left and a set of bullet point on the right explaining key concepts. 

Section 1: Data-led optimisation

• Transition away from static, intuition-based rules and adopt a data-first approach.
• Understand which rules are generating value and which are just creating unnecessary work.

Section 2: Precision over volume

• Not all anomalies are going to be a risk.
• Focus on precision; use signals and patterns to target actual fraud behaviours, not the outliers.

Section 3: Explainable decisioning

• AI/ML are really powerful, but only if they are understandable.
• Your fraud system needs to deliver recommendations that compliance teams and auditors can both interpret and justify.

Section 4: Human Analysts on high-value work

• Free up your fraud team to focus on complex cases and emerging typologies.
• This is where their expertise delivers the most value.

Reduce false positives, and boost performance

At Sopra Steria, we’ve developed ODE – the Optimised Decision Engine – to address this exact challenge.

ODE is a rule optimisation solution that uses advanced analytics to identify and recommend improvements to your existing fraud detection logic. It’s not about replacing your current systems, it’s about making them smarter, faster and fairer.

What does ODE deliver?

• Reduction in false positives without compromising your fraud capture.
• Improved analyst efficiency by reducing alert volumes and intelligence.
• Explainable outputs to meet regulatory expectations.
• Human-readable recommendations to allow rules to be actioned and deployed at speed.

ODE operates as an augmentation layer, meaning that it integrates with your current tech stack, across decisioning engines, case management and even legacy rulesets.

Rethinking success in fraud ops

In the past, success in fraud prevention has focused on maximising alert volumes and aggressive intervention. However, that approach has now materially shifted. Success today is focused on:

• Customers completing their journeys safely and without interruption.
• Analysts focusing on real threats, not false alarms.
• Teams being able to explain, and crucially evidence, every decision that they make.
• Operational costs improving as opposed to spiralling out of control.

Summed up, this means – reducing fraud without harming trust.

Final thoughts

Fraud prevention is non-negotiable. But doing it in a way that burns out teams, frustrates customers, and fails compliance expectations? That’s no longer acceptable. It’s time to rethink how we measure success.

Are you measuring fraud success metrics by what you stop, or by how many customers you frustrate?