Proving the Value of New Intelligence

Are you investing in intelligence, or just adding in more noise?

Fraud teams today are surrounded by opportunity. New data sources, be that device signals or behavioural analytics, all promise sharper detection and fewer false positives. But with every new feed comes a familiar question: Will this actually improve outcomes?

In reality, many institutions still rely on instinct and vendor promise when evaluating new intelligence. They commit to contracts before seeing real-world impact for their portfolio. They deploy new feeds without knowing how the insight complements or contradicts their existing rules. This can result in finding themselves locked into expensive solutions that add complexity, not clarity.

It’s time to rethink how we test, validate, and invest in fraud intelligence.

The Intelligence Investment Problem

Fraud leaders are continually under pressure to innovate, do more with less, and justify every pound of spend – be that across technology, operational costs or fraud losses. However, traditional evaluation methods can be expensive, slow to provide answers, and often don’t solve the questions that need to be answered.

The tried and tested approach of sandbox testing and pilot deployments are commonplace, but they often arrive with severe limitations:

• Limited Scope – Pilots often run on narrow datasets through down sampling, missing the edge cases or real-world complexity
• Operational disruption – Integrating new feeds, even on a temporary basis, can impact latency, integration and analyst workflows
• Delayed Insights – It can take months to gather enough data to assess impact, by which time fraud patterns may have shifted

Collectively, these can create uncertainty. This uncertainty results in institutions either over-investing in unproven intelligence or missing opportunities due to a lack of evidence. Both scenarios undermine the goal of doing more with less.

Industry approaches and considerations

Across the industry, there is growing recognition that fraud detection must evolvefrom reactive rule deployment to proactive intelligence validation. However, the approaches for this vary.

Some institutions rely on vendor-led pilots, which can lack transparency and fail to isolate the true impact of the new data. Business cases for investment often require internal validation of external analysis.

Others attempt internal A/B testing, but can struggle with tooling, governance and the ability to simulate at scale.

Some leading banks are building internal simulation environments, but these are often costly, resource-intensive and don’t fully replicate the live environment of decisioning.

What is consistent across all approaches is the need for following fundamental principles:

The Optimised Decision Engine (ODE) is Sopra Steria’s proprietary simulation and calibration tool designed to help financial institutions validate new fraud intelligence before integration. It enables structured, explainable testing of new data sources against historical fraud patterns, allowing smarter investment decisions and reducing the risk of costly, ineffective deployments

A Smarter way forward: Simulation before integration

At Sopra Steria, we believe there is a better way to approach this challenge. This is why we built ODE (Optimised Decision Engine) to help institutions simulate the impact of new intelligence before making long-term commercial commitments.

ODE enables a phased, structured approach to intelligence validation. It’s not about saying no to innovation, rather is it saying yes to the right innovation

A phased approach to intelligence validation

ODE begins by analysing the current fraud detection environment:

• Identifies underperforming rules and high-friction thresholds.
• Highlights detection gaps and false positive hotspots.
• Establishes baseline metrics: fraud catch rate, false positive ratio, analyst workload, and customer impact.

This creates a clear picture of where improvements are needed—and where new intelligence might help.

Objective: Establish a clear performance benchmark to identify where new intelligence can drive improvement

Once the bank appends the proposed data points to its historical transaction and fraud-labelled datasets, ODE can be re-run to simulate performance. This enables a true A vs. B comparison:

• A: Existing rulesets and detection logic without the new data.
• B: Rulesets enhanced with the appended attributes.

This side-by-side simulation allows institutions to:

• Quantify the standalone and combined impact of new data points.
• Identify whether the new intelligence improves precision, reduces noise or simply duplicates existing logic.
• Score each scenario across key metrics - fraud detection, false positives, analyst workload and customer friction.

But simulation alone isn’t enough. The real challenge lies in calibrating new data alongside existing attributes. Many institutions struggle to determine:

• which combinations of data points yield the best signal-to-noise ratio.
• whether new intelligence should act as a primary trigger, a secondary filter, or a contextual modifier.
• how to avoid overfitting or introducing bias when layering new logic onto legacy rules.

ODE addresses this by modelling multi-variable interactions, helping fraud teams understand not just the value of new data, but how to use it effectively.

Objective: Quantify the real-world impact of new data and calibrate it for optimal use within existing logic

Rather than deploying wholesale changes, ODE supports incremental rule refinement:

• Introduces new attributes into targeted rulesets.
• Optimises thresholds based on performance.
• Ensures every change is explainable, auditable and aligned to business goals.

This allows fraud teams to build confidence gradually validating each step before scaling.

Objective: Enable controlled, explainable enhancements to detection logic without disrupting operations

With simulation results in hand, institutions can make informed decisions:

• Proceed with integration if uplift is proven.
• Negotiate performance-based contracts with vendors.
• Decline feeds that add complexity without value.

This phase transforms procurement from speculative to strategic decision making.

Objective: Transform procurement into a data-driven process, ensuring only proven intelligence is adopted

Why does this matter?

In today’s environment of maximising the value in fraud prevention, fraud teams must be both innovative and accountable. Every new feed, new signal, new solution must be justified – not just in the promise of value but in the actual performance.

ODE empowers institutions to:

• make smarter investment decisions based on real-world actual outcomes.
• avoid costly integrations that don’t deliver.
• understand how new data works in context, not just in isolation.
• build trust with procurement, compliance and executive stakeholders.

This is not about rejecting new intelligence. It’s about validating it before it becomes part of your fraud strategy.

Final Thoughts

Fraud prevention is no longer just about detection. Key metrics are around precision, efficiency and trust. With ODE, institutions can move beyond gut feel and vendor hype, towards a model of intelligence validation that’s grounded in evidence and aligned to outcomes.

Don’t just add data. Prove its value and calibrate it wisely.

Find out how ODE can help strengthen your fraud defences, reduce false positives and respond faster to new threats