In summary:
- Most fraud strategies over-focus on detection, ignoring the true cost of decisions like false declines, customer friction, and lost revenue.
- Every fraud decision carries two costs: fraud losses and the often-unseen commercial impact of the control itself.
- Decision economics helps organisations balance risk and value, creating smarter, more precise controls that improve outcomes and competitiveness.
Why the variable that most companies optimise for is the wrong one, and what it costs them.
Many companies focus on the wrong variable when it comes to fraud optimisation, resulting in hidden costs and missed opportunities. Typically, fraud functions centre their efforts on metrics such as the amount of fraud prevented, avoided losses, detected cases, and activated rules. These indicators seem appropriate, as they reflect the main objectives of fraud teams.
Yet, a crucial question often goes unasked: What is the actual cost of each fraud-related decision, and does it generate or diminish value? This question lies at the heart of decision economics and its significance extends well beyond the fraud team itself.
Every fraud decision has two costs
Whenever a transaction is denied, an account frozen, or a customer subjected to increased friction, two distinct costs emerge. The first is obvious: the repercussions of failing to intercept fraud, including losses, operational strain, recovery efforts, and regulatory implications. Fraud teams are designed to minimise these, and generally do so effectively.
The other cost is less apparent and, in many organisations, barely measured: the expense of the decision itself. This includes lost revenue, higher servicing load, customer attrition, and diminished trust. Both costs are interdependent, shaped by the logic that guides each decision.
When organisations discuss fraud economics, they usually refer to the first type of cost. The approach outlined here demands consideration of both costs simultaneously, and, most importantly, assessing whether their balance is intentional or coincidence.
The wrong optimisation variable
Most fraud management systems are geared towards detection, which is logical given their design and performance criteria. However, focusing solely on detection introduces a fundamental flaw: it treats every intervention as equal. Blocking a £12 purchase is measured the same as blocking a £12,000 transaction. Likewise, turning away a genuine customer during onboarding is viewed no differently from stopping a fraudster.
This measurement framework fails to recognise the economic differences between decisions. Over time, it fosters an environment that prioritises sensitivity, without accountability for commercial impacts. Rules become stricter in response to losses, each change seeming prudent, but collectively causing greater harm to revenue and customer experience than is formally acknowledged.
The focus narrows from "Are we effectively catching fraud?" to simply "Are we catching fraud?". The economic perspective is lost in the gap between these two questions.
Decision Value: a different frame
There is a more insightful way to approach this. Every fraud decision, be it acceptance, rejection, escalation, or review, has an expected value. This depends on the population being assessed: the likelihood a signal is fraudulent, potential losses if so, anticipated revenue if not, and customer value implications in either scenario.
This method is akin to expected value optimisation, widely used in credit, but rarely applied with rigour in fraud management. By using this framework, several previously hidden truths become clear:
- The most costly decisions are often those that block legitimate customers at crucial moments, rather than the ones that let fraud slip through. Declining a new account or a significant transaction can destroy more value than it safeguards if fraud risk is low.
- Fraud’s economic impact varies. Protecting a high-margin channel with minimal loss risk requires a different strategy than securing a low-margin one facing persistent threats. Applying identical controls to both is imprecision at scale.
- Friction is not linear. The first added step in a customer journey may have a small effect, the second a greater one, and the third could trigger high abandonment rates. Neglecting compounded friction effects leads to underestimated costs.
The question is not whether controls are effective. It is whether they are proportionate, and whether proportionality is something you are actively managing or something you are assuming.
Why decision economics stay hidden
The main reason decision economics rarely features in fraud governance is structural. Fraud losses are visible, reported, and acted upon, showing up in the profit and loss statement and attracting leadership attention. Accountability is clear.
Conversely, the cost of excessive control is dispersed and slow to surface. Declined customers rarely complain about their declines, they simply go elsewhere, quietly, over time. Attrition is often blamed on product or pricing issues, not the friction that prompted departure. Revenue losses from blunt decision-making are real but rarely traced back to the original logic.
This imbalance in visibility perpetuates unchecked control drift. Under-controlling is immediately evident in financials, while over-controlling only becomes noticeable when the effects are substantial.
What deliberate decision governance looks like
Transitioning from detection optimisation to decision economics requires three elements that many organisations lack or have yet to implement robustly:
- Measurement: You cannot manage what you do not track. This means monitoring not just fraud outcomes, but every decision (accepted, declined, referred) and their downstream commercial impacts. Achieving this demands a comprehensive data infrastructure that connects fraud, product, and customer systems, which are often siloed.
- Governance: Decision economics is not solely the domain of the fraud team. The trade-off between friction and protection is fundamentally a business issue, requiring input from revenue owners, product leaders, and risk functions. Too often, governance structures keep these conversations apart, preventing proper commercial weighting against control objectives.
- Willingness to revisit inherited assumptions: Decision frameworks accumulate over time. Rules created in response to threats years ago may still be active, affecting outcomes even after the landscape has changed. Legacy logic carries costs, and treating it as default is misguided. The real question is: Does this decision still earn its place?
None of this advocates for higher fraud acceptance; rather, it calls for greater precision in trade-offs, making decisions deliberately rather than by default.
The competitive dimension
One final point, often persuasive with leadership, is that organisations applying economic precision to fraud decisions gain a structural edge over rivals who do not. A company able to approve customers its competitors reject, with similar or lower loss rates, builds a compounding advantage. Serving high-value segments with reduced friction and confidence in risk pricing enables growth in markets others deem too complex.
This is why fraud decisioning is a competitive lever. Not in spite of its risk function, but because of it. The discipline needed to get the economics right creates a control environment that fosters growth: transparent, calibrated, and governed by intention rather than instinct.
The organisations that will outperform in this space are not the ones with the tightest controls. They are the ones with the most precise ones.
Decision economics in fraud is not novel. However, its implementation is lagging, as it requires integrating data, governance, and commercial accountability in ways most fraud teams are not currently designed for.
That gap is the opportunity.