To calculate the return on fraud analytics, compare the intervention’s attributable, realized or expected benefits with its full incremental cost over a defined period and against a credible estimate of what would have happened without it. State the formula: a benefit-cost ratio is not the same as a net-return percentage. The result is only as defensible as its loss baseline, counterfactual, attribution and cost boundary.
Define what you are evaluating
Set the unit of analysis before calculating anything: a particular analytics program, business process, portfolio or fraud type. Specify the control or analytics investment, the fraud exposure it addresses, the population and geography, who bears each cost, and the evaluation period. A result for one process or a public-sector program should not be presented as the return for an entire organization or for fraud analytics in general.
Also identify whether the calculation is an ex ante estimate, made to inform an investment decision, or an ex post assessment of outcomes observed after deployment. Label modeled savings as modeled; do not describe potential exposure as money actually saved.
Build a baseline and a counterfactual
Estimate the loss or risk in scope
Start with a fraud-loss estimate whose coverage and uncertainty are clear. Where data and resources allow, a representative sample, investigation and careful extrapolation can provide an evidence-based estimate. If comprehensive loss measurement is not feasible, document the historical or comparable-program data and risk assessment used instead. OECD guidance treats loss measurement as useful for establishing a baseline and recognizes alternatives when a full exercise is impractical: OECD, Evaluating, Updating and Monitoring Anti-Fraud Strategies (2026).
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Estimate what would have happened without the intervention
The counterfactual is the expected outcome for the same population and period in the absence of the intervention. Compare the intervention with a credible historical, comparison-group or modeled scenario, and explain the assumptions. The UK Public Sector Fraud Authority’s framework defines approximate savings by comparing predicted reduced fraud and error with a counterfactual over a defined period: Fraud Prevention Savings Framework.
Account for factors that can change the comparison, such as fraud prevalence, deployment delays, changes in detection or reporting, investigation capacity and displacement of fraud to another channel. If those factors cannot be measured directly, show how they affect the estimate in a sensitivity range rather than hiding them in a single point figure.
Identify benefits without double counting
Separate the kinds of value the intervention may produce. Record the basis, realization and confidence for each amount; do not add theoretical exposure to benefits already counted as avoided loss.
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- Prevented loss: payments or other losses credibly estimated to have been stopped because of the intervention, relative to the counterfactual.
- Detected or recovered funds: money recovered after fraud occurred. Keep recoveries separate from prevention, and account for any overlap with loss estimates.
- Operational savings: avoided response or investigation expense, or reduced manual review, when the reduction is attributable and the resource saving is realized or can be valued defensibly.
- Wider outcomes: resilience, public trust or other effects that matter but cannot be reliably monetized. Report these separately rather than forcing them into a monetary ratio.
OECD guidance identifies monetary benefits such as increased revenue, recovered assets and penalties, while noting that significant qualitative benefits may not translate into budget savings. Its 2026 guidance cautions that “ROI typically captures only monetised impacts and should therefore be interpreted alongside broader evidence on non-financial outcomes.” OECD, 2026.
Count the full incremental cost
Use costs attributable to the intervention and within the stated scope. Make shared costs visible and explain how they are allocated. Include both implementation and ongoing ownership over the evaluation period—not just the software or model purchase.
- Software or license costs, or model development; computing and infrastructure.
- Data acquisition, preparation and integration.
- Analyst and operations time, training, tuning, monitoring and model-risk oversight.
- Case review, investigation and false-positive handling.
- Customer friction or other downstream effects, when they are measurable and attributable.
Some investigation expense may be a cost of operating the intervention even when the intervention also avoids other investigations. Include both sides where relevant, without netting or counting the same labor saving twice. OECD’s analytics framing includes analytics and investigation costs in the denominator of its cost-benefit approach: OECD, Analysing the Costs and Benefits of Anti-Fraud Interventions (2019). A 2015 article on fraud analytics likewise highlights total ownership cost, the wider organizational impact of fraud, and the utility of detection and investigation; its historical loss statistics should not be treated as current: Baesens, Van Vlasselaer and Verbeke (2015).
Choose and label the calculation
There is no single universal meaning of “ROI” in the sources. Name the calculation and show its formula so readers can distinguish a benefit-cost ratio from a net-return percentage. Use consistent monetary units and the same evaluation period for benefits and costs.
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| Measure | Formula | What it communicates |
|---|---|---|
| Net benefit | Attributable benefits − incremental costs | The monetary surplus or shortfall over the defined period. |
| Benefit-cost ratio (often called an ROI ratio) | Attributable benefits ÷ incremental costs | Benefits per unit of cost. A value above 1:1 means estimated benefits exceed costs under the stated assumptions. |
| Net-return percentage | (Attributable benefits − incremental costs) ÷ incremental costs × 100% | Net benefit expressed as a percentage of cost. This is not the same as a benefit-cost ratio. |
For example, if an evaluation estimates £120,000 in attributable benefits and £100,000 in incremental costs over the same period, the benefit-cost ratio is 1.2:1 and net benefit is £20,000. The net-return percentage is 20%. These are alternative descriptions of the same assumptions, not separate benefits.
OECD describes cost-benefit analysis as more comprehensive and ROI as a simplified ratio that generally captures monetized impacts alone. The UK Public Sector Fraud Authority states, “For an intervention to be considered cost effective, it would need to have a ROI ratio greater than 1:1.” That threshold belongs to its public-sector framework; it is not proof that every private-sector project above 1:1 is worthwhile or that every project below it should be rejected. UK Public Sector Fraud Authority, 2026.
Measure operational performance alongside ROI
A monetary ratio does not show how the system creates its results or what it misses. In particular, false positives consume investigator time, while a high hit rate does not by itself establish good overall performance: it can result from reviewing only a narrow group of alerts while substantial fraud goes undetected.
Pair the financial calculation with operational measures such as:
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- alert volume and the share of alerts reviewed;
- confirmed fraud rate among reviewed cases, and value-weighted yield;
- review time and investigator workload;
- loss coverage, detection delay and estimated missed fraud, where the data support them; and
- customer impact, where it can be measured.
OECD describes hit rate as the share of selected potential cases that are actual fraud and discusses the resource value of avoiding benign investigations. OECD, 2019. These measures help explain efficiency and coverage; they do not replace the counterfactual or establish causation on their own.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use public estimates as context, not as your baseline
Published figures can indicate the scale of fraud in a specified setting, but they cannot substitute for an organization’s own in-scope loss estimate. The UK Home Office’s second edition of its economic and social cost report estimated £14.4 billion as the total cost of fraud against individuals and businesses in England and Wales in financial year 2023/24: £9.2 billion affecting individuals and £5.2 billion affecting businesses. The total excludes public-sector fraud. It is not a directly addressable market or an organization-specific baseline. UK Home Office, 2026.
For businesses in England and Wales, that report estimated £3.7 billion in defensive expenditure and £507 million in direct fraud financial loss for the period it describes. The direct-loss estimate excludes opportunity costs and excludes reimbursements to avoid double counting. The report cautions that rare high-loss incidents and undetected or undisclosed fraud may not be captured. These categories should not be combined or repurposed as the expected return for an analytics deployment. UK Home Office, 2026.
Other public-sector evidence also has a limited role. The UK framework reports approximate prevention ROI of 21:1 and reactive-measure ROI of around 5:1 from analysis of fraud-loss and workforce-reporting data. The reactive figure excludes court proceedings and wider societal harms that continue until detection. These are results from a specific public-sector analysis, not forecasts for a commercial analytics investment. UK Public Sector Fraud Authority, 2026.
Similarly, the U.S. GAO reported that one-third of 24 surveyed federal agencies lacked regular fraud monitoring and evaluation, and half did not regularly adjust efforts based on evaluation results. The report describes its 2023 survey; these findings concern evaluation practice, not the effectiveness or ROI of a particular analytics product. U.S. GAO, 2026.
Make the result useful for an investment decision
Present the assumptions with the result so a decision-maker can see what would change it. At minimum, report:
- the program or process, fraud type, population, geography and period;
- baseline coverage, uncertainty and counterfactual method;
- which benefits were measured, modeled or realized, and how attribution was established;
- the included costs, treatment of shared costs and any important exclusions;
- the exact formula, net benefit and benefit-cost ratio or ROI measure used;
- a sensitivity range for prevalence, effectiveness, deployment timing, fraud displacement and investigation capacity; and
- non-monetized outcomes and operational performance measures.
For a build-versus-buy or vendor comparison, evaluate each option against the same historical or controlled evaluation set. Ask for evidence on fraud-loss coverage, false-positive workload, assumptions about prevented loss, deployment and ongoing staffing costs, monitoring needs, performance drift, integration readiness, explainability and governance, and time to deploy. No named vendor capability or price follows from the public-sector estimates above.
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