Sooner or later, finance asks two questions: what is fraud costing us, and what are we getting for the people we pay to stop it? Both come down to the same discipline, which is measuring the full cost of fraud and then measuring your response against it. This post puts the two together, starting with company-level costs and working down to the individual analyst.
Count every cost, in one unit
The total cost of fraud goes well beyond chargebacks. A fraud study found that the total cost of fraud now exceeds $5 for every $1 of direct loss, at roughly $5.13 in the US and $5.23 in Canada. The multiplier has more than doubled in a decade, up from about $2.40 in 2016. Most of that gap sits in costs that never show up on a chargeback report.
To find your own multiplier, pick one unit and hold it across every line item. If you express fraud losses as a percentage of revenue, express review costs, lost sales, and tooling the same way. Flat currency amounts work too. Consistency matters because the goal is the combination of choices that keeps the total lowest, not the one that makes a single line look best.
Start with direct losses and lost sales
Direct losses are the visible part. They include fraud chargebacks and their fees, refunds issued to head off chargebacks, fraudulent replacement orders, and reward program abuse. This is revenue that left through fraudulent activity.
Lost sales are harder to see. They are the legitimate orders you cancelled or declined because they looked risky. No manual process or automated model is perfect, so some good customers get turned away. To size the damage, log every customer who complains about an incorrect cancellation, then check whether those customers bought again after the rejection. Their purchasing patterns show how many rejected customers were real.
Many rejected customers never call and never return, and for them, stay conservative. Pull a list of rejected orders and review each one quickly for clear signs of fraud. Record which were confirmed fraud and which were rejected without clear evidence, then assume the second group were errors. That gives you a conservative floor for your false positive rate. The cost of those errors is the profit margin on the lost orders plus the future revenue you give up when a customer has a bad experience.
Add review, hidden, and technology costs
Manual review costs more than analyst hours. If you sell something with instant payoff, such as digital codes or information, holding an order in a queue lowers the odds that the customer comes back. Like lost future revenue, this is an estimate, and it still belongs in your total.
Hidden costs are the ones with no immediate, measurable impact. After an account takeover, fraudsters may keep access and use the account to test other tactics, such as credential stuffing. The damage compounds until the vulnerability is closed. Account for the prevalence of all abusive behavior, not one attack type.
Technology is the last line item. Include development time on internal tools, integration time for third-party tools, and, if you run a rules-based system, the time spent configuring and updating those rules.
Add it all up and you have your total cost of fraud. These lines are closely linked, so a small change in one often moves the others. Accepting slightly higher direct losses, for example, can cut lost sales and review costs by far more. The net gain is usually worth the trade.
Forecast your chargeback rate instead of waiting for it
With the company-level picture in hand, turn to your team. Risk Ops has an advantage over most operations teams: you have ground truth in the form of chargebacks and false positive data. That makes it easier to justify headcount, defend an existing budget, run daily operations, and handle performance reviews.
Break every metric out by count, dollar amount, team, agent, and time. Many teams measure chargeback rate one way only, as chargebacks received last month divided by orders received last month. If your rate is under 1%, measured the way Visa measures it, move away from that lagging indicator and toward a forecasted rate. Chargebacks take time to arrive, but you can forecast the expected rate within a week of the original transaction date.
Measure agent ROI
The core metric gives you a return on every analyst and on the team as a whole:
(dollars stopped by agent − dollars lost in chargebacks by agent − dollars lost in refunds by agent) ÷ cost of a fully onboarded agent
Look for a ratio of at least 3:1. If a fully onboarded agent costs $100,000 a year, expect $300,000 or more in the numerator. Once every agent has a number, you can stack rank the team, see what production and accuracy are possible, and set goals that are reasonable instead of guessed.
Track insults, then share the numbers
An insult is a false positive: a legitimate customer your team or your models turned away. Track reopen and reorder rates to set a floor insult rate for the team and for each agent. Then multiply:
Reopens × average lifetime value per user = total lifetime dollars insulted
If you auto-decline transactions or accounts, hold those models to the same KPIs. They are an extension of your ops team.
Some of an agent’s best work never produces a transaction. When an analyst takes down a ring of accounts, some may still be sleeping, with no orders and no disputes to measure. You can only ballpark the savings, but a workable estimate is the average loss per fraud account or transaction multiplied by the number of closed accounts with zero transactions.
Put the results on one chart. Plot total dollars lost per agent on the X-axis and total customer insults on the Y-axis, with one bubble per agent sized by the number of reviews completed. Shade the target zone, where everyone should land. Picture a team of seven agents, A through G. Agents C, F, and D top the group, Agent B sits lowest, and Agent D reviewed the most transactions. The team average is within target for dollars lost, but the team is insulting too many customers. A team can hit its loss goal and still cost the business revenue, and this view shows it.
Finally, decide how transparent to be. Share the numbers with the whole team so everyone can see what each person contributes, and mask identities with agent ID numbers if you need to. The openness may make a few people uneasy at first. It also pushes a team to self-regulate, help each other, and improve without management stepping in, and it makes promotions and bonuses easier to justify.
See how Sift helps fraud teams cut false positives and lower the total cost of fraud. Request a demo.
Frequently asked questions
What is included in the total cost of fraud?
Direct losses (chargebacks, fees, refunds, replacement orders, reward abuse), lost sales from incorrectly cancelled orders, manual review time, hidden costs such as compounding account takeover damage, and technology costs including development, integration, and rule maintenance.
What is a good ROI target for a fraud analyst?
Aim for at least a 3:1 ratio of dollars stopped, minus chargeback and refund losses, to the cost of a fully onboarded agent. At $100,000 a year, that means $300,000 or more in net dollars stopped.
How do I measure false positives when customers don’t complain?
Track reopen and reorder rates to set a floor, then review rejected orders for clear signs of fraud. Treat rejections without clear evidence as likely errors, and multiply reopens by average lifetime value per user to estimate lifetime dollars insulted.
