Approving more payments without growing fraud losses
The strategy that earned the most only worked because half its review queue was being declined. Staffing up to clear the queue made it earn less.
The question
A fictional payments platform processes 1,000,000 attempted payments a month at an $80 average ticket, with 1% of attempts fraudulent. It has a 12-person review team, enough for 10,800 manual reviews a month. The CEO wants to approve more legitimate customers. Which acceptance strategy should it pilot?
Three strategies, side by side
| Strategy | Good customers approved | Fraud loss / mo | Review coverage | Contribution / mo |
|---|---|---|---|---|
| Conservative | 91.7% | $20,224 | 100% | $464,660 |
| Targeted review | 94.9% | $7,950 | 48.4% | $479,342 |
| Growth first | 97.7% | $129,536 | 100% | $388,364 |
Growth first approves the most good customers, but fraud losses rise more than sixfold over the conservative policy. The extra approvals don't come close to paying for them.
Targeted review wins on contribution and cuts fraud losses the most. But look at review coverage: it sends 22,300 payments a month to a team that can review 10,800. The overflow, more than half the queue, is simply declined.
So staff up?
Covering the full queue takes 25 reviewers, 13 more. At full coverage, acceptance of good customers rises to 95.9%, but monthly contribution falls to about $412,000. That's below both the understaffed version and the conservative policy. In this model, the 13 extra salaries cost more than the margin on the additional approvals.
The "best" strategy on a spreadsheet was quietly relying on declining customers it had flagged for review. A good recommendation names that dependency instead of hiding it.
What I'd actually recommend
- Pilot targeted review on a slice of traffic with explicit guardrails: a loss ceiling, a customer-decline ceiling and a rollback owner.
- Fix the referral rate before adding headcount. Better rules or a model that sends fewer good customers to review beats hiring 13 people.
- Treat overflow declines as a customer-experience cost, not free fraud prevention, and track them separately.
- Wait for mature loss data. Chargebacks arrive late, so a pilot that looks great in week two may not in week ten.
These numbers come from invented assumptions, not market benchmarks, and the model compares three fixed policies rather than optimizing. The reasoning carries over: approval rate, loss and operational capacity are one decision, not three.
Change the assumptions yourself. Raise fraud incidence, resize the team and download a decision memo.
Open the Payments Strategy Lab → · Model, methodology & tests