How to Show Finance that Fraud Prevention Drives Revenue

Finance teams already track approval rates next to fraud losses. The 2026 Global eCommerce Payments & Fraud Report from the Merchant Risk Council, commissioned by Visa Acceptance Solutions and Verifi, ranks payment success rate and revenue as the top two KPIs among 14 payment metrics. Fraud leaders who report in those terms get budget conversations that start with revenue, not loss avoidance.

Count the costs that never reach the fraud report

Chargebacks, unauthorized transactions, and refunds appear in the financials, so finance measures them. The costs that matter most for growth sit in other areas:

• False declines that turn away legitimate customers
• Signup friction that causes abandonment
• Manual review hours that raise operating costs
• Churn among customers who find security steps frustrating

The same Merchant Risk Council report shows why this gap is widening. False positives keep rising even as overall fraud has declined for the second year in a row. The line finance sees is shrinking while the line it does not see is growing.

A declined legitimate order carries both risk and revenue consequences, which is why merchants increasingly measure fraud and acceptance together. The report’s sponsors note that the survey results are directional, so treat the trend as the takeaway rather than any single figure.

Speak in the metrics finance already trusts

Translate fraud outcomes into the numbers on the finance dashboard:

• Approval rate: the share of legitimate orders accepted. Every point is revenue.
• Onboarding conversion: the share of signups that complete. Less friction for trusted people means more activated accounts.
• Customer lifetime value (CLV) to customer acquisition cost (CAC): a legitimate customer wrongly declined is acquisition spend that never pays back. Keeping that customer improves the ratio without raising marketing spend.
• Cost to operate: manual reviews per thousand orders, dispute volume, and support tickets tied to fraud events.
• Total cost of ownership: time to value, integration effort, and ongoing maintenance for any platform you evaluate.

Sift customers show what these metrics look like in practice. Patreon’s case study cites a 99% new user acceptance rate. Paula’s Choice reports 6x ROI. Smartproxy reports 96% fewer customer escalations. Each result maps to a line finance can model: acceptance, return on investment, and support cost.

Run the numbers on one metric first

Start with approval rate, because finance can verify it quickly. Added revenue equals annual volume multiplied by the change in approval rate.

Here’s an illustration with assumed figures. Say a business processes $100 million in annual online volume. A one-point lift in approval rate adds $1 million in accepted sales. At a 30% gross margin, that is $300,000 in gross profit, before you subtract any change in fraud losses and the cost of the platform.

Run the same math on operations. If a team manually reviews 5,000 orders a month at six minutes each, that is 500 hours of analyst time. Automating the clear-cut decisions returns those hours to casework that needs a human.

Swap in your own volume, margin, and review counts. Then check where you stand with the Fraud Industry Benchmarking Resource (FIBR®), which compares fraud metrics against industry benchmarks from Sift’s global network.

Plan for what changed in 2026

Three shifts from the past year belong in any business case.

Agentic commerce adds a new kind of buyer. AI agents increasingly shop and transact on behalf of people. Approval logic that assumes a human at the keyboard can misread legitimate automated activity, which creates false declines of a new kind. Fraud strategy now has to separate trusted automation from risky automation, and finance should expect approval rate to depend on getting that right.

Generative tools lower the cost of attacks. Fraudsters use them to produce convincing fake accounts, messages, and documents at higher volume and lower cost. More attack volume makes a purely manual response more expensive every quarter.

Automation is moving into fraud operations. Machine learning decisions and automated workflows let a team absorb more volume without a matching rise in headcount. Sift’s Fraud Attack Defense Suite, introduced in October 2026, shows where fraud operations are heading.

Each shift adds a revenue question to a risk question. Teams that already measure both are better placed to answer it.

Pull four numbers before you meet with finance

Gather these before the meeting, and ask finance how it defines approval rate and CAC so your figures match theirs:

1. Your current false decline rate and the revenue those orders represent
2. The share of manual reviews that end in approval, and the total reviewer hours behind them
3. Chargebacks that better visibility could have prevented
4. Customer service and remediation costs tied to fraud events

Then widen the circle. Fraud prevention touches operations, product, compliance and risk, customer experience, and security and IT. Bring go-to-market teams in early so fraud metrics tie to conversion and expansion goals. Work with product and customer experience leads on onboarding friction, and agree on one shared model that finance, fraud, and growth all read the same way.

A fraud platform earns its place the way any system investment does, with measurable returns. The case is strongest when it shows approved revenue, recovered hours, and retained customers next to the losses it prevents. Sift’s global data network analyzes more than 1 trillion events per year for 700+ brands, which gives those decisions the context to stay accurate as fraud patterns shift.

Take these numbers to finance. Download the CFO’s Guide to Fraud Prevention, Value Creation, and Risk Management. Get the guide here.

Frequently asked questions