How PayPal Budgets and Forecasts Revenue

There have been countless books and articles written about PayPal—the “mafia,” the stars who came out of it, and the company’s relentless growth. But what’s never covered? How PayPal actually built its forecasting machine. Because at a payments giant handling over $2 trillion in annual transactions, forecasting isn’t just a finance function—it’s a survival skill.

At PayPal, forecasting wasn't just about predicting numbers—it was about understanding how money moves at scale. Erica Gessert, who ran FP&A at PayPal for eight years, shared a behind-the-scenes look at how the company approached budgeting and forecasting revenue as it scaled.

What makes PayPal unique is that it operates at the intersection of payments, technology, and macroeconomics. Unlike a traditional SaaS business with predictable subscription revenue, PayPal's financials are driven by transaction volume, consumer spending behavior, and economic cycles. This complexity forced the FP&A team to develop sophisticated models that could adapt in real time to changing market conditions.

In this piece, we’ll break down PayPal’s budgeting and forecasting playbook, focusing on three key areas:

PayPal’s experience offers valuable lessons for any finance leader managing growth, uncertainty, and the balance between top-down targets and bottom-up execution. It’s a masterclass in harnessing the chaos of a usage based model.

At the core of PayPal’s financial forecasting was Total Payment Volume (TPV)—the total dollar value of transactions flowing through the platform. Unlike a SaaS business where revenue is largely locked in through subscriptions, PayPal’s revenue was a direct function of consumer spending behavior, merchant adoption, and macroeconomic conditions.

When Erica Gessert first joined PayPal in 2015, the company relied on regression-based models that used historical TPV trends to predict future performance. Over time, however, PayPal built a more sophisticated forecasting engine, incorporating dozens of variables across:

At first, regression-based models worked fine in a steady-state environment. But as PayPal expanded across more geographies, currencies, and business lines, it became clear that a single-variable model couldn’t capture the complexity. Revenue swings in one region might be offset by growth in another, and macroeconomic factors like interest rates or currency fluctuations had a bigger impact than historical trends alone could predict.

To improve accuracy, PayPal developed a predictive model called “Galileo”, which used advanced algorithms to refine forecasts. But despite its sophistication, Galileo had a major flaw—it lacked “explainability.”

Ultimately, PayPal settled on a hybrid approach, using Galileo as a baseline but layering in human judgment and business context to adjust forecasts.

Even at a massive scale, PayPal’s experience proves that no model is perfect - even the super smart ones created in a lab. The best forecasting strategies combine data-driven predictions with human intuition, and they evolve based on macro trends, product shifts, and customer behavior. But at the end of the day, even the most sophisticated models need a simple, common-sense check—take a step back and ask,