
Mastercard's first-quarter earnings topped expectations as consumers kept spending despite cloudy macroeconomic conditions spurred by President Donald Trump's trade war.
The payments company reported $3.41 billion in adjusted net income, or $3.73 of adjusted diluted earnings per share, for the first three months of the year. Analysts had expected adjusted EPS of $3.58. Net revenue grew 17% to $7.3 billion on a currency-neutral basis.
"While there is uncertainty in the world, we've built a diversified, resilient business model and proven strategy that enables us to effectively navigate various economic environments," Chief Executive Officer Michael Miebach said in a statement Thursday.
Global purchase volume — the aggregate dollar amount of purchases made with Mastercard-branded cards — rose to $1.99 trillion, while falling short of analysts' estimates of $2.04 trillion.
Mastercard shares climbed 1.3% in early New York trading.
Consumers have continued spending even amid the threat of a recession triggered by Trump's mercurial tariff policies, benefiting global payment networks run by Mastercard and rival Visa. The latter reported earnings earlier this week that topped estimates while maintaining its full-year guidance.
Purchase, New York-based Mastercard introduced an
Mastercard previously said its net revenue for the full year is likely to increase by a percentage in the "low double digits." On Thursday, the firm
The card network earlier this week released Mastercard Agent Pay, a program that generates personalized payment experiences to consumers, merchants and issuers. The platform expands Mastercard's existing
The card network is partnering with Microsoft to scale the platform, with other partners including IBM, which is contributing B2B technology, and payment firms like PayPal's Braintree and Checkout.com for security. Security and authentication use tokenization, a process that replaces existing account numbers with one-off numbers that make the card useless if stolen. Mastercard is also using Databricks software to train the card network's gen AI engine to produce responses to users with less human interaction.
—John Adams contributed to this article.