PayPal (PYPL) investor relations material

Technology transformation and modernization

• Unified all technology functions under one roof to streamline operations and standardize systems, reducing fragmentation and technical debt across identity, risk, and compliance platforms.

• Consolidated identity infrastructure between PayPal and Venmo, enabling shared features like multi-factor authentication and reducing redundant development efforts.

• Initiated a multi-year migration to cloud-native platforms, with Braintree and Venmo already transitioned and PayPal in progress, aiming for greater flexibility and innovation.

• Leveraged AI to automate code migration from C++ to Java, accelerating modernization and reducing manual intervention, with 150 applications migrated in six months.

• Adopted a strategy of loosely decoupled, highly aligned systems to balance efficiency, speed of innovation, and future optionality for business units.

Merchant and consumer experience improvements

• Addressed merchant integration challenges by intercepting legacy API calls on the backend, delivering modern checkout experiences without requiring merchants to upgrade immediately.

• Developed an AI-powered Merchant Integration Agent to automate code upgrades, certification, and ongoing monitoring, significantly reducing the time and effort for merchants to adopt new features.

• Enhanced consumer checkout with passkeys and streamlined flows, reducing friction and improving conversion rates through single-click and zero-click experiences.

• Enabled interoperability between PayPal and Venmo, allowing seamless money transfers and expanding payment options for consumers and merchants globally.

• Introduced Payment Ready APIs to help merchants present the most relevant payment options, boosting conversion rates and customer satisfaction.

AI adoption and operational efficiency

• Pioneered the use of Model Context Protocol (MCP) to connect large language models with internal and external tools, automating workflows and product features.

• Implemented AI-driven software development lifecycle (SDLC), including code assist and knowledge layers, enabling rapid feature deployment and problem triage.

• Achieved 2,000+ AI-assisted pull requests per week, with code assist usage growing 50% month-over-month, driving significant productivity gains.

• Focused AI investments on high-impact modernization and migration tasks, balancing cost, efficiency, and speed while maintaining quality and security.

• Reinvested cost savings from automation and cloud migration into building a best-in-class technology platform, targeting a two- to three-year transformation cycle.