Equifax (EFX) 46th Annual William Blair Growth Stock Conference summary

Proprietary data and AI strategy

• Over 90% of revenue is generated from proprietary data, inaccessible to external AI providers and protected by regulations like the Fair Credit Reporting Act.

• Unique data sets include credit files from 10,000+ financial institutions, cell phone and utility data covering 190 million Americans, and income/employment data from 5 million companies.

• AI is leveraged to enhance scores, models, and products, with a focus on explainable AI for financial services.

• Investment in AI includes 10 new patents in Q1 and 40 in the previous year, totaling around 450 unique AI capabilities.

• All models and scores now utilize proprietary AI, resulting in significant performance improvements.

Cloud transformation and innovation

• $3 billion invested over five years to become a cloud-native data analytics company, consolidating all data into a single fabric.

• Cloud migration enables faster, higher-performing product delivery and supports AI-driven innovation.

• The Vitality Index, measuring revenue from new products over three years, rose from 5-7% pre-cloud to 17% in Q1, reflecting strong innovation momentum.

• New product launches, such as integrating income and employment data into credit reports, are driving share gains, especially in mortgage and auto sectors.

• Advanced AI tools democratize analytics, making sophisticated capabilities accessible to smaller financial institutions globally.

Operational efficiency and productivity gains

• AI deployment is a strategic priority for both customer-facing solutions and internal productivity, with $75 million in cost savings targeted for 2026-2027.

• Margin expansion guidance increased from 50 to 75 basis points due to AI-driven productivity, with rapid adoption across operations, technology, and support functions.

• AI agents in call centers and paper processing have improved first-call resolution and overall efficiency.

• Technology teams are using AI tools for coding, accelerating product development and reducing costs.

• Privacy and security controls are in place to ensure safe AI deployment at scale.