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In the fast-paced world of artificial intelligence (AI) adoption, Experian has taken a different path, focusing on a slow and steady approach. By developing internal processes, frameworks, and governance models, Experian has been able to test and deploy generative AI at scale, transforming itself from a traditional credit bureau to a sophisticated AI-powered platform company. This approach, which combines advanced machine learning, agentic AI architectures, and grassroots innovation, has not only improved business operations but also expanded financial access to around 26 million Americans.

Experian’s journey contrasts sharply with companies that only began exploring machine learning after the emergence of ChatGPT in 2022. Experian has been building its AI capabilities methodically for almost two decades, establishing a strong foundation that allowed it to quickly leverage generative AI breakthroughs when they occurred.

According to Shri Santhanam, EVP and GM of Software, Platforms, and AI products at Experian, AI has been ingrained in the company’s operations long before it became a trend. The organization has been using AI to harness the power of data and create positive impacts for businesses and consumers for over 20 years.

From Traditional Machine Learning to AI Innovation Engine

Before the era of modern generative AI, Experian was already harnessing the power of machine learning. Instead of relying on basic statistical models, Experian pioneered the use of Gradient-Boosted Decision Trees and other machine learning techniques for credit underwriting. The company also developed explainable AI systems essential for regulatory compliance in the financial services industry.

The early work conducted by the Experian Innovation Lab in language models and transformer networks positioned the company to quickly adopt generative AI advancements instead of starting from scratch when ChatGPT emerged. This foundation enabled Experian to move directly to production implementation, bypassing the experimental phases that many other enterprises are still navigating.

Four Pillars for Enterprise AI Transformation

Experian’s approach to adopting generative AI is organized around four strategic pillars that can provide guidance to technical leaders in other organizations:

• Product Enhancement: Identifying opportunities for AI-driven improvements in customer-facing offerings.

• Productivity Optimization: Implementing AI across engineering teams, customer service operations, and internal innovation processes.

• Platform Development: Investing in building platform infrastructure for scaling AI initiatives enterprise-wide.

• Education and Empowerment: Driving innovation throughout the organization rather than limiting AI expertise to specialized teams.

By following this structured approach, enterprises can move beyond scattered AI experiments and towards systematic implementation with measurable business impact.

Technical Architecture: How Experian Built a Modular AI Platform

Experian’s technical architecture showcases how to build enterprise AI systems that balance innovation with governance, flexibility, and security. The company has constructed a multi-layered technical stack with core design principles that prioritize adaptability. This approach contrasts with enterprises that commit to single-vendor solutions, providing Experian with greater flexibility as AI capabilities evolve.

• Model Layer: Offers multiple large language model options.

• Application Layer: Provides service tooling and component libraries for building agentic architectures.

• Security Layer: Includes partnerships for security and policy governance specifically designed for AI systems.

• Governance Structure: Involves a Global AI Risk Council with direct executive involvement.

Measurable Impact: AI-Driven Financial Inclusion at Scale

Experian’s AI implementation has resulted in concrete business and societal impacts, particularly in addressing the challenge of "credit invisibles." These are individuals with insufficient credit history to generate a traditional credit score, numbering around 26 million Americans.

The company has tackled this issue through four specific AI innovations:

• Alternative Data Models: Incorporating non-traditional data sources into creditworthiness assessments.

• Explainable AI for Compliance: Maintaining regulatory compliance by articulating scoring decisions.

• Trended Data Analysis: Examining how financial behaviors evolve over time to predict creditworthiness.

• Segment-Specific Architectures: Designing custom models for different segments of credit invisibles.

This approach has allowed financial institutions to approve more applicants from previously invisible populations while maintaining or improving risk performance.

Actionable Takeaways for Technical Decision-Makers

Experian’s experience offers actionable insights for enterprises looking to lead in AI adoption:

• Build adaptable architecture.

• Integrate governance early.

• Focus on measurable impact.

• Consider agent architectures.

For technical leaders in regulated industries, Experian’s journey demonstrates that responsible AI governance enables sustainable and trusted growth. By combining methodical technology development with forward-looking application design, traditional data companies can transform themselves into AI-powered platforms with significant business and societal impacts.