Credit where credit’s due: Inside Experian’s AI framework that’s changing financial access

While many businesses are now competing to adopt and deploy AI, the Credit Bureau Giant explore A very measurable approach was taken.

Experian develops its own internal processes, frameworks, and governance models that help it test the generated AI, deploy at scale, and make an impact. The company’s journey helps transform from the operations of a traditional credit bureau into a sophisticated AI-driven platform company. Its approach – Fusion Advanced Machine Learning (ML), Agent AI Construction and Grassroots Innovation – Improves business operations and expands financial access to approximately 26 million Americans.

Experian’s AI Journey In sharp contrast Companies that began exploring machine learning only after Chatgpt appeared in 2022. The credit giant has methodically developed nearly two decades of AI capabilities, creating a foundation that enables it to quickly leverage the generated AI breakthroughs.

“AI has always been a part of Experian Way, surpassing the coolness of AI,” Shri Santhanam of EVP and GM, Software, Software, Platform and AI products told VentureBeat in an exclusive interview with Experian. “Over the past two decades, we have used AI to unleash the power of data to have a better impact on businesses and consumers.”

From traditional machine learning to AI innovation engine

Before the modern AI era, Experian had used ML to innovate.

Santhanam explained that Experian did not rely on basic traditional statistical models, but instead took the lead in using gradient-enhancing decision trees and other machine learning technologies for credit underwriting. The company has also developed an interpretable AI system (critical for regulatory compliance in financial services) that sheds light on the reasoning behind automated lending decisions.

Most importantly, before Chatgpt’s release, Experian Innovation Lab (formerly the Data Lab) experimented with language models and transformer networks. This early work allowed companies to quickly leverage the advancements in generated AI rather than starting from scratch.

“When the Chatgpt meteor hit, it was a pretty simple acceleration point for us because we understood the technology, considered the application, and we just stepped on the pedal.”

The Technology Foundation enables Experian to bypass the experimental phases where many businesses are still navigating and directly transfer to production implementation. While other organizations are just beginning to understand what large language models (LLMs) can do, Experian has deployed them in their existing AI frameworks to apply them to specific business issues they have previously identified.

Four pillars of enterprise AI conversion

Experian does not panic or pivot when the generated AI appears. It accelerates along the path that has been drawn. The company organizes four strategic pillars approaches to provide technology leaders with a comprehensive framework for AI adoption:

1. Product Enhancement: Experian examines existing customer-facing products to identify opportunities for AI-driven improvements and brand-new customer experiences. Rather than creating standalone AI capabilities, Experian integrates generation capabilities into its core suite of products.
2. Productivity optimization: The second pillar addresses productivity optimization through the implementation of AI across engineering teams, customer service operations and internal innovation processes. This includes providing AI coding help to developers and simplifying customer service operations.
3. Platform development: The third pillar – perhaps crucial to tedious success – is based on platform development. Experian realized early on that many organizations will work to go beyond proof-of-concept implementation, so it invested in building platform infrastructure designed specifically for responsible scales within the scope of AI initiatives.
4. Education and Authorization: The fourth pillar introduces education, empowerment and communication, creating structured systems that drive innovation across the organization rather than limiting AI expertise to professional teams.

This structured approach provides a blueprint for businesses to try to move beyond decentralized AI experiments, implement them towards systems, and have measurable business impact.

Technical Architecture: How Experian builds a modular AI platform

For technology decision makers, Experian’s platform architecture demonstrates how to build enterprise AI systems to balance innovation with governance, flexibility and security.

The company has built a multi-layer technology stack with core design principles that can be adapted to:

“We avoid going through one-way doors,” Santhanam explained. “If we make choices on technology or frameworks, we want to make sure in most cases … we make choices and if we need to, we can turn from the center.”

The architecture includes:

• model: A variety of large language model options, including through Azure’s OpenAI API, AWS cornerstone models, including numerous Claude and fine-tuned proprietary models.
• Application layer: Service tools and component libraries that enable engineers to build proxy architectures.
• Security layer: Cooperate with early stages Generator AI For security, policy governance and penetration testing are designed for AI systems.
• Governance structure: Global AI Risk Council, direct implementation.

This approach contrasts sharply with businesses dedicated to single-vendor solutions or proprietary models, and Experian has greater flexibility as AI capabilities continue to evolve. The company now sees its construction transforming into what Santhanam calls “a mixture of AI systems, more of a mix of experts and agents powered by experts and professional experts or small language models.”

Measurable Impact: AI-driven financial inclusion

In addition to architectural elaboration, Experian’s AI implementation also demonstrates specific business and social impact, especially when dealing with the challenge of “credit invisible”.

In the financial services industry, “credit invisible” refers to Americans who lack sufficient credit history to generate traditional credit scores. These people, often young consumers, recent immigrants or those in a historic community, face significant barriers to accessing financial products, despite potentially reputable people.

Traditional credit scoring models rely primarily on standard credit bureau data such as loan payment history, credit card utilization, and debt levels. Without this traditional history, lenders historically viewed these consumers as high risk or refused to serve them entirely. This creates a capture 22 because people cannot access the credit product in the first place, so people cannot build credit.

Experian solves this problem with four specific AI innovations:

1. Alternative data model: Machine learning systems that combine non-traditional data sources (leasing payments, utilities, telecom payments) into reputation assessments and analyze hundreds of variables rather than limited factors in regular scale models.
2. AI that explains compliance: By articulating why specific scoring decisions are made, complex models can be used in a highly regulated lending environment, thus maintaining a framework for regulatory compliance.
3. Trend data analysis: AI systems examine how financial behavior develops over time instead of providing static snapshots, detecting patterns and payment behaviors of balanced trajectories, thereby better predicting future credibility.
4. Segment-specific architecture: Custom model design custom models for different segments that are not visible to credit – these segments have files compared to files without traditional history.

The result is huge: Financial institutions using these AI systems can approve 50% of applicants approved from previously invisible populations while maintaining or improving risk performance.

Viable technical decision makers

Experian’s experience offers several actionable insights for businesses looking to lead AI adoption:

Building an adaptive architecture: Build an AI platform that allows model flexibility, rather than betting on a single provider or method only.

Integrate governance as soon as possible: Create cross-functional teams that can work together from the very beginning, security, compliance and AI developers can work together instead of operating in silos.

Focus on measurable impact: Prioritize AI applications, such as Experian’s credit expansion, which can provide tangible business value while also addressing broader societal challenges.

Consider proxy architecture: Go beyond simple chatbots toward well-planned multi-agent systems that can handle complex domain-specific tasks more efficiently.

For technology leaders in financial services and other regulated industries, Experian’s journey shows that responsible AI governance is not a barrier to innovation but a driver of sustainable, trustworthy growth.

By combining methodical technology development with forward-looking application design, Experian is a blueprint for how traditional data companies can transform themselves into AI-driven platforms with significant business and social impact.