How AI modernizes commercial loan document review 

The EY team worked closely with the bank’s product, business and technology teams to validate the value of an automated, end-to-end loan data remediation capability. The objective was to demonstrate how AI could be applied responsibly and effectively to process unstructured commercial loan documentation at scale without sacrificing transparency or control.

The EY team designed and implemented a retrieval-augmented generation (RAG) architecture to serve as the backbone of the solution. Leveraging machine learning and AI technologies including large language models (LLMs), the solution digitized and analyzed commercial loan document packages, extracted key data elements and applied domain specific business rules aligned to regulatory reporting requirements.

What made the approach distinctive was the integration of deep credit domain knowledge directly into the AI workflow. Prompts aligned to the bank’s data dictionary, regulatory interpretations and document hierarchies allowed the solution not only to extract data but also to understand which source documents carried authority for each reporting element. Built-in audit trails and rationale generation provided transparency into how values were derived, supporting confidence in the results. This design allowed outputs to be consistently reviewed, validated and reused across reporting cycles.

The EY team adopted an iterative, test-and-learn approach, initially validating the solution across a subset of facilities before scaling. Outputs were continuously compared against manually validated data, with prompts, logic and processing methods refined to improve accuracy and consistency. This disciplined approach accelerated learning while maintaining rigorous quality standards.

Beyond performance metrics, the engagement demonstrated a clear path forward for the bank to standardize and strengthen FR Y-14Q reporting. By reducing reliance on manual review, the organization can lower operational costs, decrease risk and reallocate specialized talent toward higher-value activities. 

Equally important, the solution established a scalable framework for future use cases. The same architecture and design principles can be extended to additional regulatory elements, booking quality controls, servicing validation and ongoing compliance processes — transforming document review from a one-time, reactive effort into a continuous, repeatable capability.

For the bank, the engagement marked a shift in how AI could be deployed across commercial credit. Rather than a black-box tool, AI was applied as an explainable, auditable service that enhances transparency, builds trust and supports long-term operational resilience. In doing so, the bank took a step toward more intelligent operations, one that meets today’s demands while preparing for tomorrow’s challenges.