Streamlines Credit Underwriting with AI-Powered Document Extraction

About the Client

A European business bank specializing in financing large-scale projects is known for its rigorous approach to credit underwriting. Their success depends on carefully evaluating complex loan requests to make informed, responsible lending decisions, all while delivering timely answers to clients.

When Manual Review Becomes a Bottleneck

Underwriting new loans required teams of domain experts to review hundreds of pages of diverse documents for every application—applicant details, project specs, market analyses, and financial statements, all in different formats and layouts. This manual extraction of key information consumed around 25% of the roughly 10-day timeline needed to draft a transaction proposal for the credit committee. The result was a significant bottleneck that diverted expert attention from higher-value work like analysis and risk assessment, slowing decision-making and reducing operational efficiency.

A Smart, Tailored AI Document Extraction Hub

Partnering with Xebia, the bank set out to augment its credit underwriting process with an AI-powered solution. Xebia’s engineers co-developed an MVP built on the Xtractor platform, collaborating closely with underwriting experts to capture their knowledge in prompt-based questionnaires that guide precise, structured information extraction.

The system automatically pulls key details from large, varied document sets and returns answers with direct citations to source documents for easy verification and full traceability. This not only reduces time spent on manual review but builds trust in the AI’s output among expert users. The entire platform runs securely on Azure within the bank’s private network, with robust authentication via Azure Entra ID, scalable containerized deployment, and modern CI/CD practices to ensure reliability and easy maintenance.

The Business Impact

Though quantitative gains are still being measured, the AI-powered extraction hub has already reduced the manual effort required to prepare underwriting proposals. Experts spend less time sifting through documents and more time on meaningful analysis and risk evaluation, shortening the overall 10-day approval timeline. Built-in transparency through document citations ensures high accuracy and trust in AI-generated insights. By standardizing extraction workflows with reusable questionnaires, the solution delivers a more agile, efficient, and reliable credit underwriting process that supports faster, better-informed lending decisions.