GenAI-powered credit agreement extraction delivers 60-70% efficiency gain in PoC testing

A US-based bank managing complex corporate and institutional (C&I) loan portfolios operates a distributed credit infrastructure with hundreds of borrowers, high transaction volumes and multi-system environment. The bank approached us for a proof of concept (PoC) to show how its processes in C&I credit agreements and documentation can be improved.

• Last-minute rush to complete loan bookings created data-entry errors in the loan management system, resulting in post-close data breaks and downstream reconciliation issues
• BAU operations teams were unable to dedicate sufficient bandwidth for data remediation as day-to-day loan servicing workload took priority over resolution of outstanding data breaks
• Data inconsistency across multiple systems—loan management, risk and regulatory reporting platforms—made reconciliation complex and a time consuming process
• High volume of complex LSTA-based credit agreements made manual attribute extraction error-prone and inconsistent across teams
• Amendment and modification processing created ongoing data drift, with terms of booked loans progressively falling out of sync with the executed documentation
• Lack of audit traceability created exposure during internal and regulatory review cycles

• We ran a PoC using DeepMine, our proprietary GenAI-powered document extraction engine, hosted in a secured third-party cloud environment
• We built on a multi-agent extraction pipeline using a modular architecture that can be deployed on any LLM the client needs
• We applied a multi-agent document-analysis framework, combined with an AI-driven, self improving prompt library
• We configured DeepMine to extract 100+ key data elements from credit agreements and related documentation using a taxonomy that can be customized to any attribute
• We applied confidence-based validation to route complex or low-confidence attributes for SME review, enabling teams to focus on exceptions rather than manually extracting and validating every data point
• We structured validated outputs for downstream consumption, including integration with the bank’s loan management system, and tested both loan-booking and data-remediation use cases through the PoC