Streamlining Obligor Data Management and
Credit Monitoring for a Global Bank
Background
A leading global investment bank with a significant book of private credit ABL facilities across private markets needed a more reliable and consistent approach to collecting, managing, and validating obligor level financial data. Non standardized reporting from funds, fragmented data sources, and spreadsheet based tracking created operational inefficiencies and constrained the bank’s ability to monitor risk and manage funding effectively.
Challenge
Inefficient data tracking led to operational delays as funds submitted financial statements in inconsistent, non‑standard formats across multiple sources. The lending team manually tracked each obligor and facility, reconciled naming inconsistencies, and cross‑referenced numerous statements—adding significant effort, increasing error risk, and slowing funding. Heavy reliance on spreadsheets made managing diverse facilities cumbersome, requiring repeated normalization of financial data and further delaying decisions. Without a centralized data repository, the bank lacked clear, aggregated visibility into exposures and risk, limiting its ability to analyze obligor‑level trends, assess portfolio health, or identify concentration risks in a timely manner.
Streamlining Obligor Data Management and Credit Monitoring for a Global Bank
- 45% improvement in productivity through automation of data workflows and reduced manual processing.
- Faster updates to models through standardized inputs and automated recalculations.
- 99% accuracy of the AI / ML algorithm deployed for data extraction.