Park Square Capital adopts Alter Domus’ Credit.OS

The Business Challenge

The continued expansion of Park Square Capital‘s portfolio to more than 100 companies was the catalyst for the firm to seek a software solution to drive efficiencies in data ingestion and reporting at the asset level. While Park Square has always had a rigorous focus on active portfolio management, the existing operational model could be laborious, due to manual inputs of hundreds of data points into its portfolio management framework, creating an increased workload of lower-value tasks.

The deal team was spending valuable time on laborious manual data entry and reporting, when they could instead have been focusing on the underlying drivers of company performance and deeper credit analysis. In addition, the lack of data model standardization impeded some deal team synergies and a more enterprise-wide view on credit, and was at odds with Park Square’s best-in-class data strategy.  

How the adoption of Alter Domus’ Credit.OS transformed Park Square Capital’s private credit data management capabilities 

Alter Domus’ longstanding position in the debt capital markets, coupled with our strategic direction of technology investment, inspired us to focus on three key areas in developing our end-to-end Credit Operating System (Credit.OS): the accurate extraction and digitization of corporate financial data, the efficient and timely calculation of covenants, and the enhanced ongoing monitoring of assets within a portfolio.

The data extraction function of Credit.OS was specifically designed to help resolve the exact challenges Park Square had been facing. Developed with automation and machine learning at its core, our framework digitizes, normalizes, and aggregates data, and has been trained on millions of corporate financial documents. Data checks and reconciliations are made through proprietary QA review, backed by our in-house team of data scientists and credit experts.

As Park Square explored the utilization of the Credit.OS with AD, the following feature-functions stood out:

1. Robust, accurate, timely, and transparent data collection, extraction, and normalization
2. Operationally sound, programmatic mapping of data to fully functional Excel-based models
3. Enablement of standardization and streamlining across the Park Square portfolio
4. Agility to feed data into Park Square’s internal and third-party downstream systems

The impact on Park Square Capital

• Minimization of manual data entry and more centralized tracking of hundreds of financial datapoints 
• The enablement of one standardized reporting template across 100+ portfolio companies, all with slightly different report formats
• A dramatic reduction in time spent on valuation audit queries due to the systemization of reporting and data
• Improved oversight of key reporting deadlines due to improvements of workflow efficiency