Case Study: Granular Credit Business Intelligence Platform

The Client

  • The client is an established financial services sector investor in the European markets. Their credit strategy is focused on a broad range of performing and non-performing consumer, SME and corporate debt, as well as structured credit, corporate lending and real estate.
  • To date the client has raised nearly €3bn for the strategy across four funds, which have been deployed into 75+ investments across 10+ EU countries.

The Challenges

  • To scale the business intelligence platform effectively in a timely manner without excessive manual effort and the reduction of repetitive tasks by the deal execution and asset management teams.
  • To create a robust automated process for ingesting data from multiple sources, in various languages & formats from servicers of varying degrees of sophistication.
  • To create and maintain a golden source of data to be leveraged to fulfill all the business internal and external reporting needs and supply clean accurate data on which to run advanced pricing and analytical processes.

Solution Delivered

  • Created a cloud-based, end-to-end automated algorithmic process for ingesting, cleansing, reconciling, normalizing and standardizing granular credit data.
  • Transformed raw data tapes into accurate, timely and meaningful information to drive reporting and analytics requirements.
  • Designed a robust quality assurance framework that validated data inputs from a business logic perspective and quickly identified gaps and inconsistencies that needed to be resolved with loan servicers.
  • Built a KPI engine to calculate the operational and investment metrics that ensured accurate monthly data.

Results & Benefits

  • Through the application of automated intelligent processing and Minerva as the primary reporting tool, Equipped’s robust, scalable and centralized intelligence platform not only delivered the client with a deep understanding of performance drivers for their individual investments but also interactive BI dashboards which provided views at the country, fund and asset class levels.
  • Intelligent automation and dashboarding removed key man dependencies and all but eliminated manual data entry and validation, which enabled the client teams to focus on value additive activities such as sourcing new deals and maximizing the returns on existing investments.