Custom model development is challenged by identifying and accessing attributes most predictive of borrower default. In fact, many of the best attributes are locked away in prebuilt score models preventing variable-by-variable access beneficial in building a powerful custom score model. PayNet can help you better serve the market by providing you with a variety of predictive borrower attributes that enable custom models to maintain your proprietary advantage.

Product Sheet

Details

PayNet Custom Score Variables deconstruct the attributes contained within PayNet's powerful commercial credit scores to allow modelers unprecedented access to predictive data and state-of-the-art scoring science. Profits increase from models customized to the specific borrower attributes and characteristics that are most important to your business. PayNet Custom Score Variables have sufficient breadth and depth to be used as sole inputs to models, or combined with other data sources, and/or prebuilt scores.

 

Benefits

Utilizing the extensive learnings embodied in PayNet Custom Score Variables can provide valuable components to help you safely book more business. The predictive data and state-of-the-art scoring science contained within PayNet's commercial credit scores can be fitted to your unique scoring requirements.

Solutions

The Challenge

Our Solution
  • Limited breadth of data available with existing legacy systems
  • Uniformly aggregated contract-level data from the largest commercial-debt database ever created
  • Sufficient number and variety of attributes available to build the most powerful model possible
  • Sophisticated variables developed through PayNet's extensive experience building credit scoring models
  • Potential rigidity or applicability concerns of pre-built "generic" scores that may not be the best fit for your business
  • Unprecedented variable-by-variable access to attributes that are proven to provide predictive lift across a wide variety of lenders and industry segments
  • Availability of sufficient historical data to ensure that there are no cyclical biases
  • Variables derived from PayNet's proprietary collection of current and historical contracts incorporating variables proven to be predictive across – not just within – economic cycles