Predictive Analytics

How a Predictive Analytics-based Framework Helps Reduce Bad Debts in Utilities

Author(s)

A WNS Perspective

Download Whitepaper

Key Points

Write-offs of utilities have risen from approximately USD 400 million in 2008 to about USD 2.8 billion in 2014
Utilities can minimize bad debts by using predictive analytics to identify high risk customers; design targeted collection tactics for the high-risk segment; and improve customer interactions and experience
The opinion on whether US should adopt a Solvency II-like regulatory framework is divided. Some from the US insurance fraternity feel that an exact replica of Solvency II is not required
Proactive action against high-risk customers can help companies reduce bad debt write-offs by as much as 40 percent

The utilities industry has been riddled with payment delinquencies for the past several years, forcing utility companies to trade off profits for survival, and give up on their rightful revenue by taking the ‘write-off’ route. An ‘integrated three-pronged revenue protection strategy’ aids utility companies in effectively minimizing bad debt write-offs. Predictive analytics lays the foundation for this strategy by enabling customer segmentation, revising collections tactics and enhancing customer satisfaction interventions.

DOWNLOAD WHITEPAPER

Join the conversation