Prior Situation / Scenario:
- Intuition as guide to start debt recovery process
- Losses due to failed debt recovery processes
- Regulatory changes made the process more expensive
- Active Alternative Fake Energy providers on Social Networks
- Create an objective, data-based solution to decide when to start debt recovery process
- Prioritize customers based on their probability to pay and the expected value to be recovered
- Reduce energy theft
Strata Solution/ Key Enablers:
Development of an indicator that combines:
- A machine learning algorithm to predict the likelihood of an account to pay after fraud was detected.
- Attributes that are related to higher fraud debts.
- We develop an automated Social Network analytics scrapper to identify suspicious alternative fake energy providers.
- We enable the ranking of customers that had a higher propensity to fraud.
- We Improved the fraud management process efficiency
- Timely debt recovery process improvement
- Early detection of “Fake Energy providers”
With the application of the machine learning algorithm to the debt recovery process, the company increased the amount of debt repaid by 19% and reduced losses.