Analytic-based Fraud Prevention and Recovery Solution

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

Client Challenges:

• 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.

Outcome:

• 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”