Energy Fraud Detection

Identify quickly risks of fraud in Energy industry
Published by Accenture

Features

  • Advanced Analytics
  • Key Differentiators

Advanced Analytics

  • To forecast the claims fraud risk: Classical Time Series Models, Regressions, Autoregression, Neural Networks , GAM models​
  • To cluster the points/meters in groups with similar behavior, hierarchical clustering algorithms​
  • APP is provided with a library of pre-configured business rules to detect the fraud risk​
  • To select the most effective model in library, back testing simulation logic
  • Key Differentiators

  • The APP allows to reduce cost reducing the total time of detecting frauds since the whole detecting process is completely industrialized and automated
  • The user will be able to focus on the most risky clients first increasing the effectiveness of the detecting process
  • The process does not require any extra data flow like email or paste and copy increasing the speediness of the process and reducing the operational risk
  • The algorithm will suggest how risky can be a new client reducing the risk to acquire a new client
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