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Towards addressing unauthorized sharing of subscriptions
1Adobe Inc, San Jose, CA USA.
Applied Intelligence (Dordrecht, Netherlands)
|November 12, 2021
Summary
This study introduces an efficient method to detect unauthorized subscription sharing using user profiles built from geolocation and device data. It helps service providers identify revenue loss and recover lost income.
Area of Science:
- Business Analytics
- Data Science
- Cybersecurity
Background:
- Subscription services face significant revenue loss due to unauthorized account sharing.
- Differentiating legitimate family sharing from unauthorized sharing is complex due to data volume and legitimate use cases.
Purpose of the Study:
- To develop an efficient solution for identifying unauthorized subscription account sharing.
- To help service providers mitigate revenue loss caused by illicit sharing.
Main Methods:
- Building user profiles by accumulating geolocation and device usage information from log data.
- Analyzing account usage patterns to estimate the risk of unauthorized sharing.
Main Results:
- The proposed solution effectively identifies a large number of shared accounts.
- The method provides a way for service providers to recoup lost revenue.
Conclusions:
- The developed solution offers an efficient approach to combatting unauthorized subscription sharing.
- Accurate identification of illicit sharing can significantly improve service provider revenue.
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