1.2 PROBLEM FRAMING
In the face of mounting concerns surrounding their platform, a leading video-streaming service provider recognized the need for an intelligent solution to combat piracy proactively. With millions of daily active users spanning diverse geographical regions, and a vast library encompassing live sports, movies, TV series, and more, their dedicated anti-piracy team encountered numerous critical challenges:
• Uncertainty regarding typical pirate behavior • Inability to pinpoint users at elevated risk of piracy • Dependence on manual and subjective decision-making procedures • Limited visibility into piracy catalysts and indicators • Inability to adapt to evolving piracy patterns
To overcome these challenges, the streaming provider joined forces with Fractal to deconstruct the overarching problem into smaller objectives. We aimed to create a robust anti-piracy framework by leveraging our expertise in AI. The following requirements were identified for the framework:
Automated risk scoring of accounts, based on streaming behaviour, IP address, device usage and other attributes.
AUTOMATED RISK SCORING
Identification of other accounts with attributes and behaviours similar to identified pirates.
BEHAVIOR IDENTIFICATION
Near real-time monitoring of IP addresses and devices being used across multiple accounts and geographic regions.
IP ADDRESS / DEVICE TRACKING
Investigating suspicious devices to identify how those are used across accounts, to terminate or suspend the account.
DEVICE USAGE MONITORING
Investigating suspicious devices to identify how those are used across accounts, to terminate or suspend the account.
DEVICE USAGE MONITORING
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