Fraud-Proof Revenue Division on Subscription Platforms
This addresses fraud prevention for subscription platforms like streaming services, offering a novel mechanism rather than incremental improvements.
The paper tackles the problem of fraud in subscription platform revenue division by proposing manipulation-resistant mechanisms, showing that a widely used streaming platform mechanism fails to prevent fraud and makes detection intractable, while introducing a novel rule called ScaledUserProp that satisfies all three manipulation-resistance axioms and is supported as fairer by experiments with real-world and synthetic data.
We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules.