How to create a music streaming app: features, process and trends

Once money follows streams, someone will eventually try to manufacture streams.

IFPI treats this as a serious industry problem in its Global Music Report 2026. The report describes how fraudsters upload tracks and use bots to generate artificial plays, diverting royalty money from legitimate artists and right holders. It also points to generative AI as an accelerant, and Deezer’s own numbers show how quickly the volume is growing. In January 2026, the company said it was receiving more than 60,000 fully AI-generated tracks every day. By June, that figure had climbed to about 90,000 a day, representing more than 50% of all new uploads on peak days. Deezer also reported that up to 85% of streams on fully AI-generated tracks were fraudulent in 2025.

The tricky part is that unusual traffic is not automatically fraudulent. Music fandom can produce some very strange patterns all by itself.

Your anti-fraud system also needs some cultural awareness. A sudden wall of repeat plays might be bots. Or BTS dropped something.

That is why raw play counts tell you very little on their own. The system needs context: account behavior, device patterns, timing, geography, repetition, payment relationships, upload history, and other signals that help separate highly committed humans from automated manipulation.

Actual abuse can take many forms: bot traffic, replay farms, account abuse, duplicate uploads, fake artist profiles, stolen recordings, misleading metadata, or synthetic tracks produced and uploaded in bulk. Generative AI adds another identity question too: who made the track, whose voice is being used, and what should the listener be told about it?

A streaming platform needs controls on both the content and listening sides. That can include upload checks, duplicate detection, artist verification, suspicious-play rules, rate limits, credits, AI-use labels, takedown flows, and tools for the people reviewing questionable activity. IFPI also points to identity verification, content checks, platform-level fraud detection, and information sharing across the music ecosystem as key responses.

The level of protection depends on the product. A closed catalog supplied by a handful of labels has a very different risk profile from an open service where anyone can upload a thousand tracks before breakfast.

And waiting until the fraud becomes obvious gets expensive. Fake plays can distort payouts, charts, recommendation signals, and the visibility of legitimate artists before the team even realizes what is happening.

For a music service, fraud prevention and artist identity sit very close to the money. That alone earns them a place in the architecture.