Tuned Global has introduced a new Service Manipulation Detection (SMD) solution designed for streaming platforms and rights holders.
The system focuses on protecting streaming data integrity while ensuring accurate royalty reporting.
The launch comes as artificial streaming activity continues to grow across the music industry.
Practices such as bot usage, click farms, and repeated scripted plays have raised concerns about inflated metrics.
In response, licensing agreements have started to shift. They no longer focus only on access and distribution.
Now, they require platforms to put clear systems in place. These systems must detect and prevent suspicious behavior.
They also need to operate across multiple levels, from individual tracks to users and broader network activity.
How Tuned Global Address The Problem
To address this, Tuned Global built the SMD solution directly into its platform. It does not rely on external tools.
Instead, it provides a structured layer of monitoring, automated safeguards, and governance processes.
These features help identify and reduce artificial activity that can distort play counts, chart rankings, and royalty distribution.
According to CEO Con Raso, the company developed this system to meet growing expectations from rights holders.
He explained that labels increasingly demand clear detection frameworks.
These frameworks need to follow how activity moves across the platform.
They look at tracks, users, and even wider network behavior to spot anything unusual.
At the same time, they rely on consistent rules to keep decisions fair.
They also maintain clear audit trails, so every action can be traced and reviewed when needed.
He also emphasized that platforms no longer just monitor activity. They take action as soon as issues appear.
That action can include removing artificial streams from royalty calculations.
After that, platforms should report the results clearly, so partners understand what was adjusted and why.
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How The System Works
The system operates across several layers.
At the track level, it detects unusual listening patterns, including abnormal play-to-listener ratios and repetitive playback behavior.
At the artist level, it analyzes broader catalog data to uncover patterns that may not appear in individual tracks.
Meanwhile, user-level monitoring evaluates listening habits, flagging excessive or unrealistic activity.
It also tracks network behavior, identifying suspicious logins, shared devices, and geographic inconsistencies.
In addition, the platform uses automated rules and statistical models to detect anomalies.
Each day, the system reviews listening activity in detail. It looks at plays per user and per track, building a clear picture of how the data moves.
From there, it compares the activity against defined thresholds.
When something goes beyond those limits, the system can step in and exclude it from royalty and chart calculations.
Every flagged case goes through a structured internal review process, supported by clear governance rules and audit documentation.
When manipulation is confirmed, the system can remove affected streams, adjust reporting, or suspend accounts in line with contractual terms.
Rights holders can also access detailed reports.
These include monthly summaries of excluded plays and broader insights into manipulation trends.
This level of transparency helps partners better understand how data is managed and protected.
The Future of SMD
The SMD solution works alongside Tuned Global’s existing tools, including authentication systems and content onboarding processes.
It also integrates with external partners such as Beatdapp, allowing additional layers of detection when needed.
Looking ahead, the company plans to expand the system further.
Looking ahead, the system will continue to evolve. Future updates will introduce more adaptive models that can respond to changes in behavior.
These improvements will include machine learning approaches.
With that, the system will be able to detect both familiar manipulation patterns and new ones as they begin to emerge.
Over time, the focus will shift toward predictive detection, supported by aggregated and anonymized data across its network.
This approach is expected to strengthen detection accuracy while improving industry-wide standards for fairness and transparency.
