Type and Duration
Innosuisse, April 2024 until January 2026Coordinator
Liechtenstein Business SchoolMain Research
Business Process ManagementDescription
The music industry is facing a growing challenge: unpaid copyright royalties are expected to soon reach nearly $8 billion annually, up from $2.5 billion currently. In a groundbreaking initiative to combat this unfair distribution, the Eastern Swiss start-up HELGA.works is launching an Innosuisse project with the Professorship for Data Science & Artificial Intelligence at the University of Liechtenstein.The aim of the project is to help artists get the royalties they are entitled to. The key challenge is to identify the legitimate recipients despite inadequate data quality, a problem that particularly, but not only, affects niche artists. By using state-of-the-art machine learning models, such as graph neural networks, which can handle incomplete and inaccurate data, the aim is to create a solution that is not only innovative, but also remains simple and interpretable. The aim is to develop a prediction service that estimates the risk and amount of unpaid royalties.
The potential product solution has already aroused significant interest among investors as well as in the industry, including from renowned music publishers and artists, and promises a competitive advantage for HELGA.works and its customers. Even rough predictions can be very helpful in guiding efforts to further analyze potentially unclaimed royalties. Based on a model prediction, an agent can decide whether to further investigate the causes and potential amounts of unpaid royalties from existing customers or to focus marketing on potential customers who would benefit most from HELGA.works' offerings.
In addition to creating economic value, the proposed solution also supports niche artists in particular in receiving their legitimate remuneration and thus contributes to musical and cultural diversity. Together with the University of Liechtenstein as a research partner, a Swiss start-up is involved as an implementation partner in the Innosuisse project, whose employees have knowledge and experience in the areas of machine learning and data science.
