MATCHING ENGINE
Tackling music industry fraud detection with machine learning and Microsoft Azure AI
Sector: Software development, Music copyright management
Size: 51-200 employees
Technology used: Microsoft Azure
Solution area: Data and AI solutions
AI-powered music rights management
In an era where digital royalties drive the music business, fraudulent registrations threaten trust and revenue. The Matching Engine is Spanish Point's proprietary enterprise software solution for the music industry. Built on Microsoft Azure, it is fully cloud-native and is used by customer across 13 markets in Europe and North America that distribute over €2.6 billion in royalty payments annually. Leveraging Microsoft Azure AI, Matching Engine empowers Collective Management Organisations (CMOs) to safeguard rights, modernise operations, and ensure accurate royalty distribution through intelligent, scalable fraud detection.
The challenge
Fraudulent song registrations were a long-standing problem in the music industry, with bad actors exploiting metadata similarities to divert royalties. This led to lost income for rightsholders and reputational risk for Collective Management Organisations. As the issue grew, so did the operational strain. Collective Management Organisations manually investigated and corrected payments, consuming time and resources. A technology solution was needed to process large volumes of data efficiently, automate fraud detection, improve royalty accuracy, and restore trust. CISAC, the international governing body for Collective Management Organisations, commissioned Spanish Point to create an industry-first pilot solution to combat fraud in the music industry.
The solution
Spanish Point designed and delivered new functionality in the Matching Engine. Built on Microsoft Azure, the solution used AI-driven metadata analysis, combining Azure Databricks, Azure AI services, and a Spanish Point–trained machine learning model for prediction. The cloud-native architecture enabled large-scale data processing and automated anomaly detection to identify and prevent fraudulent registrations efficiently. Developed in collaboration with CISAC, the predictive model analysed key features such as similarity to famous works, creator tenure, and submission rates.
The result
While still in its early stages, the pilot has already delivered strong results and is now being evolved into a core feature of a globally deployed product, improving accuracy, compliance, and operational efficiency. Machine learning and advanced algorithms were used to accelerate data processing and increase auto-match rates to achieve an uplift in operational efficiency across the industry.
Technical deep dive
The project aimed to build a scalable, AI-driven fraud detection system for music metadata. Requirements included real-time anomaly detection, integration with each Collective Management Organisations’ systems, secure cloud deployment, and compliance with industry standards for data privacy and royalty accuracy.
MLOps, AI and data engineering strategy
The solution utilised Azure Databricks for distributed data processing and machine learning model training. Metadata from millions of tracks was normalised and enriched using Python-based ETL pipelines. Anomaly detection algorithms applied clustering and similarity scoring to identify fraudulent patterns. Azure Cognitive Services enabled advanced text analytics for metadata validation. MLOps practices ensured continuous model improvement and scalability.
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