Unmasking the confusion: Dispelling common misconceptions about contemporary antifraud practices

As the digital landscape continues to evolve, the need for effective fraud prevention strategies becomes increasingly crucial. A trend in the industry is consortium data sharing, a collaborative approach aimed at creating an omniscient network for instant fraud detection. However, a recent study sheds light on the significant structural limitations that undermine its effectiveness.

The future of fraud prevention lies in the quality of proprietary, context-rich data with clear provenance and direct operational relevance. Yet, the anonymisation processes in consortiums, necessary to preserve privacy, result in the loss of vital contextual information. This loss significantly hampers the ability to detect fraud trends over time and dilutes the effectiveness of such data.

One misconception about consortium approaches is the fallacy of scale without quality. While massive volumes of consortium data may be collected, they may not reveal insights not present in the original signals. Anonymisation processes in consortiums obscure details necessary for identifying and analyzing nuanced fraudulent activities, thus limiting the data's utility for fraud detection.

The goal of consortium data sharing is noble, but the overall reduction in data utility due to anonymisation illustrates the profound trade-offs required to balance privacy concerns with effective fraud detection. The effectiveness of modern fraud prevention techniques relies heavily on the quality of data, not the volume.

Organisations can create a more resilient and effective fraud prevention framework by building and maintaining high-quality datasets tailored to their specific operational needs and challenges. This approach ensures that the data used is relevant, context-rich, and provides the necessary insights for detecting and preventing fraud.

In the European Union, the WE BUILD Consortium, part of the EU Large Scale Pilot program focused on fraud prevention, identity, and access management, is working towards this goal. Although the specific companies involved are not publicly listed, the consortium's work and insights were highlighted by KuppingerCole in May 2025.

It is essential to remember that the effectiveness of advanced ML models is still dependent on the quality of data, the intricacy of feature engineering, the interpretability of models, and adherence to regulatory compliance and operational constraints. As we move forward, it is crucial to prioritise the quality of data over the quantity for effective fraud prevention.

The current date is June 20, 2025.

Unmasking the confusion: Dispelling common misconceptions about contemporary antifraud practices