By Husnain Bajwa, SVP of Product, Risk Solutions, SEON
As digital threats escalate, businesses are desperately seeking comprehensive solutions to counteract the growing complexity and sophistication of evolving fraud vectors. The latest industry trend – consortium data sharing – promises a revolutionary approach to fraud prevention, where organisations combine their data to strengthen fraud defences.
It’s understandable how the consortium data model presents an appealing narrative of collective intelligence: by pooling fraud insights across multiple organisations, businesses hope to create an omniscient network capable of instantaneously detecting and preventing fraudulent activities.
And this approach seems intuitive – more data should translate to better protection. However, the reality of data sharing is far more complex and fundamentally flawed. Overlooked hurdles reveal significant structural limitations that undermine the effectiveness of consortium strategies, preventing this approach from fulfilling its potential to safeguard against fraud. Here are several key misconceptions about how consortium approaches fail to deliver promised benefits.
Fallacy of Scale Without Quality
One of the most persistent myths in fraud prevention mirrors the trope of enhancing a low-resolution image to reveal more explicit details. There’s a pervasive belief that massive volumes of consortium data can reveal insights not present in any of the original signals. However, this represents a fundamental misunderstanding of information theory and data analysis.
To protect participant privacy, consortium approaches strip away critical information elements relevant to fraud detection. This includes precise identifiers, nuanced temporal sequences and essential contextual metadata. Through the loss of granular signal fidelity required to anonymise information to make data sharing viable, said processes skew data while eroding its quality and reliability. The result is a sanitised dataset that bears little resemblance to the rich, complex information needed for effective fraud prevention. Further, embedded reporting biases from different entities can likewise exacerbate quality issues. Knowing where data comes from is imperative, and consortium data frequently lacks freshness and provenance.
Competitive Distortion is a Problem
Competitive dynamics can impact the efficacy of shared data strategies. Businesses today operate in competitive environments marked by inherent conflicts, where companies have strategic reasons to restrict their information sharing. The selective reporting of fraud cases, intentional delays in sharing emerging fraud patterns and strategic obfuscation of crucial insights can lead to a “tragedy of the commons” situation, where individual organisational interests systematically degrade the potential of consortium information sharing for the collective benefit.
Moreover, when direct competitors share data, organisations often limit their contributions to non-sensitive fraud cases or withhold high-value signals that reduce the effectiveness of the consortium dynamics.
Anonymisation’s Hidden Costs
Consortiums are compelled to aggressively anonymise data to sidestep the legal and ethical concerns of operating akin to de facto credit reporting agencies. This anonymisation process encompasses removing precise identifiers, truncating temporal sequences, coarsening behavioural patterns, eliminating cross-entity relationships and reducing contextual signals. Such extensive modifications limit the data’s utility for fraud detection by obscuring the details necessary for identifying and analysing nuanced fraudulent activities.
These anonymisation efforts, needed to preserve privacy, also mean that vital contextual information is lost, significantly hampering the ability to detect fraud trends over time and diluting the effectiveness of such data. This overall reduction in data utility illustrates the profound trade-offs required to balance privacy concerns with effective fraud detection.
The Problem of Lost Provenance
In the critical frameworks of DIKA (Data, Information, Knowledge, Action) and OODA (Observe, Orient, Decide, Act), data provenance is essential for validating information quality, understanding contextual relevance, assessing temporal applicability, determining confidence levels and guiding action selection. However, once data provenance is lost through consortium sharing, it is irrecoverable, leading to a permanent degradation in decision quality.
This loss of provenance becomes even more critical at the moment of decision-making. Without the ability to verify the freshness of data, assess the reliability of its sources or understand the context in which it was collected, decision-makers are left with limited visibility into preprocessing steps and a reduced confidence in their signal interpretation. These constraints hinder the effectiveness of fraud detection efforts, as the underlying data lacks the necessary clarity for precise and timely decision-making.
The Realities of Fraud Detection Techniques
Modern fraud prevention hinges on well-established analytical techniques such as rule-based pattern matching, supervised classification, anomaly detection, network analysis and temporal sequence modelling. These methods underscore a critical principle in fraud detection: the signal quality far outweighs the data volume. High-quality, context-rich data enhances the effectiveness of these techniques, enabling more accurate and dynamic responses to potential fraud.
Despite the rapid advancements in machine learning (ML) and data science, the fundamental constraints of fraud detection remain unchanged. The effectiveness of advanced ML models is still heavily dependent on the quality of data, the intricacy of feature engineering, the interpretability of models and adherence to regulatory compliance and operational constraints. No degree of algorithmic sophistication can compensate for fundamental data limitations.
As a result, the core of effective fraud detection continues to rely more on the precision and context of data rather than sheer quantity. This reality shapes the strategic focus of fraud prevention efforts, prioritising data integrity and actionable insights over expansive but less actionable data sets.
Evolving Into Trust & Safety: The Imperative for High-Quality Data
As the scope of fraud prevention broadens into the more encompassing field of trust and safety, the requirements for effective management become more complex. New demands, such as end-to-end activity tracking, cross-domain risk assessment, behavioural pattern analysis, intent determination and impact evaluation, all rely heavily on the quality and provenance of data.
In trust and safety operations, maintaining clear audit trails, ensuring source verification, preserving data context, assessing actions’ impact, and justifying decisions become paramount.
However, the nature of consortium data, which is anonymised and decontextualised to protect privacy and meet regulatory standards, cannot fundamentally support clear audit trails, ensure source verification, preserve data context, and readily assess the impact of actions to justify decisions. These limitations showcase the critical need for organisations to develop their own rich, contextually detailed datasets that retain provenance and can be directly applied to operational needs to ensure that trust and safety measures are comprehensive, effectively targeted, and relevant.
Rethinking Data Strategies
While consortium data sharing offers a compelling vision, its execution is fraught with challenges that diminish its practical utility. Fundamental limitations such as data quality concerns, competitive dynamics, privacy requirements and the critical need for provenance preservation undermine the effectiveness of such collaborative efforts. Instead of relying on massive, shared datasets of uncertain quality, organisations should pivot toward cultivating their own high-quality internal datasets.
The future of effective fraud prevention lies not in the quantity of shared data but in the quality of proprietary, context-rich data with clear provenance and direct operational relevance. By building and maintaining high-quality datasets, organisations can create a more resilient and effective fraud prevention framework tailored to their specific operational needs and challenges.
