The Growing Threat of Synthetic Identity Fraud
Synthetic identity fraud is rapidly becoming one of the most damaging threats to the UK’s financial sector, with losses exceeding £300 million in 2024 and continuing to rise. Unlike traditional identity theft, synthetic fraud combines real and fabricated data – such as a valid National Insurance number paired with a fictional name – to create a new, seemingly legitimate profile.
These synthetic identities often masquerade as genuine customers: opening accounts, conducting transactions, and even establishing credit over time. However, their ultimate purpose is to exploit this legitimacy by defaulting on loans or maxing out credit before disappearing. The financial impact is significant, and detection methods must evolve to combat this sophisticated threat.
Moving Beyond Onboarding Checks
Fraud detection cannot be limited to the application stage. Many synthetic profiles exhibit genuine customer behavior for extended periods – sometimes months or years – before executing their fraudulent intent. Therefore, detection must be an ongoing process rather than a one-time event.
To remain effective, financial institutions require tools that can identify subtle, hidden signals throughout the customer lifecycle. Jaywing’s machine learning development environment, Archetype, enables organizations to rapidly develop, train, and deploy customized fraud models designed to detect synthetic patterns that static rules often overlook. With Archetype, organizations can:
- Detect subtle anomalies across large datasets
- Score both new and existing accounts in real-time
- Adapt models in response to emerging threats
- Automate fraud detection while maintaining transparency and compliance
This adaptability is crucial in defending against sophisticated, slow-moving fraudulent activities.
Implementing a Layered Detection Approach
An effective synthetic identity fraud strategy must encompass multiple touchpoints across the customer lifecycle:
- Identity Verification at Onboarding: Utilize biometric checks and real-time data to verify applicants’ authenticity beyond document validation.
- Device and Behavioral Analytics: Monitor subtle signals such as uniform typing speeds, lack of cursor movement, and repeated device use across different identities to detect non-human or scripted behavior.
- Continuous Monitoring: Track transaction patterns, personal data changes, and digital activity to identify dormant synthetic identities.
- Machine Learning Risk Scoring: Leverage tools like Archetype to identify complex combinations of traits and behaviors that indicate synthetic identities.
- Industry Collaboration: Share intelligence through methods like federated learning to strengthen fraud defenses across the financial sector without compromising sensitive data.
Evolving Fraud Prevention Strategies
Synthetic identity fraud represents a persistent, long-term threat rather than a one-time breach. Consequently, fraud prevention measures must be equally enduring. By combining advanced behavioral analytics, sophisticated modeling techniques, and sector-wide collaboration, financial institutions can protect themselves against threats that may not be apparent during initial onboarding checks.
The evolving nature of synthetic identity fraud necessitates continuous improvement in detection and prevention methods. Financial institutions must remain vigilant and adapt their strategies to stay ahead of these sophisticated fraudulent activities.
