Synthetic Identity Fraud (SIF) is a sophisticated and growing type of fraud where criminals create fictitious identities by combining real and fake information. Unlike traditional identity theft, where an existing person’s information is stolen, synthetic identities are fabricated, making them harder to detect and more challenging to track. This type of fraud poses a significant threat to financial institutions, businesses, and consumers. One study found that SIF cost financial institutions $20 billion in 2020 which ultimately means that some of these costs are passed down to the customers. In addition, research says that 1 in 50 children have been the victims of identity fraud which may take many years to uncover, time consuming, stressful and costly to resolve.
How Synthetic Identity Fraud Works:
Fraudsters typically use a combination of real data, such as a legitimate National Insurance (NI) or Social Security Number (SSN), paired with fabricated or incorrect details like a false name or address. They can bypass the usual verification processes. This is fuelled by the theft of 14 million identities every year. They then use this synthetic identity to apply for credit, open accounts, or make purchases. Over time, they build up a credit history for this fake identity, eventually leading to larger-scale fraud, such as taking out substantial loans or credit lines, and disappearing without repayment.
Detecting Synthetic Identity Fraud:
- Anomalies in Data: Look for inconsistencies or mismatched information within applications, such as discrepancies in SSN and name combinations or untraceable addresses.
- Credit History Irregularities: Monitor for unusual credit-building patterns, such as a sudden spike in credit activity or a lack of historical data.
- Behavioural Analytics: Use advanced analytics to detect abnormal behaviour patterns, such as multiple applications from the same IP address or rapid changes in account behaviour.
Preventing Synthetic Identity Fraud:
- Advanced Identity Verification: Employ multi-factor authentication and verification processes that go beyond basic identity checks, including biometric verification, device fingerprinting and document validation.
- Leverage Digital Footprint Analysis: This is designed to identify people without relying on ID documents or biometrics. This includes email and phone analysis and also social media profiles.
- Data Sharing and Collaboration: Participate in industry-wide data-sharing initiatives to track and share information about known synthetic identities.
- Machine Learning Algorithms: Implement AI and machine learning models that can detect and flag potential synthetic identities based on large data sets and predictive analytics. This may include user behaviour such as velocity rules, which analyses how quickly did the applicant complete the process and by what means.
Conclusion:
Synthetic Identity Fraud is a complex and evolving threat that requires vigilance and advanced detection methods. By combining sophisticated technology with proactive monitoring and industry collaboration, businesses and financial institutions can better protect themselves and their customers from this insidious form of fraud.
To find out more on how to protect yourself from cybercrime speak to our team at Archway Securities.
