Uncovering Synthetic Identity Fraud
A new report from Lexis Nexis
Lexis Nexis just published an important and interesting document about synthetic identity fraud and its impact on organizations, including the signs of synthetic identity fraud and the challenges in detecting it.
- Synthetic identity fraud is a type of fraud in which criminals create fictitious identities that don’t directly relate to any real person using manipulated or manufactured details.
- Synthetic identity fraud is a low-risk, high-reward crime for fraudsters, as they invest time and money upfront to establish strong credit profiles and gain the trust of lenders and institutions before cashing out and disappearing.
- Synthetic identity fraud is a growing problem, with almost 3 million identities in circulation in the UK displaying high-risk factors indicating synthetic identity fraud.
- Synthetic identity fraud poses significant dangers to organizations, as fraudsters can keep accounts open for extended periods, max out credit lines, and disappear without a trace, leading to high-frequency and high-value losses.
- Organizations need to balance approval speeds with increased confidence by implementing fraud prevention approaches that accurately recognize legitimate customers, identify risk factors for synthetic identity fraud, and flag suspicious accounts.
haracteristics of synthetic farms and houses, as mentioned in the document, include:
- Synthetic Farms:
- Multiple identities linked to properties: Synthetic farms are properties, often in isolated rural locations, that have multiple identities associated with them.
- Thin financial data: Identities constructed on thin financial data, such as credit bureau and short-term loan data, with no other proof of existence from electoral roll or government sources.
- Apparent linkages between locations: Analysis reveals apparent linkages between synthetic farms operating across different parts of the UK, suggesting broader organized criminal networks operating across multiple geographic locations.
- Easily accessible and unsecure post boxes: Synthetic farms often have easily accessible and unsecure post boxes, allowing fraudsters to intercept mail.
- Synthetic Houses:
- Clustered identities within a small geographic area: Synthetic houses are residential properties with a high concentration of identities residing within a small geographic area.
- Manipulated personally identifiable information (PII): High levels of name and date of birth manipulation, along with other credit application fields, such as reason for loans, income brackets, and job type cycling through a limited number of common responses.
- Limited validation through trusted sources: Only a small number of identities associated with the address can be validated through trusted government sources, while the rest rely on credit bureau and short-term loan records.
- Repeated use to gain access to credit: Synthetic houses are repeatedly used to gain access to credit, although credit providers may not necessarily see this trend.
According to the document, 85% of synthetic identities were not flagged by third-party fraud models. This indicates that traditional approaches and existing fraud prevention controls often overlook or fail to detect synthetic identity fraud.
Organizations face several challenges in identifying and quantifying losses from synthetic identity fraud, as outlined in the document:
- Lack of Consumer Victims: Unlike traditional fraud where an entire identity is stolen and used, synthetic identity fraud often does not have a specific consumer victim. This makes it difficult for organizations to detect and stop fraud since there is no clear evidence of fraudulent activity reported by consumers.
- Extended Account Lifespan: Fraudsters using synthetic identities can keep accounts open for months or even years, gradually building credit standing and credit line increases. This allows them to maximize gains before eventually disappearing without a trace. As a result, organizations may not realize the presence of synthetic identity fraud until the account charges off.
- Categorization as Bad Debts: When synthetic fraud accounts eventually default, they are often categorized as bad debts rather than fraud losses. This is because there is no clear evidence of fraudulent activity, making it challenging for organizations to identify and address the synthetic fraud problem effectively.
- Inconsistencies and Disagreements: There can be inconsistencies within organizations regarding what constitutes a synthetic identity and whether it should be classified as a fraud or credit problem. This lack of consensus further complicates the identification and quantification of losses from synthetic identity fraud.
Overall, the absence of clear consumer victims, extended account lifespans, categorization challenges, and internal disagreements make it difficult for organizations to track and measure the extent of losses caused by synthetic identity fraud.