Synthetic identity fraud heats up

Countering this complex challenge involves smart technologies and solutions capable of adapting and advancing as new fraud threats emerge.

Of the many fraud trends that shook the financial services industry during the pandemic, synthetic identity fraud (SIF) was arguably the biggest headliner. It may have flown under the radar for many financial institutions prior to the pandemic, but has rapidly garnered more scrutiny as monetary losses and media coverage have both escalated.

SIF involves fabricating a new identity procured from a mixture of fake information and authentic personally identifiable information stolen from multiple victims. The synthetic identity might use a legitimate Social Security number and birthdate from a real person, as well as random false data to fill in any gaps.

Synthetic fraud attacks have intensified in diversity, velocity and scale as fraudsters continue to leverage new approaches and technologies to achieve their goals. SIF is well suited to innovation because the manipulated or manufactured identity seems to be legitimate, and it has spawned a host of fresh fraud trends.

Sign up for the free BAI Banking Strategies newsletter and get industry insights delivered to your inbox.

By clicking the Subscribe button, you acknowledge that you have read our Privacy Policy and Terms of Use and agree to be bound by them.

Synthetic identity BNPL fraud: Buy now, pay later has risen in popularity in recent years due in part to its accessibility for consumers with poor, limited or nonexistent credit history. But the rapid growth of this new payment service has already exposed a number of vulnerabilities that make it an attractive target for bad actors. Distributed payment installments also enable fraudsters to cast a broader net for attacks. Because fraudsters are using synthetic identities for the initial purchase, it’s extremely difficult to identify and track these criminals.

Child synthetic identity fraud: Bad actors leverage various social-engineering techniques to illegally procure children’s data from sources such as school district systems, social media accounts and even the dark web. The data is used to compile a convincing synthetic identity that can be used to fuel various types of fraud. Because in many cases the crime is only discovered once the child comes of age and applies for credit or a federal student loan, the synthetic identity can be successfully used for years without anyone noticing.

Synthetic auto-loan fraud: The mainstream adoption of automation and digitalization in the auto-finance industry is presenting numerous challenges for lenders. Current conditions make it possible for fraudsters to use synthetic identities to apply for auto loans at scale. This further streamlines identification checks and onboarding to enable easy access to financing. Fraud losses from falsified auto-loan applications are expected to grow as lenders push their digital transformation agendas forward.

Deepfake synthetic identity fraud: Deepfake synthetic identities are increasingly being used to help fraudsters secure remote jobs, primarily in technology. Deepfakes and synthetic content, including identities, lend verisimilitude to various social-engineering scams. In this latest manifestation, the approach is deployed in online job interviews with the goal of accessing financial data, corporate databases, sensitive customer data and other valuable information frequently associated with roles in technology.

The inclusion of both authentic and falsified information helps synthetic identities bypass existing fraud-detection models, enabling criminals to establish credibility. The patience many fraudsters exhibit in nurturing accounts and mimicking legitimate account holder behaviors also helps. Financial institutions also struggle with the lack of a single source of truth for identity verification, siloed identity verification data sources and inaccurate identity data across data sources.

Fraud prevention is a balancing act—solutions and approaches must be robust enough to stop criminals before they can infiltrate the system and commit fraud, yet optimized in such a way that the digital experience isn’t diminished for real customers.

The use of artificial intelligence and machine learning to detect and intervene in real time can enable FIs to efficiently combat synthetic identities while addressing this need for balance. This strategy relies on an abundance of high-quality data to authenticate identities and address any information voids, and on advanced analytics to recognize and manage risks.

Synthetic identity fraud has been successfully weaponized by bad actors. Countering this complex challenge involves smart technologies and solutions capable of adapting and advancing alongside the progression of new and emerging fraud threats.

Eric Tran-Le is head of Actimize Premier at NICE Actimize.

Relevant insights for financial services organizations, regardless of where they are in their digital journey, can be found in the BAI Executive Report, “Where does digital transformation go from here?”