Financial crime is becoming increasingly sophisticated, evolving alongside the digital economy and the data-first era. Synthetic identity fraud, one of the fastest accelerating forms of financial crime in the US, is a particularly complex security threat contributing to the growing risk landscape and reshaping investment priorities for financial services organisations (FSOs).
Cyber-criminals are exploiting gaps in fraud prevention capabilities and the unprecedented shift to digital channels to perpetrate financial fraud at scale, like using synthetic identities.
Research from Aite Group approximates that by 2023, synthetic identity fraud for unsecured US credit products will total USD 2.42 billion* annually, and will account for the majority of debit deposit account (DDA) losses associated with application fraud, which are forecasted to reach almost USD 1 billion* annually.
The historical lack of effective solutions to help detect synthetic identity fraud, coupled with the absence of source data to verify individual identities, makes this issue even more challenging. Advanced analytics and AI-driven fraud prevention solutions are powerful tools in the ability to proactively mitigate fraud with real-time detection, decision making, and responsiveness.
What is synthetic identity fraud?
Synthetic identity fraud stems from criminals falsifying identities to steal money via new credit lines or accounts. With this fraud, only part of the identity is legitimate, or even none of it. Identities are often cultivated for extended lengths of time and are infrequently detected because there’s no specific consumer victim to communicate suspicious activity.
There are two categories of synthetic fraud:
Manipulated: Minimal alterations are made to an authentic identity, like the address and date of birth (DOB), while retaining the SSN and name. This can then be used to access credit, or attached to an existing credit product to fast-track identity building.
Manufactured: Data is patched together from multiple real identities to create a new false identity. Alternatively, the information might be entirely fabricated apart from a valid SSN or derived from a random sequence of numbers.
Synthetic identity fraud drivers
As FSOs amplify digital transformation initiatives and eliminate physical interactions, a climate susceptible to synthetic identity fraud has emerged, making it difficult to prove that an identity is authentic.
Fraudsters can elude detection for years when using stolen SSNs from children because children can’t apply for credit until they’re 18 years old. Young adults who are just creating credit identities for the first time, individuals who are new to the country, divorced women, incarcerated individuals, and the elderly are just a few of the demographics vulnerable to exploitation by criminals because they represent identity groups that are challenging to authenticate and difficult for FSOs to decline.
Other enablers of synthetic fraud include:
siloed identity verification data sources;
traditional tools that fail to detect fraud and prevent financial losses once a synthetic identity enters an institution and matures;
inaccurate identity data across data sources, leading to more common red flags;
lack of a single source of truth surrounding identity verification.
All of these drivers have added a new dimension to the scope of cyber-vandalism and sparked urgency for new investments in automated fraud prevention and management solutions. FSOs need to incorporate protection across every phase of the application lifecycle, execute early monitoring of new accounts, and continuously monitor all associated account data.
Tackling synthetic fraud
Data and intelligent technologies are the most effective weapons to fight synthetic fraud. Temporary fixes, like linking photos or videos to identity verification processes, have been shown to reduce fraud, but won’t be sustainable long-term given the proliferation of deep fakes. The key is to use copious quantities of data to authentic identities and close information gaps and use advanced analytics to manage risks across the entire application lifecycle.
An AI-powered enterprise fraud management system can connect all data types and sources, and facilitate the following regarding identity authentication:
evidence of a person’s existence concerning their specified address and information;
data corroboration to establish trust and ensure authentic applicants can get through the system;
verifying that the information is a fit for the applicant’s age, occupation, and nationality.
FSOs should strategically support these intelligent capabilities by further investigating anomalies and gaps. Additionally, velocity should be monitored to prevent multiple applications from being submitted from the same device, email, or phone across a short time period.
Together, this provides a modern control framework from which FSOs can guard their organisation against synthetics, reimagine the customer experience, and advance transformation initiatives.
Embedded fraud management fuels innovation
The future of digital fraud is already here, and FSOs must enable rapid digitisation while protecting high-value assets against complex threats. As FSOs prepare for the next iteration of digitisation and its risks and opportunities, they need a smart fraud management solution that drives a holistic approach to fraud detection and prevention.
This editorial was first published in our Financial Crime and Fraud Report 2021 - How to Fight Fraud and Master KYC, Onboarding & Digital ID, which provides a comprehensive overview of the major trends driving growth in fraud prevention, identity management, digital onboarding and KYC, transaction monitoring, financial crime compliance, regtech, and more.
About Glenn Fratangelo
About NICE Actimize
NICE Actimize, the industry’s largest and broadest provider of financial crime, anti-money laundering, enterprise fraud and compliance solutions is the leader in Autonomous Financial Crime Management. NICE Actimize drives the Autonomous journey with ActOne for investigation and case management, as well as X-Sight, a cloud-based Financial Crime Risk Management Platform-as-a-Service.
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