New Fed Report Recommends ‘Comprehensive Approach’ To Fighting Synthetic Identity Fraud

As a one-size-fits-all solution does not exist for the increasing problem of synthetic identity fraud, The Federal Reserve reports that experts suggest a “comprehensive approach” for the challenge. A strategy that is “multi-layered” that makes use of manual and digital information analysis provides the best odds of organizations discovering and alleviating fraud caused by synthetics, according to the Fed’s “Mitigating Synthetic Identity Fraud In The U.S. Payment System” report.

SentiLink noted that it found synthetic identities in between 0.3 percent and 0.6 percent of new accounts, according to the report. However, the software firm forecasted that the rate of approved accounts made to a synthetic identity could be as much as 2.7 percent of all new accounts for certain financial institutions. Synthetic identities also comprise just over 20 percent of all losses in a given loan portfolio while they only comprise under 1 percent of all loans, according to a study the Fed cited by artificial intelligence (AI) firm Coalesce.

Fraud experts say per the report that organizations that are most successful at mitigating synthetic identity fraud extend their observation beyond basic personally identifiable information like name, date of birth, address and social security number. They also noted the advantages of “robust link analysis processes” that examine different banking instruments to find relationships or similar properties of synthetic identities.

The Fed also noted that it observes “increased use” of machine learning (ML) and artificial intelligence (AI) to find and alleviate synthetic identity fraud. According to the Fed, “AI and machine learning can create efficiencies for financial institutions, while also saving time and labor costs.”

As it stands, EMV cards and other anti-fraud procedures have had a large impact on fraud behavior. This is said to have brought about an increase in synthetic identity fraud, where hackers make great efforts to make fake individuals by combining the purloined details of real people. They then make card accounts at scale and slowly drain those that aren’t detected just long enough to get trust.