Most financial institutions employ a layered fraud prevention strategy using multiple tools to prevent identity-related fraud. But many legacy fraud prevention systems are rules-based with gaps and limitations that may create customer friction or false positives, Visa said.

AIS works by leveraging Visa's artificial intelligence and predictive machine learning capabilities with application and identity-related data to generate a risk score for new account applications. The tool creates a two-digit Fair Credit Reporting Act (FCRA)-compliant identity fraud score in near real-time to help prevent fraud loss at the point of credit or loan application.

Visa's AI examines data points in areas including application velocity (the frequency of applications within a period of time), fraud and suspicious activity, and bankruptcy data across consumer identity elements, while incorporating data from government agencies, third-party data providers, law enforcement agencies, and self-reported data from consumers.

According to Visa, AIS is the only fraud solution that leverages virtually all U.S. approved/declined bank card application data and account level fraud data to detect and prevent potential fraud.

"With more than 14.7 billion data records breached since 2013, many of which include sensitive data such as name, tax ID number, and address, new account fraud has been a consistently growing challenge for financial institutions," Julie Conroy, research director at the Boston-based Aite Group, said. "Financial institutions are looking for solutions that can help effectively detect synthetic and stolen identities at the time of application. The consortium data and sophisticated analytics that power Visa's Advanced Identity Score promise to make it a valuable addition to financial institutions' control framework."