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The rise in identity fraud has set new records in 2022. This was put in motion by fraudulent SBA loan applications totaling nearly $80 billion being approved, and the rapid rise of synthetic identity fraud. Almost 50% of Americans became victims of identity fraud between 2020 and 2022. The National Council on Identity Theft Protection found that, on average, there is an identity theft case every 14 seconds. Last year alone, businesses lost $20 billion to synthetic identity fraud, $697B from bots and invalid traffic, and more than $8 billion from international revenue share fraud (IRSF).
Cyberattackers use a combination of real and fake personal information, including Social Security numbers, birthdates, addresses, employment histories and more, to create fake or synthetic identities.
Once created, they’re used to apply for new accounts that fraud detection models interpret as a legitimate new identity and grant credit to the attackers. It’s the fastest growing form of identity fraud today because it’s undetectable by many organizations’ existing fraud prevention techniques, models, and security stacks.
Synthetic identity fraud is the most difficult to identify, as combining real and fictitious identity data can easily trick existing fraud detection models, gaining account and credit privileges for attackers. Source: Federal Reserve, Mitigating Synthetic Identity Fraud in the U.S. Payment System.Existing fraud models fall short
Fraud prevention analysts are overwhelmed with work as the variety of the evolving nature of bot-based and synthetic identity fraud proliferates globally. Their jobs are so challenging because the models they’re using aren’t designed to deal with synthetic identities or how fast fraud’s unstructured and changing nature is.
Approaches using structured machine learning algorithms are effective to a point. However, they’re unable to scale and capture the nuanced type of attacks synthetic identities are creating today. Machine learning (ML) and artificial intelligence (AI) techniques to capture the nuanced na …