The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims.
The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.
Most financial institutions use a kind of AI called anomaly detection, a process through which computers can classify activity on a consumer’s account as either typical or suspicious.
Instead of learning from the expertise of a human with training data, the goal of unsupervised outlier detection is to help the human to see patterns they didn’t see before.
In this case, rather than identifying individual transactions as criminal based on the training data from the past, the AI would try to define groups of companies that share similar patterns of behavior.
In this way, we can learn how criminals are organizing themselves, and use the information in the future to detect these new kinds of money laundering automatically.