Mail-related check fraud is surging. In 2022, check fraud-related SARs filed to FinCEN reached over 680,000, almost doubling the previous year’s number of filings. In response to a nationwide surge of stolen check fraud schemes targeting the U.S. mail system, FinCEN has released a new alert to financial institutions, with financial red flags to help identify and report these crimes.
Using Traditional Methods for Modern Schemes: Criminals Continue to Choose Check Fraud
Check fraud is an antiquated scheme to defraud victims, but criminals take advantage of this traditional method to exploit institutions and their customers in modern ways. Check fraud is a preferred means to commit money laundering, with criminals “washing” checks which may then be copied and reprinted several times. The fraudsters may rely on money mules to deposit altered or counterfeit checks into criminally controlled accounts. Fraudulent checks can also be sold in exchange for cryptocurrencies and used on various dark web platforms in an attempt to anonymize the criminal activity.
Protecting your Institution: FinCEN’s Red Flags for Mail-Related Check Fraud
“Fraud, including check fraud, is the largest source of illicit proceeds in the United States and represents one of the most significant money laundering threats to the United States, as highlighted in the U.S. Department of the Treasury’s most recent National Money Laundering Risk Assessment and National Strategy for Combatting Terrorist and other Illicit Financing.”
– FinCEN Alert on Nationwide Surge in Mail Theft-Related Check Fraud Schemes Targeting the U.S. Mail
FinCEN has identified the following financial red flags to help financial institutions detect, prevent, and report suspicious activity related to mail-related check fraud:
- Non-characteristic large withdrawals on a customer’s account via check to a new payee.
- Customer complains of a check or checks stolen from the mail and then deposited into an unknown account.
- Customer complains that a check they mailed was never received by the intended recipient.
- Checks used to withdraw funds from a customer’s account appear to be of a noticeably different check stock than check stock used by the issuing bank and check stock used for known, legitimate transactions.
- Existing customer with no history of check deposits has new sudden check deposits and withdrawal or transfer of funds.
- Non-characteristic, sudden, abnormal deposit of checks, often electronically, followed by rapid withdrawal or transfer of funds.
- Examination of suspect checks reveals faded handwriting underneath darker handwriting, giving the appearance that the original handwriting has been overwritten.
- Suspect accounts may have indicators of other suspicious activity, such as pandemic-related fraud.
- New customer opens an account that is seemingly used only for the deposit of checks followed by frequent withdrawals and transfer of funds.
- A non-customer that is attempting to cash a large check or multiple large checks in-person and, when questioned by the financial institution, provides an explanation that is suspicious or potentially indicative of money mule activity.
The Power of Verafin’s Consortium Analytics in Fighting Stolen Check Fraud
Traditional check fraud approaches that focus solely on MICR or image analysis are not effective at tackling the growing challenge posed by stolen checks, as these are legitimate authorized checks that are being deposited by the wrong payee. Through the power of the Verafin network we can link stolen checks to the risk of the deposit and significantly improve your ability to manage this growing threat. Whether it is new account fraud or a deposit scam, understanding the risk associated with the deposit is the solution to stolen check fraud.
Verafin’s Consortium Analytics approach leverages insights from our network of thousands of U.S. financial institutions to provide a deeper understanding of deposit-related risk, without sharing Personally Identifiable Information (PII) between institutions, allowing your institution to detect in-clearing check fraud more effectively. Our deposit fraud machine learning models identify higher risk deposits at institutions within the Verafin network and leverages this insight to inform risk at the in-clearing check institution.