An emerging method of committing financial fraud is stealing checks, and the circumstances surrounding these scams are often bizarre and highly public. Checks are appropriated from the familiar blue United States Postal Service (USPS) collection boxes and rewritten, repurposed, and cashed by thieves. One victim had their check stolen twice from the same collection box, even after taking measures to get a new bank account and checks.
Check out the previous blog ”What ISO 20022’s extra data means in the fight against fraud” to learn more about fraud challenges and opportunities.
Social engineering scams are flourishing, putting a spotlight on the way unauthorized and authorized electronic transactions are interpreted under Regulation E (Reg. E). Banks and financial institutions (FIs) in the U.S. face increasing scrutiny, particularly with their responsibility toward customers in the event of a fraud report for a digital transaction and the associated liability when there’s a financial loss.
When it comes to fighting financial crime, challenges exist that go beyond the scope of merely stopping fraudsters or other bad actors. Some of the newest, advanced technologies that are being launched often have their own specific issues that must be considered during adoption stages. Addressing one of these concerns, when AI-based systems are used to gain operational efficiencies in financial crime, model fairness and data bias may occur when a system is skewed for or against certain groups or categories in the data. Typically, this stems from erroneous or unrepresentative data being fed into a machine learning model. Biased AI-systems may particularly represent a serious threat when reputations may be affected. In fraud detection, as one example, biased data and predictive models could erroneously associate last names from other cultures with fraudulent accounts, or falsely decrease risk within population segments for certain type of financial activities.
Perpetrating complex fraud isn’t overly taxing; it’s as easy for fraudsters to procure the tools to infiltrate financial institutions (FIs) as it is for any consumer to buy a smart home system on Amazon.
The ‘Travel Rule’ was first introduced by FinCEN in the US’s Bank Secrecy Act (BSA) and came into effect in the US on May 28, 1996. FinCEN’s ‘Travel Rule’ required financial institutions to pass on certain information to the next financial institution during the ‘transmittal of funds,’ which often refers to wire transfers. In 2012, the Financial Action Task Force (FATF) updated their FATF 40 recommendations to include similar ‘Travel Rule’ guidelines for wire transfers, as outlined in Recommendation 16.
Trace Fooshee is a Strategic Advisor in Aite-Novarica’s Fraud & AML practice, covering fraud and data security issues. Trace will be speaking at NICE Actimize’s virtual customer event, ENGAGE, on June 9, on a panel titled “Evolving Fraud Strategy across the Customer Journey.” Click here to learn more and to register for this industry-leading financial crime event.
Charles (Chuck) Subrt is the Director of Aite-Novarica’s Fraud & AML practice, and he covers anti-money laundering and compliance issues. Chuck will be speaking at NICE Actimize’s ENGAGE virtual customer event on June 9, on a panel titled, “ Seeing through the Corporate Facade – Money Laundering Threats in Corporate Banking”. Click here to learn more and to register for this industry-leading financial crime event.