The Importance of AI and ML in Fighting Fraud: How Nacha Rules Highlight the Need for Advanced Technology in Banking

Addressing the Growing Threat of Business Email Compromise and Credit-Push Fraud: The Need for Advanced Monitoring and Technologies in Financial Institutions

Financial institutions and stakeholders are facing a growing threat from business email compromise (BEC) and credit-push fraud, prompting the need for increased monitoring and advanced technologies to combat these scams. Nacha recently introduced new rules aimed at creating a baseline level of ACH monitoring for all parties in the ACH Network, except for consumers.

The new rules are designed to enhance fraud detection through the credit-push payment flow, from origination to receipt at the receiving depository financial institution (RDFI). When fraud is detected, the ODFI can request the return of the payment, the RDFI can delay funds availability, and suspicious transactions can be returned without waiting for a request or customer claim.

According to PYMNTS Intelligence, fraudulent transactions in the U.S. have been on the rise, with 43% of banks reporting an increase last year. Impersonation schemes accounted for 12% of scams, and the financial impact of fraudulent transactions reached $3.2 million in 2023.

BEC scams have also seen a significant increase, with the FBI reporting a 17% rise in identified global exposed losses between December 2021 and December 2022. In the UK, draft legislation is being considered to mandate reimbursement for victims of authorized push payment fraud and implement a policy to delay payments if fraud is suspected.

To combat fraud, financial institutions are increasingly turning to technologies such as rules-based algorithms, artificial intelligence, and machine learning. PYMNTS Intelligence found that 60% of financial institutions use rules-based algorithms to combat fraud, with larger banks leading the way in adopting AI and ML technologies.

Overall, 56% of financial institutions plan to increase their use of AI and ML models to combat fraud, highlighting the importance of advanced technologies in the fight against BEC and credit-push fraud.

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