In 2020, the Federal Reserve announced the FraudClassifier model Off-site), a set of tools and materials to help organizations classify fraud in a consistent way. The FraudClassifier model is now being used by a variety of organizations, either in their internal fraud detection processes or via third-party solutions. They have found that better fraud data can lead to a better defense against fraud.
A streamlined reaction to fraud
Stephanie Ziegler is a senior fraud investigations manager and certified fraud examiner with SELCO Community Credit Union, a $2.7 billion financial institution serving members in 27 counties in Oregon and eight counties in Washington. Under SELCO’s decentralized fraud model, branch and operational teams handle initial research and disputes, then escalate cases as needed to Ziegler’s team, which handles all Bank Secrecy Act (BSA) and anti-money laundering (AML) obligations, such as AML filings and coordination with law enforcement.
“Because so many people at SELCO have responsibility for different aspects of fraud events, the intuitive FraudClassifier model really streamlines our process,” Ziegler said. “With the FraudClassifier model, various SELCO teams work in the same system, answer the same questions about a fraud incident and use the same definitions, rather than selective opinions. This standardization ensures our fraud data is more consistent and accurate, gives us a head start in investigations, and makes it easier for us to pivot quickly to address the changing fraud landscape.”
Finding fraud across payment silos
“Many in the industry acknowledge that fraud is growing and it can be a challenge to find enough resources to invest in fraud mitigation,” said Rene Perez of technology service provider Jack Henry™. “Compounding their concerns, an organization’s ACH fraud people may not always speak to the card fraud people or the check fraud people or the faster payments fraud people to see broader trends across these and other payment rails. The FraudClassifier model will help you understand fraud trends you did not know you had in your institution” by facilitating internal consistency in fraud classification, more robust fraud tracking and responses, and customer education on current fraud methods and prevention. For these reasons and others, Jack Henry requires customers who use the Jack Henry Financial Crimes Defender™ to classify their suspected fraud and AML cases using the FraudClassifier model.
Since 2013, the Federal Reserve has collaborated with a wide array of industry stakeholders to advance payment security and support fraud mitigation. Notable achievements have included Federal Reserve publications on emerging areas of fraud, such as synthetic identity fraud (Off-site); engagement at conferences and other venues that have dramatically increased the industry’s fraud awareness and collective action; and Federal Reserve-led industry work groups tackling important issues, such as accurate and consistent classification of authorized and unauthorized fraud and scams.
Note: The FraudClassifier model was developed by a cross-industry work group to provide a consistent way to classify and understand how fraud occurs across the payments industry. The FraudClassifier model is not intended to result in mandates or regulations, and does not give any legal status, rights or responsibilities, nor is it intended to define or imply liabilities for fraud loss or create legal definitions, regulatory or reporting requirements. Absent written consent, the FraudClassifier model may not be used in a manner that suggests the Federal Reserve endorses a third-party product or service.
“Jack Henry” and “The Jack Henry Financial Crimes Defender” are registered trademarks of Jack Henry & Associates, Inc®.
