Date of Event: November 12–13, 2024
Event: Global Anti-Scam Summit Americas 2024
The Scam Classification and Measurement panel brought together key players in the fight against fraud to discuss the Federal Reserve’s Scam Classifier Model and its implications for the financial industry. This session, moderated by Trace Fooshée of Datos Insights, explored how standardizing scam definitions can improve detection and prevention efforts.
Key Themes and Insights
The Scam Classifier Model
Staci Shatsoff, Assistant Vice President at the Federal Reserve Bank of Boston, outlined the origins of the Scam Classifier Model, building on the 2020 Fraud Classifier. Designed to bring consistency to scam definitions, the model serves as a foundation for better reporting, data analysis, and prevention. Shatsoff highlighted the industry's collaborative development process, emphasizing the balance between detailed classification and adaptability for evolving scam tactics.
Challenges in Adoption
Erin Vertin from Ally and Travis Moseley from Capital One shared real-world hurdles in adopting the Scam Classifier Model, including legacy systems, tech debt, and prioritization issues. Both emphasized the importance of starting small, using existing taxonomies, and leveraging preliminary data to drive organizational buy-in.
Industry Applications
Panelists discussed how the Scam Classifier Model enables financial institutions to standardize definitions, improve internal communication, and enhance customer interactions. From fraud detection models to better categorization of scam types, the model helps institutions tailor their responses to specific threats.
Consumer Impact and Regulation
The discussion underscored the broader role of scam prevention in maintaining trust in financial systems. Panelists noted the operational and reputational risks associated with scams, as well as the need for proactive industry measures to prevent heavy-handed government regulations.
Looking Ahead
The panel concluded with a call to action for the financial industry to adopt the Scam Classifier Model and align on definitions to improve scam detection and prevention. By sharing insights and data across institutions, the industry can enhance its ability to combat scams and protect consumers.
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