Since its launch in 2024, the ScamClassifier model (Off-site) has given financial institutions and service providers a standardized approach to classify scams. Drawing insights from early adopters and user work groups, organizations have identified a variety of potential uses and benefits from using the model, such as:
- Helping with targeted prevention and detection
- Streamlined response and resource allocation
- Improved internal education and communication
- Data analysis and trend identification
How does the ScamClassifier model work?
Classification begins with the definition of a scam: the use of deception or manipulation intended to achieve financial gain, to distinguish an actual or attempted scam from other types of fraud. Questions determine the results of the scam, method of deception and type of scam. The model further facilitates accurate scam classification by including definitions and examples of nine types of scams.
Using the ScamClassifier model to enhance scam reporting and analysis
The ScamClassifier model assists organizations in classifying types of scams more consistently, which enhances reporting and analytics. For example, if an organization can more easily identify a rise in merchandise scams, it can flag similar payment patterns, such as merchant names or account numbers, and issue timely alerts to affected customers. The model helps institutions benchmark their own performance, refine fraud strategies based on real-time trends, and share information with other organizations to identify potential improvements or controls.
Turning data into action
With more consistent scam classification, financial institutions can uncover patterns in real time, by scam type, region or method, and proactively warn customers. Using the model’s common terminology allows organizations to more effectively document the type of scam and if — and how — a payment occurred. Some organizations use the ScamClassifier model to route customer reports of scams to designated internal staff who can initiate claims, document scam details, take steps to protect their customers’ accounts and/or attempt payment recovery.
Improving training practices using the ScamClassifier model
The ScamClassifier model can be a valuable training tool. For example, financial institutions are using it to train new staff and customer service teams, helping them ask better questions, listen for red flags and prevent scams before payments are initiated. To support employee training on how to identify and handle scams, some organizations have updated their policies and procedures to include the ScamClassifier model.
How the ScamClassifier model assists in scam prevention
Scams and fraud are an ongoing challenge within the payments industry. The ScamClassifier model is a simple, intuitive tool that can be used by different types of organizations. The data it provides can help organizations categorize and quantify payments scams, identify their full impact, and take steps to mitigate them and better protect their customers.
Note: The ScamClassifier 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 loss or create legal definitions, regulatory or reporting requirements. While sharing and use of the ScamClassifier model throughout the industry is encouraged, any adoption of the ScamClassifier model is voluntary at the discretion of each individual entity. Absent written consent, the ScamClassifier model may not be used in a manner that suggests the Federal Reserve endorses a third-party product or service.
