Toolkit Module 4: ScamClassifier^SM Model

Fight Scams with Better Reporting

The ScamClassifier model is a voluntary classification structure that supports consistent and detailed classification, reporting, analysis and identification of scams and related trends — a prerequisite to help promote accuracy of scam reporting, detection and mitigation.

Similar to the FraudClassifier^SM model, the ScamClassifier model does not depend on the communication method, payment application or payment type. The ScamClassifier model uses a series of questions to differentiate and classify scams by methods, categories and types. Classification begins with the scam definition, the use of deception or manipulation intended to achieve financial gain, to distinguish an actual or attempted scam from other types of fraud. Subsequent questions determine the results of the scam, method of deception and scam type. The model further facilitates accurate scam classification by including definitions and examples of the nine scam types shown in the graphic below.

Benefits of the ScamClassifier model

Potential benefits for organizations that adopt the ScamClassifier model for consistent classification of scams include:

• Enhanced detection and mitigation to address specific scam types
• Improved scam reporting to identify trends and quantify the impact of scams on the organization
• Expedited routing of scam cases to internal teams for case management and response

Model Integration: Using the ScamClassifier and FraudClassifier Models

The ScamClassifier model can be used as a standalone classification model or applied either before or after the FraudClassifier model. This video details several paths to navigate between the models when used together. Also see the downloadable resources below.

Downloadable Resources

Explore these resources for additional information on the ScamClassifier model.