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Fraud Detection:
Beyond the Rules-Based Approach
Bruce Ratner, Ph.D.

Fraud on credit cards continues to grow and cuts profits by about $280 million each year. Healthcare fraud costs its industry an estimated $45 billion to $150 billion each year. These staggering figures imply that the credit card and healthcare sectors are in urgent need to adopt advanced analytic techniques in order to protect losses, and to avoid passing along these potentially avoidable costs to consumers. Currently, these sectors use the traditional rules-based (e.g., “if …then”) approach for fraud detection. However, this approach is not strong enough for the necessary data mining that would uncover undetected risk-predictive relationships. This knowledge can be used for predictive modeling with techniques, such as, statistical regression models, and artificial neural networks methods. The purpose of this article is to apply the data mining muscle, and the alongst predictive power of the new machine learning method – the GenIQ Model©  – for fraud detection. For an eye-opening preview of the 9-step modeling process of GenIQ, click here. For FAQs about GenIQ, click here.

For more information about this article, call Bruce Ratner at 516.791.3544,
1 800 DM STAT-1, or e-mail at