Three Recent Applications of the Unsupervised PRIDIT Predictive Modeling Technique: Fraud, Medical Care Quality, and Ris

1. Three Recent Applications of the Unsupervised PRIDIT Predictive Modeling Technique: Fraud, Medical Care Quality, and Risk management Moderator: Richard A. Derrig, Opal Consulting LLC Speakers: Jing Ai, University of Hawaii Robert Liberthal, Jefferson School of Population Health James Guszcza, Delloitte Consulting CAS RPM Seminar March 21, 2011 New Orleans, LA

2. Supervised learning Most common situation A dependent variable Frequency Loss ratio Fraud/no fraud Some methods Regression CART Some neural networks MARS Logistic Regression Naïve Bayes Unsupervised learning No dependent variable Group like records together A group of claims with similar characteristics might be more likely to be fraudulent Ex: Territory assignment, Text Mining Some methods Association rules K-means clustering Kohonen neural networks PRIDIT Major Kinds of Predictive Modeling

3. REFERENCES Ai, J., Brockett, Patrick L., and Golden, Linda L. (2009) “Assessing Consumer Fraud Risk in Insurance Claims with Discrete and Continuous Data,” North American Actuarial Journal 13: 438-458. Brockett, Patrick L., Derrig, Richard A., Golden, Linda L., Levine, Albert and Alpert, Mark, (2002), Fraud Classification Using Principal Component Analysis of RIDITs, Journal of Risk and Insurance, 69:3, 341-373. Brockett, Patrick L., Xiaohua, Xia and Derrig, Richard A., (1998), Using Kohonen’ Self-Organizing Feature Map to Uncover Automobile Bodily Injury Claims Fraud, Journal of Risk and Insurance, 65:245-274 Bross, Irwin D.J., (1958), How To Use RIDIT Analysis, Biometrics, 4:18-38. Lieberthal, Robert D., (2010), Hospital Quality: A PRIDIT Approach, Health Services Research, Health Research and Educational Trust, PP 1-20