Casualty Actuarial Society

2012 Ratemaking and Product Management Seminar

The How, Why, and When of PRIDIT: Examples from Hospital Quality and Fraud Detection

Tuesday, March 20, 2012: 4:00 p.m.
414-415 (Philadelphia Marriott Downtown)
PRIDIT, or Principal Component Analysis of RIDIT Scores, is a relatively new and versatile technique for producing a rank-ordered score for the intensity of a latent variable, by identifying a monotone relationship between this latent variable and a set of ordinal predictor variables. PRIDIT has previously been used to explore fraud, hospital quality, and risk management. Many actuaries involved or who wish to be involved in predictive modeling may wonder how PRIDIT works, why they should use it, and when it is an appropriate model to fit available data. This talk will introduce a general description of PRIDIT that was developed with input from practicing actuaries. It will also present two applications to hospital quality and fraud detection.

This actuarial introduction to the method will then be used to show how it is applied to the problem of determining hospital quality. The motivation for the determination of hospital quality comes from health actuaries--the Society of Actuaries Health Section provided grant funding for the work that the speaker will describe. The example will be generic, so that anyone who has personal experience with a hospital should be able to relate to the problem of determining overall hospital quality from many measures of hospital performance. The goal is that attendees will get an understanding of the method with hospital quality that will allow them to apply PRIDIT to their line of business.

A more detailed application will be presented to fraud detection. The attendees will be able to see how the PRIDIT method is used to analyze this realistic and important problem in the P&C insurance industry, with two data sets from the practice. Also will be discussed in this context is a fraud rate estimation method designed based on PRIDIT that can be used to monitor fraud activities within the company and assess the effectiveness of any detection efforts.


Panelists: Robert D. Lieberthal, Assistant Professor, Thomas Jefferson University
Jing Ai, Assistant Professor, Shidler College of Business, The University of Hawaii at Manoa
  • PRIDIT_RDL.pdf (596.1 kB)
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