1. Predictive Modeling Project Stephen P. D’Arcy Professor of Finance University of Illinois at Urbana-Champaign ORMIR Presentation October 26, 2005

2. Motivation - To Advance the Science of Predictive Modeling by: Applying predictive modeling to a key aspect of insurance operations Sharing the results of this research fully so that other researchers can replicate the results and improve the process Educating practitioners about the value of predictive modeling Opening up the “black box” approach of data mining that has generally been applied

3. Predictive Modeling in Insurance Massive amounts of data available –Accuracy varies –Much of it is ignored in rating or claims handling Innovators –Use of credit scoring in rating –Predictive modeling applications Underwriting Claims handling Fraud investigation Studies treated as proprietary and not shared or published

4. Project Details Jointly funded by the National Center for Supercomputing Applications (NCSA) and ORMIR Data set: –Detail Claim Database created by the Automobile Insurers Bureau of Massachusetts Predictive modeling tool: –Data-to-Knowledge (D2K) program of NCSA Results: –Papers –Presentations

5. Steps in Predictive Modeling 1.Decide question to be investigated 2.Access data 3.Understand your data 4.Preliminary data mining analysis Decision trees Generalized linear regression 5.Evaluate results and investigate problems 6.Additional data mining analysis Trees and regression Neural networks Other techniques 7.Apply results to insurance operations 8.Evaluate impact of change

6. Detail Claim Database (DCD) Created by the Automobile Insurers Bureau (AIB) of Massachusetts; Primary objectives: –Supporting company claim negotiation and claim denial activities –Assisting the Board of Registration –Responding to the Division of Insurance and to the Legislature –Assisting the Insurance Fraud Bureau of Massachusetts in detecting possible fraud rings Accessible for all member companies of the AIB and selected researchers

7. DCD Observations and Variables 491,591 Claim Observations (1/1/94 and subsequent) 95 Variables from 5 Categories: –Policy Information –Claim Information Coverage Total amount paid Accident date Type of injury Report date Type of treatment –Outpatient Medical Provider Information (up to 2 providers) Provider type (MD, Chiropractor, Physical Therapist, Hospital, Other) Amount billed and PIP/MED amount paid –Attorney Information –Claim Handling Information Type of investigation, if any

8. Types of Investigations Independent Medical Examination (IME) –66,876 Requests (16.72%) –Average Savings $348.71 –Favorable Outcomes (60%) Medical Audit (MA) –44,099 Requests (11.02%) –Average Savings $367.08 –Favorable Outcomes (67%) Special Investigation (SI) –16,668 Requests (4.17%) –Average Savings $1805.39 –Favorable Outcomes (46%) Problem – Average Savings values are based on a formula and may not reflect actual savings

9. Steps to Avoid Problems with Recorded Savings Value 1.Use Favorable Outcome as dependent variable 2.Generate value for expected payment Stepwise linear regression (33 steps) Based on claims not investigated Apply to IME Requested claims Compare expected payment to actual payment Result of IMEMean (Expected – Actual) No change recommended-562 Favorable result18

10. Regression Results

11. Primary Medical Provider Types and Attorney Representation Frequency

12. Highest Attorney Representation by Individual Medical Provider

13. Injuries’ Seasonality Trend

14. Injury Type = SS Primary Medical Provider = CH Second Medical Provider PIP CoverageEmergency Medical Treatment Decision Tree Example N Y Y N

15. Decision Tree Approach for IMEs Nodes and Favorable Outcomes –Strain and sprain only (63%) –Only 1 medical provider (67%) –No emergency room treatment (70%) –PIP claim (72%) –Bill less than $2421 (73%) –Attorney representation (75%) –Accident month November (81%)

16. Ongoing Research New dependent variable for expected savings –Refine model of expected payment –Determine estimated savings from investigations –Generate decision tree based on estimated savings Combining variables –Medical provider type –Injury type –Accident quarter (rather than month) Examine medical provider/attorney connections Suggestions?