Life-cycle Cost Modeling: Predictive Analytics for Insurance Claims

1. We wrote the book on Life-cycle Cost Modeling Whitestone Cost References published for 17 years

2. Whitestone ResearchLife-cycle Predictive Models for Insurance Reducing Property Claims Exposure: A Demonstration for Water Claims AMIG Insurance November 2012 Confidential

3. Life-cycle cost modeling is a powerful new analytical tool that uses home characteristics and proprietary knowledge of predictable component failures to anticipate claims and mitigate risk.LCC helps carriers eliminate claims and increase net earnings. We demonstrate the magnitude of the savings for AMIG in this study of homeowners water claims from 1999-2010 for mobile home policies. The Opportunity for AMIG • Nearly $200M in water losses can be predicted and mitigated. • Dramatic improvement in loss/premium ratios and earnings potential. • Simple to implement predictive program with actionable mitigation details. • Highest risk policies in LCC score > 90 avoid or mitigate - save $24M. • Policies above average risk in LCC score >50 avoid or mitigate – save $90M. • Brick Bits provides detailed reports on loss causes for improved risk • selection, mitigation, pricing, underwriting review, inspections and claims support. Nothing like this exists today. The next standard for improving your P&L.

4. Alternative Earnings Mandate The Home Insurance Market is Down A challenging U.S. economy has reduced business opportunities for homeowner insurance writers nationwide. • Home sales in 2011 were 60% lower than before the mortgage market collapse in 2008. • Loss frequencies are rising, and average property claims now cost more than $10,000. • Even with no major storms, 2011 was the fifth worst year in history for home catastrophe claims due to large numbers of mini-cats.

5. Eliminating Claims Increases Earnings Industry authorities estimate predictable (non-weather) water losses are ~15% or $2.2B.

6. Life-cycle Cost Analysis: Anticipate repair and replacement in building components to budget and maintain residential and commercial buildings. LCC modeling is a strong predictor of failures in key components of buildings (and their associated costs) that can be used to eliminate the cause of many kinds of property claims. LCC is not ITV or partial loss adjustment but predictive costing for ongoing maintenance for buildings at the component level. “As many as 15% of residential and commercial non-catastrophe property claims can now be predicted.” Life-cycle Cost Analytics Can Eliminate Claims For the first time ever, insurance companies can eliminate billions of dollars of non-storm related property claims.

7. Whitestone is the Proven Leader in Life-cycle Modeling Unparalleled LCC Modeling experience • 20 years experience in component-based LCC modeling for residential and commercial real property assets. • Thousands of specialized models range from simple residential structures to complex facilities such as overseas embassies, nuclear weapon plants and USAF launch sites. • Detailed cost data estimates total life-cycle costs: acquisition, M&R, operations, recapitalization, disposal and more. • Also able to approximate deferred maintenance and target assessment inspections. Missile test and launch facility Vandenberg Air Force Base Life Sciences Laboratory Pacific Northwest National Laboratory Definition: a building model is a list of components (usually 50 to 200) are related to repairs & replacements and other building services. Single family residence, Santa Barbara CA

8. Life-cycle Predictive Model for Insurance Data input is already collected from home characteristics file Whitestone LCC Scoring Engine with BrickBits models High score triggers detailed risk review with Brickbits tool WST Score Return on Investment to AMIG • reduced loss costs • Important member service opportunity • More cost effective inspection process • Vastly improved book of business • Earnings from non-traditional sources Mitigate highest risk policies Gather additional home data or conduct inspection

9. AMIG provided 15 years of homeowners insurance experience to test Whitestone LCC model on its book of home insurance business. This project scores individual policies for the AMIG book of business, demonstrates score accuracy, presents summary risk column, and identifies mitigation targets. 4.3 million mobile manufactured housing policies and 2.2 million single family dwelling policies. Multiple perils could be analyzed, current focus is on non-weather related water claims. Individual high risk policies can be mitigated using the Whitestone BrickBits tool. BrickBits highlights the likely causes of loss and current costs to remediate problems before a loss occurs. AMIG Pilot Project LCC scoring for each policy means better risk selection and a new ability to establish the most competitive pricing commensurate with risk. Isolate loss drivers to with policyholders to prevent losses.

10. Demonstration Using AMIG Mobile Home Data • Component models • M&R requirements • Location variables 1998 – 2012 N = 10.9 m policy records Life Cycle Data • Define a score • Refine best-estimate model 90 day process Underwriting records Predictive model by risk type Data mining & model development Data Conditioning Claims records • Establish consistent time series data • Logic and completeness checks • Truncate incomplete years (1998, 2011, 2012) 1998 – 2012 N = 141k claims

11. Policy Overview: Count by State, 1998 – 2012

12. Policy Overview: Total Water Loss by State, 1998 – 2012

13. Policy Overview: Water Claims by Year Built, 1998 – 2012 Policies n = 141K

14. Policy Overview: Water Claims by Insured Value, 1998 – 2012 Policies n = 141K

15. Whitestone LCC Policy Scoring • Model provides a score per policy from 1 - 100. • The model is driven by home characteristics and components combined with failure rates for water-related components. Other variables include location-specific cost & weather data. • Scores are estimated using a variant of the general least squares model. Year Built: 1992 City: Riverside, CA No renovation Whitestone Score: 87.4

16. 1999 - 2010 Policies Whitestone LCC Scores: 1999-2010 Mobile Home Policies • LCC score identifies disproportion risk policies: • Highest risk scores >80, 5% policies account for 11% of claims • Lowest scores <20, 5% policies account for 2% • Loss/premium ratios suggest cost effective segmentation. Without LCC scores these policies might be treated as equivalent risk.

17. Loss/Premium Ratio by LCC Score Above average loss/premium

18. 2010 Policies Focus on 2010 2010 policies were not used in model calibration but were scored. Comparison of actual and forecast claims for “out of sample” policies provide a measure of accuracy.

19. Whitestone LCC Model is Highly Accurate Whitestone LCC scoring model predicted accuracy ranged from 84% to 98% by region for a 2010 sample of 600,000 policies.

20. Brickbits Profile for Detailed Risk Review and Mitigation Online tool with specific home attribute precision Year Built: 1992 City: Riverside, CA No renovation Whitestone Score: 87.4

21. Summary • Pilot demonstrated that LCC models accurately forecast claims experience for mobile home policies in 1999-2010. • LCC scoring provides actionable findings: Identified riskiest 5 percent of policies, avoid or mitigate and save $24 million. Identified policies with above average loss/premium ratio, avoid or mitigate and save $90 million. • Quickly screen potential policies and mitigate existing policies. • Brickbits tool provides detailed LCC threat review and detailed inspection guidance.

22. Next Steps

23. Contacts Bob Caramanno, Business Development rtcaramanno@comcast.net 215-614-2500 Peter Lufkin, President plufkin@whitestoneresearch.com 805-879-9924 Jon Miller, Project Manager jmiller@whitestoneresearch.com 805-879-9920