Disclaimer

1. CAS Fair Value ProjectCasualty Actuaries in Europe Spring Meeting23 April 2004E. Daniel Thomas(1) 646-471-5746edward.d.thomas@us.pwc.com

2. Disclaimer • The opinions expressed should not be regarded as representing the views of the CAS or of PricewaterhouseCoopers LLP

3. Presentation Outline • CAS Project Objectives • Data Characteristics • Modeling Approaches • Preliminary Findings • Significant Issues

4. CAS Project Objectives • Objectives • Evaluate impact of fair value on U.S. insurance company loss and LAE reserves • Identify significant issues associated with the usefulness of fair values in insurance company financial statements • Issues outside of scope • Credit risk • Adequacy of booked reserves • Correlation adjustments across lines of business • Impact of fair value on other balance sheet items

5. Data Characteristics (1) • Loss Data • 1998 - 2002 Schedule P • 10 accident years of development • Net of reinsurance basis • U.S. Statutory basis (sufficiently close to U.S. GAAP) • Interest rates • 31 Dec 1998 - 2002 • U.S. Treasury Securities • Maturities range from 6 months to 30 years

6. Data Characteristics (2) • Three lines of business • Personal Auto Liability • Workers’ Compensation • Medical Malpractice Claims-Made • PwC selected ten companies for each line of business (2 large, 2 medium, 3 small, 3 multi-line)

7. Modeling Approaches • FV Reserves = (U.S. GAAP) – (Discount) + (Market Value Margin) • = (U.S. GAAP) x (1 – Discount Factor) x (1 + MVM) • Discount Factor Models • Duration • Matched to yield curve • MVM Models • Development model – standard deviation • Stochastic simulation – standard deviation • Stochastic simulation – percentile distribution • Return on Capital • Model Calibration

8. Findings – Discount Modeling • Discount Models • Well-defined approaches available • Generally no significant differences between duration and matching approaches • Interest rate fluctuations affect results • Shape of yield curve can affect results

9. Impact of Discount Factor Models Duration vs. Matched to Yield-Curve

10. Findings – MVM Modeling • MVM Models • Many approaches, no single approach universally preferred • For a given company and year-end, the MVM varied by model, sometimes significantly • For a given company and MVM model, the MVM varied over the time period studied, sometimes significantly • For a given company, the MVM by model did not always move in parallel over the time period (i.e. one MVM model did not always result in the highest MVM) adding further uncertainty and variation in the results • For smaller companies, the MVM tended to be larger (measured as a percentage of the loss reserves)

11. MVM by Company –Personal Auto Liability

12. MVM by Company for One Method – Personal Auto Liability

13. MVM by Method for One Company – Personal Auto Liability

14. MVM by Company Size – Personal Auto Liability

15. Findings – Balance Sheet Impact • Balance Sheet – Loss Reserves • Personal auto liability - FV reserves were generally greater than U.S. GAAP reserves • Workers’ compensation - FV reserves were generally less than or close to the U.S. GAAP reserves • Medical malpractice claims-made - Not credible findings • Impact of moving to fair value loss reserve tended to be greater for smaller companies (i.e. higher MVM charge) • Associated result that FV would have on assets and other liability values would be needed to assess impact on equity

16. Findings – Income Statement Impact • Income Statement – Incurred Losses • FV current accident year incurred losses generally greater than U.S. GAAP. This tends to defer recognition of profits. • FV calendar year incurred losses from prior accident years may not be benchmarked to zero (i.e. no impact on future U.S GAAP calendar year results if prior year loss reserves were perfectly estimated) • Under FV, a non-zero impact on future calendar year results can be generated without changing undiscounted loss reserve estimate (depending on the relative amounts of discount and MVM) • Leveraged impact of reserve changes on income statement

17. Significant Issues • Modeling Issues • MVM Estimation Issues • Financial Statement Presentation Issues

18. Modeling Issues • Payment patterns • Incomplete historical paid triangles • Data smoothing reversals, unusual data points • Measures of variation • Influenced by constraint to accept booked reserves as mean of distribution • Variation associated with the tail/prior accident year bucket • Variation for certain liabilities not amenable to statistical analysis (e.g. asbestos & environmental)

19. MVM Estimation Issues • Single MVM guideline based on an industry guideline unlikely to be appropriate • Actuarial and/or accounting literature may need to contain guidance on acceptable methods/calibration procedures for calculating MVM’s to gain industry practice consistency • Calibration of MVM models can be challenging and will significantly affect the results

20. Financial Statement Presentation Issues (1) • Any financial presentation separating current versus prior accident year will require allocation judgments across all accident years • Level of disclosure for prior accident year development can influence usefulness of information to financial statement reader

21. Financial Statement Presentation Issues (2) • Comprehensive, detailed disclosures could include impacts from: • Changes in undiscounted (mean) loss reserve best estimate in comparison to the recorded loss reserve • Natural unwinding of the discount amount • Natural unwinding of the MVM as payments are made • Changes in effective interest rate • Changes in payout pattern • Changes in the measure of MVM variation • Changes in the MVM model calibration