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1 METODOLOGÍAS Y PRÁCTICAS EN RESERVAS TÉCNICAS PARA SEGUROS DE SALUD Y SEGUROS GENERALES LIMA - 31 DE MAYO, 2007 APESEG Presentado por: APESEG & Milliman,

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Presentation on theme: "1 METODOLOGÍAS Y PRÁCTICAS EN RESERVAS TÉCNICAS PARA SEGUROS DE SALUD Y SEGUROS GENERALES LIMA - 31 DE MAYO, 2007 APESEG Presentado por: APESEG & Milliman,"— Presentation transcript:

1 1 METODOLOGÍAS Y PRÁCTICAS EN RESERVAS TÉCNICAS PARA SEGUROS DE SALUD Y SEGUROS GENERALES LIMA - 31 DE MAYO, 2007 APESEG Presentado por: APESEG & Milliman, Inc. Advantages and Limitations ■

2 2 RESERVING METHODS IN PRACTICE Source: Claims Reserving Working Party Paper

3 3 EXPLORATORY DATA ANALYSIS Rate of Development Smoothness of Development Presence (absence) of Large Losses Volume of Data

4 4 EXPLORATORY DATA ANALYSIS Appropriate Projection Methodologies Anomalies in the data Questions to ask management concerning issues manifested in the data

5 5 ANALYST RESPONSIBILITIES Recognize normal levels of random fluctuation Recognize aberrations in loss development patterns Recognize trends in loss development patterns Question management concerning patterns Identify underlying causes of aberrations Make adjustments/corrections as necessary

6 6 SENSITIVITY TESTS Optimistic/Pessimistic Loss Development Factor Selections Optimistic/Pessimistic Tail Factor Selections Optimistic/Pessimistic Bornhuetter-Ferguson Expected Loss Estimates (Loss Ratios) Different Weighting among methods

7 7 WHEN THINGS GO WRONG Management reviews the prior year actuarial report and fires claims manager because case reserves develop upwards over time New claims manager instructs staff to reserve all claims at probable maximum amount This results in an average 10% increase in the average outstanding reserve along the last diagonal What will happen to the incurred loss development method?

8 8 COMPARISON BEFORE AND AFTER STRENGTHENING Case Reserve Strengthening had a leveraged impact on the reserve – Reserves overstated by 14%

9 9 WHEN THINGS GO WRONG Management pushes claims department to settle more claims In 2006, claims department settled an additional 10% of claims (at the average outstanding + IBNR reserve) What will happen to the paid loss development method?

10 10 COMPARISON BEFORE AND AFTER ACCELERATION Settlement acceleration had a leveraged impact on the reserve – Reserves overstated by 31%!

11 11 LOSS RATIO Implicit Assumption ─ Loss Ratio is known or can be reasonably estimated ─ Ultimate Loss is proportional to exposures (premium) Strengths ─ Simplicity ─ Basic data requirements ─ Can use Pricing Assumptions concerning Loss Ratio Weaknesses ─ Depends entirely on quality of Loss Ratio estimate

12 12 PAID LOSS DEVELOPMENT Implicit Assumption ─ Losses are paid at the same rate regardless of accident year Strengths ─ Simplicity ─ Basic data requirements ─ Does not depend upon case reserves ─ Provides objective test of incurred loss development Weaknesses ─ Sensitive to changes in rate of settlement ─ Results for immature year may be erratic

13 13 INCURRED LOSS DEVELOPMENT Implicit Assumption ─ Strength of case reserves is dependent only on the delay from accident date Strengths ─ Simplicity ─ Basic data requirements ─ Converges to ultimate faster than paid method ─ May be better than paid for immature years Weaknesses ─ Sensitive to changes in case reserve adequacy

14 14 BORNHUETTER-FERGUSON Implicit Assumption ─ Reserve is dependent upon the loss ratio, exposures, and development pattern Strengths ─ Combines loss ratio method with development method ─ Avoids problem with development method instability for immature years ─ Good to use when data are erratic Weaknesses ─ Dependent on the quality of the loss ratio ─ Requires data on exposures as well as triangle data

15 15 STRENGTHS / WEAKNESSES Methods are intuitive – once data is arranged in triangle patterns become obvious No sophisticated mathematics involved Spreadsheets can be used Data requirements are reasonable Relies on implicit assumptions – may not be true Failure of implicit assumptions may result in dramatically incorrect results Too many parameters – fit data well but result in large variance in predictions No information available on reliability of fit or error distribution Haven’t really modeled the underlying process Strengths Weaknesses


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