12.4 Internal Systemic Risk

Definition 12.3 Internal systemic risk = Uncertainty arising from the liability valuation process/actuarial valuation models

  • From source anywhere along the chain of the valuation

Examples:

  • Data record/collection/organization (e.g. not collecting the right data; not using the right data)

  • Analysis, actuarial judgement

  • Reserve selection (e.g. management overrides actuary’s opinion)

Definition 12.4 3 components of internal systemic risk:

  1. Specification Error:

    From not perfectly modeling the insurance process because it’s too complicated or just don’t have the data

  2. Parameter Selection Error:

    Difficulty in measuring all predictors and the trend in these predictors are particularly difficult to measure

  3. Data Error:

    Lack of data, lack of knowledge of the underlying pricing, u/w, and claim management process, inadequate knowledge of portfolio

12.4.1 CoV for Internal Systemic Risk

Benchmarking technique:
Need to define a list of risk indicators, score them against best practice, map the scores to a CoV

  1. Score against best practice

  2. Calibrate Score to CoV

12.4.1.1 Score Against Best Practice

  1. For each valuation group, assign a score (1-5) for each risk indicator
Table 12.2: Specification Error Risk Indicators
Risk Indicators Best Practice
# of independent models used Each model should add value by considering a different dimension of claims experience
Range of results produced by models Low variation in model results
# and importance of subjective adjustments to factors Few subjective adjustments; adjustments regularly monitored and reviewed
Ability to detect trends in key claim cost indicators Models have performed well at detecting trends in the past
Table 12.3: Parameter Selection Error Risk Indicators
Risk Indicators Best Practice
Best predictors have been identified (but not necessarily used) Best predictors have been analyzed and identified (int or ext), and show a strong correlation with claims experience
Best predictors are stable over time, or change due to process changes Predictors stable over time, stabilize quickly and respond well to process changes
Value of predictors used Predictors are close to Best Predictors; lead (rather then lag) claims cost outcomes, modeled, rather than subjectively selected
Table 12.4: Data Error Risk Indicators
Risk Indicators Best Practice
Knowledge of past processes affecting predictors Actuary has good and credible knowledge of past processes and change to processes
Extent, timeliness, consistency and reliability of information from the business Regular, pro-active communication between the actuary and claims staff and the business
Data is subject to reconciliations and quality control Reconciliation against financials, and prior studies; difference are well understood
Frequency and severity of past misestimation due to revision of data No past instances of data revision
  1. Assign weight for each risk indicator (weight can vary by valuation group)

  2. Calculate weighted average the scores using the selected weights

We score the 3 components for each valuation group (OCL and PL) and then roll up the score and assign the average grade for each valuation group to a CoV

12.4.1.2 Calibrate Score to CoV

Significant amount of judgement supplement by quantitative analysis

CoV \(\in [5\%, 25\%]\)

Analysis of past model performance should aid in estimating the potential variability

  • Hindsight Analysis:

    Compare valuation of liabilities at prior point in time to the current view, to gain insight into how a better model can reduce volatility

  • Mechanical Hindsight:

    Mechanically do various ex post analysis, and see how prediction error can be reduced; e.g. do a detailed vs crude and see the difference

Additional comments:

  • Improvement from poor to fair is greater than from fair to good

  • Longer tail line will have higher CoV due to difficulty in estimating the parameters

  • Larger liability will have smaller CoV when all else being equal

  • OCL and PL might not necessarily be on the same scale

    • PL may have additional uncertainty as it’s for future business

    • For short tail lines ELR might be sufficient for PL but might not be the best practice for OCL

    • Can always just add a load on top if justified