Preview Questions Actuary | Role Specific Skill

Question 1: How do you assess "Model Risk" in actuarial models, and what strategies do you employ to mitigate errors in model assumptions or predictions?

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Model risk can be ignored as long as the model is validated once

Model risk is irrelevant as models always produce accurate results

Model risk arises from errors in model assumptions or prediction accuracy, and it is mitigated by performing sensitivity analysis, backtesting, and using model validation techniques

Model risk is only relevant in regulatory compliance and not in day-to-day operations

Question 2: What is the difference between Type I and Type II errors in hypothesis testing, in the context of actuarial models?

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Type I error is failing to reject a false null hypothesis, while Type II is rejecting a true null hypothesis

Type I error occurs when the model is overfitted, while Type II occurs when the model is underfitted

Type I is rejecting a true null hypothesis, while Type II is failing to reject a false null hypothesis

Type I error is related to multicollinearity, while Type II is related to autocorrelation

Question 3: How does multicollinearity impact regression analysis in actuarial modeling?

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It increases the accuracy of the predictions

It causes the dependent variable to become highly predictable

It reduces the explanatory power of the model

It can distort estimates of the coefficients and inflate standard errors

Question 4: What is the primary difference between a parametric and non-parametric statistical test in actuarial analysis?

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Parametric tests assume a specific distribution for the data, while non-parametric tests do not.

Non-parametric tests are used exclusively for categorical data.

Parametric tests do not require data to follow any distribution.

Non-parametric tests are used for large datasets only.

Question 5: When pricing health insurance, how do you typically adjust premiums to reflect the effects of adverse selection?

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Incorporate stochastic models to adjust for higher-than-expected claims

Use a group-based pricing model to spread the risk

Apply an age-based risk factor

Use experience rating for higher-risk individuals

Question 6: In life insurance modeling, what is the purpose of constructing a life table?

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To analyze the discount rate on future claims

To measure variance in claim frequency

To determine the premiums for policyholders

To calculate expected mortality rates at various ages