Preview Questions Actuary | Role Specific Skill

Question 1: What is the purpose of a policyholder's lifetime value in the context of insurance pricing models?

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To estimate the future premiums the insurer will collect

To estimate the cost of claims over the lifetime of the policy

To calculate the policyholder's risk profile

To adjust for operational costs over the lifetime of the policy

Question 2: In analyzing mortality rates using historical data, which distribution would best describe the frequency of life insurance claims in a population?

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Poisson distribution

Weibull distribution

Exponential distribution

Normal distribution

Question 3: In assessing the capital adequacy of an insurance company, which statistical method is best suited to model the potential financial losses under extreme conditions?

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Linear Regression

Monte Carlo Simulation

Conditional Value-at-Risk (CVaR)

Value-at-Risk (VaR)

Question 4: How do "Clustering Techniques" like K-means assist in segmentation for insurance risk modeling, and what are the challenges in choosing the right number of clusters?

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Clustering is used only for marketing purposes, not risk modeling

Clustering can be replaced by regression analysis to predict outcomes

Clustering techniques are irrelevant as segmentation should be done manually

Clustering techniques like K-means group data based on similarities, helping in risk segmentation, but challenges include selecting the optimal number of clusters

Question 5: In a large dataset of insurance policyholders, which method would be most appropriate for identifying subgroups with similar risk profiles?

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Principal Component Analysis (PCA)

K-means Clustering

Linear Discriminant Analysis (LDA)

Hierarchical Clustering

Question 6: What is the significance of "Bootstrapping" in resampling methods for actuarial data, and how does it help estimate the distribution of financial losses?

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Bootstrapping is irrelevant as it does not improve model predictions

Bootstrapping is unnecessary as regression models are sufficient

Bootstrapping only applies to small datasets and should not be used for large financial data

Bootstrapping generates multiple resampled datasets to estimate the variability of outcomes, providing a robust method for estimating financial losses in uncertain environments