Preview Questions Actuary | Role Specific Skill

Question 1: Which of the following does Solvency II require insurers to disclose in their financial statements?

Which action should you take?

Choose only one option

The exact solvency ratio required by law

The risks associated with the assets backing insurance liabilities

The internal controls for financial reporting

The expected future claims payments based on historical data

Question 2: In the context of financial risk management, what does "Value at Risk" (VaR) measure?

Which action should you take?

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The correlation between different asset classes

The maximum expected loss over a specified period at a given confidence level

The average return of an investment portfolio

The total variance of portfolio returns

Question 3: When interpreting stress testing results for a financial portfolio, what is typically assessed?

Which action should you take?

Choose only one option

The portfolio's resilience under extreme market conditions

The daily volatility of the portfolio

The average return of the portfolio under normal conditions

The total risk premium

Question 4: In actuarial modeling, which method is often used to estimate the economic capital required to cover potential risks?

Which action should you take?

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Risk-based capital models

Profitability models

Cash flow projection models

Market risk analysis models

Question 5: What is "actuarial fairness" in the context of insurance pricing?

Which action should you take?

Choose only one option

Ensuring that premiums are the same for all policyholders

Offering discounts for low-risk policyholders

Setting premiums so that the expected premium equals the expected payout, without loading for profit or expenses

Charging higher premiums for riskier individuals

Question 6: What is the most appropriate way to handle missing data in a financial forecasting model for an insurance company?

Which action should you take?

Choose only one option

Exclude the incomplete data points

Replace missing data with zero

Imputation methods (e.g., mean or regression)

Use random number generation to fill in gaps