Expected losses in insurance typically reflect which type of data?

Expected losses in insurance primarily rely on historical data of average risks. This type of data provides a comprehensive view of past incidents and claims, allowing insurers to analyze patterns, frequency, and severity of losses related to specific risks. By evaluating historical claims data, insurers can accurately predict future losses and set appropriate premiums to cover those expected losses.

The reliance on historical data helps in assessing the likelihood of various risks and understanding their financial implications. This empirical approach ensures that the calculations of expected losses are grounded in real-world experiences, leading to more informed underwriting decisions and better financial planning for insurance companies.

The other options may provide valuable insights but do not directly contribute to understanding expected losses in the same way. Projected assessments might include forecasts and estimations, current market analyses provide context but may not focus specifically on historical loss patterns, and consumer feedback could offer perspectives on customer satisfaction but lacks the quantitative data necessary for calculating expected losses.