Insurance Risk Factors

Study Notes

Frequency factors affect insurance rates and can be influenced by court interpretations or legislation, such as mandatory seat belt laws.
In the absence of sufficient internal claims data, external statistical data sources from both industry and independent entities are utilized.
Loading factors encompass various expenses including commissions, taxes, and management costs, calculated based on direct written premiums.
Profit in insurance pricing reflects the insurer's profit objectives, expected investment returns, and regulatory frameworks governing profits.
Contingency loading is designed to mitigate adverse deviations in risk assessment.

Two key risk elements in ratemaking:
- Parameter risk: associated with incorrect selection of model parameters, potentially leading to underpricing.
- Process risk: arising from the variability of future projections and contingencies.

Individual risk rating is applicable when risks are large enough to provide credible experience.
Reasons for individual rating include accurate pricing for large risks, promotion of risk control programs, and specific coverage modifications.
Two types of individual risk rating systems:
- Prospective Systems: Use past data to set future coverage costs.
- Retrospective Systems: Based on actual experience in the period, adjusting for volatility and encouraging risk control.

Important for rate-making, these projections can be based on pure losses or allocated expenses, with unallocated losses typically included in expense loading.
The loss development method is employed to predict unpaid and unreported losses, utilizing historical patterns for projections.
Claims data is organized by accident year to form a triangle of known values, facilitating analysis.

Claims development is represented as:
- Horizontal movement: indicates claims development over time.
- Vertical movement: reflects changes in exposure levels.
- Diagonal movement: illustrates evaluation data trends.
Link ratio: derived from age-to-age development factors used to calculate projected ultimate values.

Trends in severity can significantly impact ratemaking due to factors such as inflation, court awards, and medical costs.
Frequency of losses is also subject to trends; discrepancies in competitor data can lead to adverse selection for an insurer.

Rating plans evaluate risk characteristics using a matrix of frequency versus severity of losses.
For example, higher frequency may be observed in buildings with combustible materials or poorly maintained structures, while high-value properties are associated with greater severity of losses.

Ratemaking typically follows one of two approaches:
- Pure premium method: focused on calculating sufficiency of premiums to cover losses.
- Loss ratio method: compares losses to earned premiums to set rates.

Example: In a small town, the theft of insured cars is analyzed to derive the pure premium.
Formula: Pure premium = (Total losses / Total value) x 100.
If 5 out of 1000 cars valued at Rs. 5 lac each are stolen over 10 years, total losses amount to Rs. 25 lacs, resulting in a pure premium of 0.5% or Rs. 2500 per car.

The pure premium serves as the base, which is then loaded to account for additional business procurement costs and other expenditures required to sustain insurance operations.