Uncertainty; variation around expected loss vs actual loss
Core of risk management decision making.
Planned losses, expected losses, expected severity are variations around
Things exposed to loss like cars, drivers, buildings, person's life are...
Basis for insurance pricing.
premium charged for insurance per exposure unit; must cover all costs of insurers; insurer does not know all the costs until the last claim is settled
Losses, expenses, administrative costs are covered by...
Pure Premium + loading + risk charge =
Amount of gross premium sufficient to pay for losses only (P*); must be an estimate so it can be wrong; if actual losses are equal to expected losses than they break even, it's AL<EL it's a profit and if AL>EL it's a loss
Reflects the estimation risk for an insurer; needed because P* is an estimate; it is the cushion for the risk incurred by insuring something
Influences of risk charge
Accuracy of P, if confidence in P is high from lots of past data, small RC, if not confident in P* from little past data, high RC
General Office administration expenses/fixed costs, state premium taxes, advertising commissions, represents 20-30% of the premium
Measures the likelihood of an event (chance/odds); ranges from 0-1; if 0, the event is impossible, if 1, the event is certain
They cannot occur at the same time - individual probabilities must sum up to one
Outcomes depends on some chance event, the results are random like rolling the dice, examples are frequency and severity
total number of losses in a given time period
Dollar value of losses that do occur
Graph or table which indicates for each outcome of a random event, the probability of that occurring; uniform distribution; bell curve
Measure of central tendency
Mean = expected value = P*, median, and mode
1(1/6)+ 2(1/6)+ 3(1/6)+ 4(1/6)+ 5(1/6)+ 6(1/6)=3.5 (1-6 being the possible outcomes)
Measure of dispersion
How to measure the measures of risk, measures frequency variance or standard deviation and coefficient of variation. Greater dispersion, greater the risk
Law of large numbers
Overtime the data you collect or more observations that occur; your results will get close to the accurate result
Total number of accidents divided by total number of drivers
Multiply # of accidents with particular loss amount by the loss amount and add up all the numbers and divide that by the total number of losses to get the total on average value of losses.
Can be deduced in advance, all outcomes are known, all outcomes are equally likely, all outcomes are mutually exclusive
Make estimates based on statistics; run an experiment and use results from collected data
Maximum possible loss
Only one answer, the highest possible amount of loss that could occur
Maximum probable loss
Largest loss that most likely would occur, not always an exact answer; key is how you present: 95% chance loss will be 1000 or less, true; 95% chance loss will be 1000, not true because the % that 1000 will occur is not 95%.