A finished FIN-431 Topic 2 frequency and severity profile example, measuring three fleet exposures by how often and how badly they strike, with near-equal expected losses calling for opposite treatment. Searches like "fin 431 topic 2 assignment example", "fin431 topic 2 sample" and "fin-431 topic 2 example" land here.
What a finished FIN-431 Topic 2 frequency and severity profile looks like
The finished profile tables five illustrative years of collision claims for the forty vans: 14, 18, 11, 16 and 16, a mean of 15 a year with a sample standard deviation near 2.6. Average severity is 2,400, so expected annual collision loss is 36,000, and the yearly total stays close enough to budget. Cargo theft runs about two claims a year at 6,000 each, 12,000 expected. Serious injury liability has never occurred in the firm's history, so the profile borrows the case's industry table: an illustrative 2 percent annual chance of a 2,000,000 judgment, or 40,000 expected. The two largest expected losses differ by only 4,000. One is a budget line; the other exceeds the firm's 1,500,000 of equity, and the profile places them in opposite cells of the matrix.
How a FIN-431 Topic 2 example is structured
The profile is organized exposure by exposure and then placed on one grid. It begins with the composite fleet and its five-year claims file, every number invented for the case. A method paragraph defines the three measures used throughout: frequency as claims per year, severity as the average cost per claim, and expected loss as their product. Collision comes first, with the yearly counts, their mean and standard deviation, and a line explaining why a steady count makes the loss predictable. Cargo theft follows in a shorter passage. The liability section explains why the firm's own history cannot measure a loss it has never had and where the borrowed frequency comes from. A matrix then sorts each exposure by frequency and severity. The closing paragraph compares the worst plausible loss in each exposure with the firm's equity, the test that separates budgetable losses from ruinous ones.
Counts, mean and spread computed
Collision claims of 14, 18, 11, 16 and 16 give a mean of 15 a year, and a sample standard deviation near 2.6 shows how little the count moves.
Expected loss as frequency times severity
Fifteen claims at an average of 2,400 give 36,000 a year, the product the profile computes for each exposure before comparing any of them.
A loss the history cannot see
No serious injury judgment appears in five years of files, so the profile takes frequency from the case's industry table and says openly that it did.
Near-equal averages, unequal consequences
Collision at 36,000 and liability at 40,000 look alike as averages, yet one recurs every year while the other would arrive once as 2,000,000.
Worst case measured against equity
Setting each exposure's largest plausible loss beside 1,500,000 of equity sorts the losses the firm can absorb from the single one it could not survive.
Where marks go in FIN-431 Topic 2
Expected loss reported alone, without frequency and severity shown separately, strips out the information treatment depends on, because an average of 40,000 can describe a steady drip or a single catastrophe. Papers that measure liability from the company's own clean record conclude the exposure is zero, when five years without a judgment says almost nothing about a rare event. A frequency figure given with no spread hides whether the count is steady enough to budget. Severity quoted as an average without the worst plausible outcome understates the exposure that decides survival. Placing exposures on the matrix by impression, instead of by the computed figures, turns the grid into decoration. The strongest profiles compare each worst case with the firm's capacity to absorb it, since that comparison is what the retention decision in the next topic uses.
Get a FIN-431 Topic 2 example written to your instructions
Send the FIN-431 Topic 2 instructions and your classroom rubric, with the loss history or case your section provides. We write a custom example to them, with frequency and severity measured separately, expected losses computed, borrowed data labeled where the history is thin and each worst case set against the firm's capacity, in 24 to 48 hours. The first one is free.
FIN-431 Topic 2 questions, answered
Why measure frequency and severity separately?
Because they call for different responses. A loss that arrives often and costs little is predictable enough to budget and is usually cheaper to carry than to insure. A loss that arrives rarely and costs a great deal cannot be budgeted, because the average never actually occurs; what occurs is either nothing or the full amount. Multiplying the two into one expected figure erases that difference.
How can a firm measure a loss it has never had?
By borrowing experience from a larger group. Industry loss data, insurer rating tables and published claim studies cover many similar firms, so a rare event shows up often enough to estimate. Here the case supplies an illustrative industry frequency, labeled as such. A firm's own clean record is weak evidence about rare losses, since a five-year window would miss most of them.
Can the profile tell my company what to insure?
No. The fleet, its claims and the industry figure are composites built for a FIN-431 exercise. Real measurement depends on actual loss runs, exposure data and the judgment of underwriters and risk professionals, and treatment decisions follow from facts the example does not have. It shows how the course wants exposures measured before any decision is made, and it is not insurance advice.