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Property & casualty insurance loss reserving in Python. Chain ladder, Bornhuetter-Ferguson, Cape Cod, bootstrap simulation, and loss development pattern estimation. Actuarial triangle operations.
Passed 17 of 22 test cases with the skill, and 8 of 22 without it.
and 11 more in the full report
9 fixed8 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
When loading sample datasets in Python's chainladder package, what string argument loads the Reinsurance Association of America auto liability dataset?
| Checked | Without skill | With skill |
|---|---|---|
| Passes string "RAA" to load_dataset | Missed | Passed |
The instructions your model reads when the skill loads.
ChainLadder implements actuarial reserve estimation methods for property & casualty insurance. Use it for loss reserving, claims triangles, and actuarial modeling in Python.
uv pip install chainladder
import chainladder as cl
# Load sample auto liability triangle
tri = cl.load_dataset("RAA")
print(tri)
# Select development pattern
dev = cl.Development().fit_transform(tri)
# Run chain ladder method
model = cl.ChainLadder().fit(dev)
print(model.reserve_)
print(model.ldf_) # age-to-age factors
# Estimate reserve variability
mack = cl.MackChainLadder().fit(dev)
print(mack.reserve_)
print(f"CV: {mack.reserve_.std() / mack.reserve_.sum():.2%}")
print(mack.conditional_standard_error_)
test cases passed, vs 8 / 22 without
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