BerquistSherman Adjustment

import chainladder as cl

This example demonstrates the adjustment to case reserves using the Berquist-Sherman method. A key assumption, and highly sensitive one at that, is the selection of a trend factor representative of the trend in average open case reserves from year to year.

# Load data
triangle = cl.load_sample("berqsherm").loc["MedMal"]

# Specify Berquist-Sherman model
berq = cl.BerquistSherman(
    paid_amount="Paid",
    incurred_amount="Incurred",
    reported_count="Reported",
    closed_count="Closed",
    trend=0.15,
)

# Adjust our triangle data
berq_triangle = berq.fit_transform(triangle)
berq_cdf = cl.Development().fit(berq_triangle["Incurred"]).cdf_
orig_cdf = cl.Development().fit(triangle["Incurred"]).cdf_

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import matplotlib.pyplot as plt

plt.style.use("ggplot")
%config InlineBackend.figure_format = "retina"

# Plot the results
ax = (berq_cdf / orig_cdf).T.plot(
    kind="bar",
    grid=True,
    legend=False,
    title="Berquist Sherman CDF to Unadjusted CDF",
    xlabel="Age to Ultimate",
    ylabel="Case Incurred CDF Adjustment",
);