Spring 2027

Advanced Short-term Actuarial Modeling

Advanced short-term actuarial modeling covering parameter estimation for heavy-tailed insurance distributions and credibility assessment for ratemaking.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 7

Past student reviews · Paul Johnson

The course material was described as difficult. Lectures were perceived as rushed, with instructors jumping from proofs to problems without explaining the broader context.

What you’ll be able to do

  • Create new severity distributions through techniques including random variable transformation, mixtures, and splicing.

  • Apply extreme value distributions to the estimation of tail measures and probabilities in short-term insurance applications.

  • Perform model selection using graphical procedures, hypothesis tests, and score-based approaches with a focus on short-term insurance applications.

  • Estimate losses for short-term insurance coverages using credibility procedures including greatest accuracy credibility and empirical Bayesian estimation.

  • Apply advanced techniques for estimating outstanding claims associated with short-term insurance coverages, and understand the underlying statistical models and assumptions.

  • Apply advanced techniques to calculate premiums for short-term insurance coverages, such as calculating risk classification differential changes.

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

ACT SCI 652 or declared in undergraduate Business Exchange program

    • ACTSCI 652
    • declared in undergraduate Business Exchange program
    take one
ACTSCI 653

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of
    • ACTSCI 652
    • declared in undergraduate Business Exchange program

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

GREGORY WANNER is recorded teaching in Spring 2014, Spring 2015, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

PAUL JOHNSON is recorded teaching in Fall 2016, Fall 2017, Spring 2018, Fall 2019, Spring 2020, Fall 2020, Spring 2022, Spring 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

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Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records
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Student experience

the class

Past student reviews · Paul Johnson

Students found the material difficult but noted exams were more predictable than in 652. One review described Paul Johnson as an incredible professor who truly cares about students.

Recent recorded grades — Fall 2024: 3.50 GPA, 72.7% A/AB (n=11 letter grades); Spring 2025: 3.14 GPA, 45.5% A/AB (n=11 letter grades); Fall 2025: 3.30 GPA, 57.6% A/AB (n=33 letter grades).

difficulty & workload

Past student reviews · Paul Johnson

The course material was described as difficult. Lectures were perceived as rushed, with instructors jumping from proofs to problems without explaining the broader context.

Past student reviews · Greg Wanner

Greg Wanner was willing to help students who were stuck and cared about their success. Students recommended buying an Exam C/4 manual to practice alongside class topics.

Topics

  • Heavy-tailed and skewed probability distributions
  • Credibility assessment for ratemaking
  • Tail measures and probabilities in short-term insurance
  • Outstanding claims estimation

Skills

  • Creating severity distributions via transformation, mixtures, and splicing
  • Applying extreme value distributions for tail estimation
  • Estimating losses using credibility and empirical Bayesian methods
  • Estimating outstanding claims using advanced techniques

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1316 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records

No offering records for the selected term.

Raw records 
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "0d180a56fa87d0b320f53f099b1b6618072a19361f41a5eb0c1e6459fd5532c3",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}