Spring 2027

Fundamentals of Short-term Actuarial Modeling

Fundamentals of short-term actuarial modeling covering the definition and selection of probability distributions for heavily tailed and skewed insurance data.

offering recorded3 credits
Recorded instructors · Spring 2027 Paul Johnson3.7/5

Summary

1 / 6

Past student reviews · Paul Johnson

The workload was described as stressful for students with jobs or outside obligations, requiring significant extra time. Success depended on studying lecture examples and completing homework problems.

What you’ll be able to do

  • Demonstrate the appropriateness of alternative probability models, for both frequency and severity, when applied to short-term insurance loss data.

  • Evaluate the effects of coverage modifications on severity models and adjust the models to accommodate the coverage change.

  • Explain the model building process, and estimation, for the analysis of short-term insurance loss data.

  • Build aggregate loss models that quantify the risks of an insurance system, and utilize these models in applications such as reinsurance.

  • Interpret the concepts of ratemaking and reserving, and prepare data for short-term insurance ratemaking and reserving analyses.

  • Apply short-term actuarial modeling tools in conjunction with other techniques to address challenges in insurance applications.

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

(GEN BUS 317,STAT/​MATH 310,STAT 333, 340, or concurrent enrollment), declared in undergraduate Business Exchange program, or Capstone Certificate in Actuarial Science

ACTSCI 652 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Paul Johnson

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.80/5 from 15 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 3.8/5 raw quality · 4.1/5 difficulty · 8 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Paul Johnson

Paul Johnson explained difficult material clearly using examples and proved concepts during lectures. Students found the tests and homework challenging but manageable through practice problems and his office hours, where he confirmed answers and remained available for questions.

Critics described the lectures as slide-heavy and unengaging, with some finding him condescending or unhelpful outside of class. One student advised that the course required significant time, recommending external coaching for those with jobs or family obligations.

Recent recorded grades — Spring 2025: 3.48 GPA, 71.9% A/AB (n=32 letter grades); Fall 2025: 3.41 GPA, 59.1% A/AB (n=22 letter grades); Spring 2026: 3.42 GPA, 71.4% A/AB (n=42 letter grades).

Historical instructors & teaching patterns

Past student reviews · Greg Wanner

Past students found Greg Wanner’s detailed lecture explanations helpful but sometimes boring, noting that the lecture notes were essentially straight from the book. Weekly quizzes were manageable, but exams were hard and not curved as much as expected. The instructor also took a long time to respond to emails.

GREGORY WANNER is recorded teaching in Fall 2013. Recorded history may be incomplete and does not establish a future schedule.

PAUL JOHNSON is recorded teaching in Fall 2016, Spring 2017, Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019, Spring 2020, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. 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

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 550

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

Meeting source records
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Student experience

the class

Past student reviews · Paul Johnson

Paul Johnson taught a naturally difficult course but made the material clear and was available to answer questions. Students found the class useful for their actuarial careers.

Recent recorded grades — Spring 2025: 3.48 GPA, 71.9% A/AB (n=32 letter grades); Fall 2025: 3.41 GPA, 59.1% A/AB (n=22 letter grades); Spring 2026: 3.42 GPA, 71.4% A/AB (n=42 letter grades).

difficulty & workload

Past student reviews · Paul Johnson

The workload was described as stressful for students with jobs or outside obligations, requiring significant extra time. Success depended on studying lecture examples and completing homework problems.

Past student reviews · Paul Johnson

Opinions on teaching style were mixed; some students appreciated the examples and explanations, while others found the slide-reading approach unhelpful or condescending during Q&A.

Topics

  • Probability distributions for heavily tailed and skewed insurance data
  • Frequency and severity models for short-term insurance loss data
  • Aggregate loss models and reinsurance applications

Skills

  • Selecting and evaluating probability models for frequency and severity, and building aggregate loss models for risk quantification.

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

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Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · 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

1280 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
ACTSCI 652 · Spring 2027

Fundamentals of Short-Term Actuarial Modeling

Recorded 2026-10-14
Raw records 
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  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:242:003681",
    "course_id": "ACTSCI 652",
    "course_uid": "course_9f3d31814669db24532fa2c2",
    "term_id": "1274",
    "source_course_id": "003681",
    "source_subject_id": "242",
    "title": "Fundamentals of Short-Term Actuarial Modeling",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

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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
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
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  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "3c48b2f3bed8baccc37acc30f91dfcfce95f7abe054db1c8687be7cd113a26d4",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
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