Insurance Analytics

BAN427 Insurance Analytics

Spring 2019

Autumn 2019
  • Topics

    The topics can be indicated as follows:

    1. Introduction to Non Life Insurance. Adverse selection and moral hazard - and how to measure this empirically. Background literature: Einav and Finkelstein "Selection in Insurance Markets" and maybe Aarbu (2017) - "Asymmetric Information in Home Insurance".
    2. Big data in insurance/finance. How to establish a Customer TimeLine, distinguish between static characteristics and triggers/incidence, how to use incidence data in real life predictions. Interesting article: Zhang, Y., Bradlow, E. T., & Small, D. S. (2013). New Measures of Clumpiness for Incidence Data. Journal of Applied Statistics, 40 (11).
    3. Prediction methods with applications to insurance. Standard "ML tool set" that will be used (logit regression, regression trees, random forest, ensemble methods, etc).
    4. Prediction versus causation. Causal models, combining ML and causal methods. Papers from Varian (2014) and Athey (several papers) will be relevant.
    5. How to use randomized experiments in order to improve business processes.
    6. Reinforcement learning and how this can be applied. Using reinforcement methods can replace the need for randomized experiments and is of this reason very interesting to use.
    7. How to deploy and maintain many prediction models. Short intro to cloud-based real time datawarehouse platforms, deployment of analytical services etc..

  • Learning outcome

    After completing the course students:

    Knowledge

    • Know how big data and machine learning techniques is used in the insurance industry
    • Know how to build, deploy and test models and treatments using randomized experiments

    Skills

    • Can bring insurance problem into a statistical model
    • Can analyze and predict important insurance outcomes using machine learning techniques

    General Competence

    • Have general knowledge about measuring adverse selection and moral hazard from insurance data
    • Know how domain knowledge can be used to extract "causal" knowledge from observational data
    • Have knowledge about correlation vs causality - and how to empirically address causality using domain knowledge

  • Teaching

    7 lectures of 2 x 45 minutes. Anonymous data will be provided for applications of ML methods in the insurance business.

  • Recommended prerequisites

    Econometrics - for example ECN402, BUS444 or BAN431.

  • Requirements for course approval

    Mandatory participation in all lectures.

  • Assessment

    Assignment (group work, 2-3 students in each group).

  • Grading Scale

    Pass / Fail.

  • Computer tools

    Python combined Jupyter Notebook, R is optional.

  • Literature

    Some suggestions are made in the "Topics" section. A detailed list will be provided in Canvas.

Overview

ECTS Credits
2.5
Teaching language
English.
Semester

Autumn. Offered autumn 2019. Last week of the autumn semester 2019.

Course responsible

Adjunct Associate Professor Karl Ove Aarbu, Department of Business and Management Science.