cff-version: 1.2.0
message: "If you use this software, please cite it as below."
type: software
title: "insurance-gam"
abstract: >
  Interpretable GAM toolkit for insurance pricing. Three production-grade
  modelling approaches: Explainable Boosting Machine (EBM) with relativity table
  extraction, Actuarial Neural Additive Model (ANAM) with monotonicity
  constraints, and Pairwise Interaction Networks (PIN/GA2M) for interaction
  detection. All produce per-feature shape functions auditable by a pricing
  committee. Exposure-aware with Poisson, Tweedie, and Gamma losses.
authors:
  - name: "Burning Cost Contributors"
url: "https://github.com/burning-cost/insurance-gam"
repository-code: "https://github.com/burning-cost/insurance-gam"
license: MIT
version: "0.1.7"
date-released: "2026-03-21"
keywords:
  - insurance
  - GAM
  - generalised additive model
  - interpretable machine learning
  - actuarial
references:
  - type: article
    title: "An Interpretable Deep Learning Model for General Insurance Pricing"
    authors:
      - family-names: Laub
        given-names: Patrick J.
      - family-names: Pho
        given-names: Erika
      - family-names: Wong
        given-names: Steven
    year: 2025
    url: "https://arxiv.org/abs/2509.08467"
  - type: article
    title: "Tree-like Pairwise Interaction Networks"
    authors:
      - family-names: Richman
        given-names: Ronald
      - family-names: Scognamiglio
        given-names: Salvatore
      - family-names: Wüthrich
        given-names: Mario V.
    year: 2025
    url: "https://arxiv.org/abs/2508.15678"
  - type: article
    title: "Accurate intelligible models with pairwise interactions"
    authors:
      - family-names: Lou
        given-names: Yin
      - family-names: Caruana
        given-names: Rich
      - family-names: Gehrke
        given-names: Johannes
      - family-names: Hooker
        given-names: Giles
    booktitle: "Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"
    year: 2013
    doi: "10.1145/2487575.2487579"
