# Classification problem in liability insurance using machine learning models: a comparative study

arXiv: https://arxiv.org/abs/2411.00354 · HTML: https://mlsanity.com/paper/2411.00354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Boosting insights in insurance tariff plans with tree-based machine learning methods](https://mlsanity.com/paper/1904.10890/) (2019)
- [Sparse Regression with Multi-type Regularized Feature Modeling](https://mlsanity.com/paper/1810.03136/) (2018)

## Abstract (arXiv)

Underwriting is one of the important stages in an insurance company. The insurance company uses different factors to classify the policyholders. In this study, we apply several machine learning models such as nearest neighbour and logistic regression to the Actuarial Challenge dataset used by Qazvini (2019) to classify liability insurance policies into two groups: 1 - policies with claims and 2 - policies without claims.