Evaluating the Impact of Feature Engineering on Auto Insurance Claim Prediction Models

DOI

10.2991/978-2-38476-585-0_17How to use a DOI?

Keywords

Insurance Claim Prediction; Feature Engineering; Machine Learning Models

Abstract

Predicting automobile insurance claim is essential for risk assessment, premium calculating, and fraud detection. All of which help ensure the profitability and stability of insurance companies. This study introduced a novel engineered feature, the Collision Index, designed to quantify localized automobile collision risk, which is aggregated into an insurance claim dataset to evaluate its impact on model performance. Four machine learning models, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine and Neural Network, were trained using the dataset with and without the engineered feature. Their F1 scores are compared using the paired Student’s t-test. Results indicate that only the Random Forest model witnessed a statistically significant improvement with the inclusion of the collision index at 0.05 significance level. The other models fail to observe significant performance gain, or imply a decisive conclusion due to computational constraints. Limitations of this paper include the imbalanced dataset, the estimated nature of collision index and possible incorrect assumption made during paired t-test.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wenyi Fang
PY  - 2026
DA  - 2026/06/18
TI  - Evaluating the Impact of Feature Engineering on Auto Insurance Claim Prediction Models
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 142
EP  - 151
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_17
DO  - 10.2991/978-2-38476-585-0_17
ID  - Fang2026
ER  -