Publications

InsurTech innovation using natural language processing

Published in North American Actuarial Journal, 2026

This paper explores the transformative potential of Natural Language Processing (NLP) in modernizing insurance analytics by extracting actionable insights from unstructured InsurTech data, with applications such as feature de-biasing, high-cardinality feature representation, and automated industry classification.

Recommended citation: Dong, P., Quan, Z. (2026). InsurTech innovation using natural language processing. North American Actuarial Journal, forthcoming. Read Paper | Download Bibtex

Automated Machine Learning (AutoML) in Insurance

Published in Insurance: Mathematics and Economics, 2024

This paper introduces an open-source Automated Machine Learning (AutoML) framework tailored for the insurance domain, effectively automating data preprocessing, hyperparameter optimization, and imbalance learning, to streamline actuarial data science.

Recommended citation: Dong, P., Quan, Z. (2025). Automated Machine Learning (AutoML) in Insurance. Insurance: Mathematics and Economics, 120, 17-41. Read Paper | Download Bibtex

Improving Business Insurance Loss Models by Leveraging InsurTech Innovation

Published in North American Actuarial Journal, 2024

This paper demonstrates that enriching traditional in-house insurance datasets with real-time, personalized InsurTech data improves the predictive accuracy of business insurance loss models.

Recommended citation: Quan, Z., Hu, C., Dong, P., Valdez, E. (2025). Improving Business Insurance Loss Models by Leveraging InsurTech Innovation. North American Actuarial Journal, 29(2), 247-274. Read Paper | Download Bibtex

Starting Off on the Wrong Foot: Pitfalls in Data Preparation

Published in arXiv (Under Review), 2026

To prevent flawed actuarial modeling caused by inappropriate data preprocessing, we developed an effective and efficient Informed Data Preparation Pipeline (IDPP) utilizing various statistical tools.

Recommended citation: Guo, J., Dong, P., Quan, Z. (2026). Starting Off on the Wrong Foot: Pitfalls in DataPreparation. Read Paper | Download Bibtex

Bridging the Divide While Walking a Tightrope: Evidence from the Insurance Industry on Federated Data Sharing

Published in ssrn (Under Review), 2026

This paper proposes a Hybrid Federated Learning (HyFL) framework tailored for the insurance industry that bridges data silos across insurers and InsurTech partners, achieving significant predictive accuracy gains and unlocking substantial financial value while preserving data privacy.

Recommended citation: Dong, P., Feng, F., Quan, Z., Wang, T. (2026). Bridging the Divide While Walking a Tightrope: Evidence from the Insurance Industry on Federated Data Sharing. Read Paper | Download Bibtex

Efficient and Interpretable Transformer for Counterfactual Fairness

Published in arXiv, 2026

In this paper, we introduce the Feature Correlation Transformer (FCorrTransformer) and Counterfactual Attention Regularization (CAR). This efficient, attention-light framework achieves counterfactual fairness and high interpretability for tabular datasets, effectively mitigating algorithmic bias with minimal predictive performance degradation.

Recommended citation: Dong, P., Quan, Z. (2026). Efficient and Interpretable Transformer for Counterfactual Fairness. Read Paper | Download Bibtex

Hybrid Tree-based Interpretable Pricing

In preparation

With: Zhiyu Quan, Montserrat Guillen, Lluís Bermúdez.

Rare Events Data and Zero Reduction Sampling

In preparation

With: Zhiyu Quan, Haiying Wang, Jing Wang, Jiaqi Liu.

Federated Learning for Insurance Companies

Published in Society of Actuaries Research Institute, 2024

Recommended citation: Dong, P., Feng, F., Quan, Z., Wang, T. (2024). Federated Learning for Insurance Companies. Society of Actuaries Research Institute. Read Paper | Download Bibtex

Privacy-Preserving Collaborative Information Sharing Through Federated Learning

Published in arXiv, 2024

Recommended citation: Dong, P., Quan, Z., Edwards, B., Wang, H., Feng, R., Wang, T., Foley, P., Shah, P. (2024). Privacy-Preserving Collaborative Information Sharing Through Federated Learning. Read Paper | Download Bibtex