6 papers
Efficient and Interpretable Transformer for Counterfactual Fairness
Panyi Dong, Zhiyu Quan
The growing reliance of machine learning models in high-stakes, highly regulated domains such as finance and insurance has created a growing tension between predictive performance,…
Starting Off on the Wrong Foot: Pitfalls in Data Preparation
Jiayi Guo, Panyi Dong, Zhiyu Quan
When working with real-world insurance data, practitioners often encounter challenges during the data preparation stage that can undermine the statistical validity and reliability…
InsurTech innovation using natural language processing
Panyi Dong, Zhiyu Quan
With the rapid rise of InsurTech, traditional insurance companies are increasingly exploring alternative data sources and advanced technologies to sustain their competitive edge. T…
Automated Machine Learning in Insurance
Panyi Dong, Zhiyu Quan
Machine Learning (ML) has gained popularity in actuarial research and insurance industrial applications. However, the performance of most ML tasks heavily depends on data preproces…
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
Panyi Dong, Zhiyu Quan, Brandon Edwards +5
The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insuranc…
Improving Business Insurance Loss Models by Leveraging InsurTech Innovation
Zhiyu Quan, Changyue Hu, Panyi Dong +1
Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and…