Machine‐learning models can provide accurate predictions in insurance pricing, but can also increase disparities between protected groups. Existing fairness‐aware pricing approaches typically target one fairness notion at a time, making it difficult to compare trade‐offs between predictive accuracy, group fairness, i...
T. Boonen, Xin-Yue Fan, Zi-Xiao Quan· Journal of Risk and Insuranc...· 0 citations
Abstract The rise in online transactions has made credit card fraud a significant global concern, necessitating detection strategies that are both highly accurate and practically viable. While existing literature extensively explores machine learning techniques to address class imbalance, most studies optimize for trad...
Xin-Yue Fan, T. Boonen· Asia-Pacific Journal of Risk...· 0 citations
This paper studies optimal insurance design in a competitive market with one policyholder and multiple insurers. The policyholder’s risk preferences are modeled by a distortion risk measure, and the loss distribution depends on prevention effort, which reduces the loss amount; insurers price contracts using a common...
T. Boonen, Li-Wei Zheng· ASTIN Bulletin: The Journal...· 1 citation
We study peer-to-peer (P2P) insurance contracting between a risk-averse P2P reinsurer and multiple risk-averse peers in an asymmetric Nash-bargaining framework, where all agents seek to improve expected utility relative to their disagreement points. Consistent with the expected value premium principle, we impose a pric...
T. Boonen, W. Chong, Kenneth Tsz Hin Ng et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
