Insurance Fraud Detection with Unsupervised Deep Learning

报告摘要：In this talk, I will present a deep learning methodology to gain pragmatic insights into the behavior of an insured person using unsupervised variable importance. Starting with a preliminary investigation of the limitations of the existing fraud detection models, we propose a variable importance methodology incorporated with two prominent unsupervised deep learning models, namely, the autoencoder and the variational autoencoder. Both qualitative and quantitative performance evaluations are conducted. Real data sets are used to illustrate the idea and methodology. I will mention some related recent work at the end of the talk.

报告人简介：西交利物浦大学金融与精算数学系教授，国际著名精算学者，英国精算师协会名誉会员，瑞士精算师协会通信会员，国际统计协会会员。长期从事保险精算、风险管理、数理金融和应用概率论方面的研究，现担任保险精算领域顶级期刊Insurance: Mathematics and Economics主编,以及多个重要国际期刊副主编，包括North American Actuarial Journal, Stochastics等，兼任Springer Actuarial series、Encyclopedia of Quantitative Finance等丛书编辑。多次应邀在国际学术大会上作大会报告，由World Scientific出版社出版2部学术专著，在保险精算、数理金融等领域发表了高水平学术论文200多篇。