BA1, Room 213: BA1, Room 213

Our [colloquium](https://sciences.ucf.edu/math/colloquium/) series offers a diverse platform for research scholars, faculty, students, and industry experts to share and exchange ideas, fostering discussion and networking across mathematics, statistics, and data science.

Professor [Liang Hong](https://profiles.utdallas.edu/liang.hong) from the University of Texas at Dallas will speak at this week's colloquium on "Conformal Prediction of Future Insurance Claims in the Regression Problem."

Abstract: In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification, (ii) selection effect and (iii) lack of finite-sample validity. This article addresses these three issues simultaneously by employing conformal prediction, a general machine learning strategy for valid predictions. The proposed method is both model-free and tuning-parameter-free. It also guarantees finite-sample validity at a pre-assigned coverage probability level. The proposed method can be applied by the insurer to set the risk capital level, especially regarding meeting the solvency capital requirement of European insurance regulation, Solvency II.

Speaker Bio: Dr. Liang Hong is a professor in the Department of Mathematical Sciences at the University of Texas at Dallas. He received a doctoral degree in mathematics from Purdue University. He is also a fellow of the Society of Actuaries. His current research interests include actuarial science, explainable machine learning, foundations of artificial intelligence and operations research.