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Title: Imbalanced learning using actuarial modified loss function in tree-based models

Abstract:

Tree-based models have gained momentum in insurance claim loss modeling; however, the point mass at zero and the heavy tail of insurance loss distribution pose the challenge to apply conventional methods directly to claim loss modeling. With a simple illustrative dataset, we first demonstrate how the traditional tree-based algorithm鈥檚 splitting function fails to cope with a large proportion of data with zero responses. To address the imbalance issue presented in such loss modeling, this paper aims to modify the traditional splitting function of Classification and Regression Tree (CART). In particular, we propose two novel actuarial modified loss functions, namely, the weighted sum of squared error and the sum of squared Canberra error. These modified loss functions impose a significant penalty on grouping observations of non-zero response with those of zero response at the splitting procedure, and thus significantly enhance their separation. Finally, we examine and compare the predictive performance of such actuarial modified tree-based models to the traditional model on synthetic datasets that imitate insurance loss. The results show that such modification leads to substantially different tree structures and improved prediction performance.

Zhiyu (Frank) Quan is an Assistant Professor at the Department of Mathematics of the University of Illinois at Urbana-Champaign. He holds a Ph.D. in Actuarial Science from the University of Connecticut. Before joining Illinois, he worked for a cutting-edge Insurtech company as a R & D data scientist developing data-driven solutions for major insurance companies. He has a broad spectrum of research interests in data science applications in insurance such as tree-based models, natural language processing, deep learning, and applies his actuarial expertise to build predictive models for claim research, rate making, etc.

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Meeting ID: 834 3668 6293

Passcode: 12345

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