9 citations · 14 across the 5 of their papers we have counts for
6 papers
Counting models with excessive zeros ensuring stochastic monotonicity
Hyemin Lee, Dohee Kim, Banghee So +1
Standard count models such as the Poisson and Negative Binomial models often fail to capture the large proportion of zero claims commonly observed in insurance data. To address suc…
Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics
Banghee So, Emiliano A. Valdez
In this paper, we explore advanced modifications to the Tweedie regression model in order to address its limitations in modeling aggregate claims for various types of insurance suc…
Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM
Banghee So
The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess…
The SAMME.C2 algorithm for severely imbalanced multi-class classification
Banghee So, Emiliano A. Valdez
Classification predictive modeling involves the accurate assignment of observations in a dataset to target classes or categories. There is an increasing growth of real-world classi…
Synthetic Dataset Generation of Driver Telematics
Banghee So, Jean-Philippe Boucher, Emiliano A. Valdez
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset gener…
Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics
Banghee So, Jean-Philippe Boucher, Emiliano A. Valdez
Powered with telematics technology, insurers can now capture a wide range of data, such as distance traveled, how drivers brake, accelerate or make turns, and travel frequency each…