ADDRESSING IMBALANCED INSURANCE DATA THROUGH ZERO-INFLATED POISSON REGRESSION WITH BOOSTING

A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data. We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.

Automated summary

The study introduces a machine learning approach to zero-inflated Poisson (ZIP) regression, which enhances predictive accuracy through adaptive weight adjustment. The cyclic coordinate descent optimization is used to address imbalanced financial data, demonstrating significant improvement in performance through real-life data testing. The results justify the modeling techniques as compared to other popular alternatives.