Transforming claims through predictive modelling

Transforming claims through predictive modelling

A non-homogeneous Poisson process predictive model for automobile warranty claims

Automobile warranties and thus lifetimes are characterized in the two-dimensional space of time and mileage. This paper presents a non-homogenous Poisson Process (NHPP) predictive model for automobile warranty claims consisting of two components: a population size function and a failure or warranty claim rate. The population size function tracks the population in the time domain and accounts fo...

Paid-Incurred Chain Claims Reserving Method

We present a novel stochastic model for claims reserving that allows to combine claims payments and incurred losses information. The main idea is to combine two claims reserving models (Hertig’s model [11] and Gogol’s model [8]) leading to a log-normal paid-incurred chain (PIC) model. Using a Bayesian point of view for the parameter modelling we derive in this Bayesian PIC model the full predic...

Spatial modelling of claim frequency and claim size in non-life insurance

In this paper models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dep...

Learning Feature Engineering for Classification

Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed features followed by feature selection. Such approaches incur high computational cos...