66 citations · 166 across the 13 of their papers we have counts for
15 papers
An engine to simulate insurance fraud network data
Bavo D. C. Campo, Katrien Antonio
Traditionally, the detection of fraudulent insurance claims relies on business rules and expert judgement which makes it a time-consuming and expensive process (Óskarsdóttir et al.…
Catastrophe Risk in a Stochastic Multi-Population Mortality Model
Jens Robben, Katrien Antonio
This paper presents an approach to incorporate mortality shocks into mortality projections produced by a stochastic multi-population mortality model. The proposed model combines a…
On clustering levels of a hierarchical categorical risk factor
Bavo D. C. Campo, Katrien Antonio
Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nomi…
Insurance pricing with hierarchically structured data: An illustration with a workers' compensation insurance portfolio
Bavo D. C. Campo, Katrien Antonio
Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learni…
Bridging the gap between pricing and reserving with an occurrence and development model for non-life insurance claims
Jonas Crevecoeur, Katrien Antonio, Stijn Desmedt +1
Due to the presence of reporting and settlement delay, claim data sets collected by non-life insurance companies are typically incomplete, facing right censored claim count and cla…
Assessing the impact of the COVID-19 shock on a stochastic multi-population mortality model
Jens Robben, Katrien Antonio, Sander Devriendt
We aim to assess the impact of a pandemic data point on the calibration of a stochastic multi-population mortality projection model and its resulting projections for future mortali…