In the past 25 years, computer scientists and statisticians developed machine learning
algorithms capable of modeling highly nonlinear transformations and interactions of input …

For centuries, mathematicians and, later, statisticians, have found natural research and
employment opportunities in the realm of insurance. By definition, insurance offers financial …

We developed a methodology for the neural network boosting of logistic regression aimed at
learning an additional model structure from the data. In particular, we constructed two …

The aim of this paper is to operationalize claims reserving based on individual claims data.
We design a modeling architecture that is based on six different neural networks. Each …

Innon-life insurance, the payment history can be predictive of the timing of a settlement for
individual claims. Ignoring the association between the payment process and the settlement …

Stochastic forecasting and risk valuation are now front burners in a list of applied and
theoretical sciences. In this work, we propose an unconventional tool for stochastic …

We introduce an individual claims forecasting framework utilizing Bayesian mixture density
networks that can be used for claims analytics tasks such as case reserving and triaging …

Traditional non-life reserving models largely neglect the vast amount of information collected
over the lifetime of a claim. This information includes covariates describing the policy, claim …

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 …

Thanks to nonparametric estimators coming from machine learning, microlevel reserving
has become more and more popular for actuaries. Recent research focused on how to …