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

Financial risk management avoids losses and maximizes profits, and hence is vital to most
businesses. As the task relies heavily on information-driven decision making, machine …

The evaluation of auto insurance risks is a fundamental task for financial institutions, crucial
for setting equitable premiums and managing risks effectively. Traditional machine learning …

We define the nagging predictor, which, instead of using bootstrapping to produce a series
of iid predictors, exploits the randomness of neural network calibrations to provide a more …

When pricing life annuity or insurance products issued to multiple lives, actuaries require a
model for the survival of coupled lifetimes. For reasons of simplicity these multiple life …

We study non-life insurance pricing and present a general procedure for constructing a
distribution-free locally unbiased predictor of the risk premium based on any initially …

At the core of insurance business lies classification between risky and non-risky insureds,
actuarial fairness meaning that risky insureds should contribute more and pay a higher …

On the issue of insurance discrimination, a grey area in regulation has resulted from the
growing use of big data analytics by insurance companies: direct discrimination is …

Insurance and economic data are often positive, and we need to take into account this
peculiarity in choosing a statistical model for their distribution. An example is the inverse …

This article presents several actuarial applications of categorical embedding in the context of
non-life insurance risk classification. In non-life insurance, many rating factors are naturally …