Recent Challenges in Actuarial Science

@article{Embrechts2021RecentCI,
  title={Recent Challenges in Actuarial Science},
  author={Paul Embrechts and Mario V. W{\"u}thrich},
  journal={Annual Review of Statistics and Its Application},
  year={2021}
}
For centuries, mathematicians and, later, statisticians, have found natural research and employment opportunities in the realm of insurance. By definition, insurance offers financial cover against unforeseen events that involve an important component of randomness, and consequently, probability theory and mathematical statistics enter insurance modeling in a fundamental way. In recent years, a data deluge, coupled with ever-advancing information technology and the birth of data science, has… 

Figures from this paper

SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator
In this paper, we first introduce a simulator of cases estimates of incurred losses, called SPLICE (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated

References

SHOWING 1-10 OF 137 REFERENCES
AI in actuarial science – a review of recent advances – part 1
  • R. Richman
  • Computer Science
    Annals of Actuarial Science
  • 2020
Abstract Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate but
AI in actuarial science – a review of recent advances – part 2
  • R. Richman
  • Computer Science
    Annals of Actuarial Science
  • 2020
TLDR
How actuarial science may adapt and evolve in the coming years to incorporate new techniques and methodologies based on a modern approach to designing, fitting and applying neural networks, generally referred to as “Deep Learning” is investigated.
The Value of Risk: Swiss Re and the History of Reinsurance
Reinsurance is an invisible service industry which enables insurance companies to insure more risks and to make better use of their resources. Until recently, reinsurers were only known to a small
Stochastic Finance: An Introduction in Discrete Time
This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in
Micro-level stochastic loss reserving for general insurance
The vast literature on stochastic loss reserving concentrates on data aggregated in run-off triangles. However, a triangle is a summary of an underlying data-set with the development of individual
VALUATION OF HYBRID FINANCIAL AND ACTUARIAL PRODUCTS IN LIFE INSURANCE BY A NOVEL THREE-STEP METHOD
Financial products are priced using risk-neutral expectations justified by hedging portfolios that (as accurate as possible) match the product’s payoff. In insurance, premium calculations are based
Sexless and beautiful data: from quantity to quality
Actuarial science has received an enormous influence from statistics since the early times. However, in the recent decades, the interplay between those two disciplines is somehow different compared
Prediction of outstanding liabilities in non-life insurance.
A fully time-continuous approach is taken to the problem of predicting the total liability of a non-life insurance company. Claims are assumed to be generated by a non-homogeneous marked Poisson
FAIR VALUATION OF INSURANCE LIABILITY CASH-FLOW STREAMS IN CONTINUOUS TIME: APPLICATIONS
Abstract Delong et al. (2018) presented a theory of fair (market-consistent and actuarial) valuation of insurance liability cash-flow streams in continuous time. In this paper, we investigate in
Managing Risk in Reinsurance: From City Fires to Global Warming
The business of reinsurance developed at the fringe of financial services and, for most of its existence, went largely unnoticed outside the expert community. More recently, both public and
...
1
2
3
4
5
...