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2 résultats pour « Pareto front »

Reinsurance with Neural Networks

19 sept. 2024 Autre, Reinsurance, Perturbed risk process, Deep learning, Multi-objective optimization, Binary classification, Pareto front, Optimal control, Ruin probability, Cramér-Lundberg model, Gerber-Shiu penalty function

The study explores an insurance company managing financial risk through reinsurance, aiming to optimize terminal wealth and minimize ruin probability. Using neural networks, it finds the optimal reinsurance strategy based on expected utility and a modified Gerber-Shiu function, illustrated by a numerical example involving a Cramér-Lundberg surplus model.

Reinsurance with neural networks

13 août 2024 Autre, Reinsurance, Perturbed risk process, Deep learning, Multi-objective optimization, Binary classification, Pareto front, Optimal control, Ruin probability, Gerber–Shiu penalty function, Cramér–Lundberg model

“We consider an insurance company which faces financial risk in the form of insurance claims and market-dependent surplus fluctuations. The company aims to simultaneously control its terminal wealth (e.g. at the end of an accounting period) and the ruin probability in a finite time interval by purchasing reinsurance… We solve the problem of finding the optimal reinsurance strategy and the corresponding maximal target functional via neural networks.”

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