Deep Neural Network Algorithms for Parabolic PIDEs and Applications in Insurance and Finance

In this paper we study deep neural network algorithms for solving inear and semilinear parabolic partial integro-differential equations with boundary conditions in high dimension. Our method can be considered as an extension of the deep splitting method for PDEs to equations with non-local terms. To show the viability of our approach, we discuss several case studies from insurance and finance.