  In this paper we consider the numerical approximation of nonlocal integro
differential parabolic equations via neural networks. These equations appear in
many recent applications, including finance, biology and others, and have been
recently studied in great generality starting from the work of Caffarelli and
Silvestre. Based in the work by Hure, Pham and Warin, we generalize their Euler
scheme and consistency result for Backward Forward Stochastic Differential
Equations to the nonlocal case. We rely on L\`evy processes and a new neural
network approximation of the nonlocal part to overcome the lack of a suitable
good approximation of the nonlocal part of the solution.
