We present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of backward stochastic differential equations (BSDEs) for the valuation adjustments and solves these by a neural network based BSDE solver.