  We introduce a new portfolio credit risk model based on Restricted Boltzmann
Machines (RBMs), which are stochastic neural networks capable of universal
approximation of loss distributions. We test the model on an empirical dataset
of default probabilities of 1'012 US companies and we show that it outperforms
commonly used parametric factor copula models -- such as the Gaussian or the t
factor copula models -- across several credit risk management tasks. In
particular, the model leads to better fits for the empirical loss distribution
and more accurate risk measure estimations. We introduce an importance sampling
procedure which allows risk measures to be estimated at high confidence levels
in a computationally efficient way and which is a substantial improvement over
the Monte Carlo techniques currently available for copula models. Furthermore,
the statistical factors extracted by the model admit an interpretation in terms
of the underlying portfolio sector structure and provide practitioners with
quantitative tools for the management of concentration risk. Finally, we show
how to use the model for stress testing by estimating stressed risk measures
(e.g. stressed VaR) under various macroeconomic stress test scenarios, such as
those specified by the FRB's Dodd-Frank Act stress test.
