  We propose a new deep learning approach for the quantification of name
concentration risk in loan portfolios. Our approach is tailored for small
portfolios and allows for both an actuarial as well as a mark-to-market
definition of loss. The training of our neural network relies on Monte Carlo
simulations with importance sampling which we explicitly formulate for the
CreditRisk${+}$ and the ratings-based CreditMetrics model. Numerical results
based on simulated as well as real data demonstrate the accuracy of our new
approach and its superior performance compared to existing analytical methods
for assessing name concentration risk in small and concentrated portfolios.
