The assessment of the risk of a portfolio of assets and liabilities over a given time period requires a conditional valuation of the portfolio given the state of the world at a later time, a problem that is particularly challenging if the portfolio contains structured products or complex insurance contracts which do not admit closed form valuation formulas. A neural networks approach is developed to tackle this problem and numerical guarantees for its accuracy are derived. The method is illustrated on different examples from banking and insurance.