Expected Shortfall (ES) is the most important risk measure in finance and insurance. One of the most challenging tasks in risk modeling practice is to backtest ES forecasts provided by financial institutions, based only on daily realized portfolio losses without imposing specific models. Recently, the notion of e-values has gained attention as potential alternatives to p-values as measures of uncertainty, significance and evidence. We use e-values and e-processes to construct a model-free backtest of ES, which can be naturally generalized to many other risk measures and statistical quantities. This talk is based on on-going joint work with Qiuqi Wang (Waterloo) and Johanna Ziegel (Bern).