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Banking crises can be extremely costly. The early detection of vulnerabilities can help prevent or mitigate those costs. We have developed an early warning model for systemic banking crises that combines regression tree technology with a statistical algorithm (CRAGGING), with the objective of improving accuracy and overcoming the drawbacks of previously used models. Our model has a large set of desirable features. It provides endogenously determined critical thresholds for a set of useful indicators, presented in the intuitive form of a decision tree structure. Our framework accounts for the conditional relations between various indicators when setting early warning thresholds. This facilitates the production of accurate early warning signals as compared to the signals from a logit model and from a standard regression tree. Our model also suggests that high credit aggregates, both in terms of volume and long-term trends, as well as low market-risk perception, are among the most important indicators for predicting the buildup of vulnerabilities in the banking sector.