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Financial statement fraud is a global problem for investors, audit firms, regulators, and other stakeholders. Fraud detection can be regarded as a binary classification problem with a false negative being more expensive than a false positive. Although existing studies have made great efforts to detect fraud using various data‐mining techniques, the difference in misclassification costs is seldom considered. In this study, we propose a cost‐sensitive cascade forest (CSCF) for fraud detection, which places heavy penalty on false negative prediction and self‐adjusts the depth of a cascade forest according to the classifier’s recall (i.e. the classifier’s sensitivity). As missing values are ubiquitous in fraud research, we also explore the effect of selected missing data treatments on prediction performance, including complete case analysis, three selected classic statistical mechanisms (zero, mean, and modified mean imputation …