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We propose a new perspective and a new method to detect accounting frauds out of sample. We show that a logistic regression that directly uses raw accounting data as regressors outperforms the traditional logistic regression that uses expert-identified financial ratios. Using the same raw data as inputs, ensemble learning, a state-of-the-art machine learning method, further outperforms the logistic regression model. The ensemble method also outperforms a support vector machine (SVM) with adoi:10.2139/ssrn.2670703 fatcat:blpfu5pcivezzlg3ouj7b6lsne