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Anomaly detection is a pressing research problem in many subject areas, the solution of which enables timely management decision-making. This study proposes a method for identifying anomalies in economic indicators characterizing the internal and external environment of a manufacturing organization. This method can be applied in the algorithmic support of business decision support systems. The method is based on the use of an artificial neural network with an autoencoder architecture trained to replicate input data at the output. After training the autoencoder on normal data, the error in reconstructing the input at the output will be small. However, when fed anomalous data, the error will increase, which can serve as an anomaly indicator. The proposed method uses a convolutional autoencoder, so the input data is first converted into images (signatures), for which an original method for their formation is proposed. The method involves representing the historical behavior of each economic indicator as a heat matrix. Each heat matrix forms one channel, and their combination forms a signature, which is then fed to the autoencoder input for further analysis. The autoencoder utilizes depthwise separable convolutions, allowing for autonomous tuning of convolutional filters for individual signature channels. The novelty of the research results lies in the developed method for detecting anomalies in economic indicator arrays, which enables localization of collective and individual anomalies (outliers), as well as in the developed software used to test the method. Computational experiments demonstrated that the method achieves anomaly detection accuracy comparable to some modern models.