A method for fraud detection when installing mobile applications was proposed. The developed method, in contrast to existing ones, uses all available data regardless of their types, dimensions, and discrepancies and converts such data into homogeneous coefficients based on the proposed scaling method. This approach allows one to improve accuracy of task solution and build an open to expansion knowledge base with characteristics of fraudsters and rules of detecting fraudulent users. A system of scales for converting heterogeneous data into homogeneous coefficients has been developed which has enabled construction of a mathematical model of the scaling process. The algorithm of scaling heterogeneous data sets based on the proposed scales and the mathematical model of the process of scaling large arrays of heterogeneous data has been developed which has made it possible to reduce the whole data set to two homogeneous groups. The algorithms of processing the resulting groups of homogeneous data and detection of fraudulent users were offered. The developed algorithms using coefficients of similarity between user characteristics form fingerprints of fraudsters, determine characteristics and dependences of fraudsters which allows one to increase efficiency and speed of the process of fraudster detection. A scheme of the fraud detection process which was used in the intelligent system of automatic detection of fraudsters for carrying out of experimental studies was proposed. According to the results of experimental study, accuracy of fraudster detection was 99.14 % for a given representative sample. The results of experimental studies have shown effectiveness of automatic detection of fraudsters and the possibility of expanding formats and characteristics of fraudsters based on intelligent analysis and knowledge bases.