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The article provides the results of applying machine learning methods for prediction of unfavorable outcome of the public procurement procedure in the building and grounds maintenance domain. Based on a comprehensive analysis of the domain it was decided to investigate the following risks: the risk of collusion among suppliers; the risk of conspiracy between customers and suppliers; the risk associated with inaccurate data in the Unified Information System. Usage of various classification techniques has been researched while modeling the problem in the domain. In order to form sustainable groups of suppliers, the association rule mining was done using the "Apriori" algorithm. While searching for representative characteristics of the groups of similar objects, the solution to the clustering problem was found using the Ward and K-means++ methods. The Cluster models, which were defined to analyze each of the collusion risks, were built on the feature space. The models make it possible to identify the most typical behavioral patterns of two suppliers with each other as well as the customer with the supplier.

The growing popularity of payment cards has led to the emergence of new types of illegal transactions with money. In particular, the widespread use of non-cash payments has allowed fraud to reach the international level. Therefore, financial institutions are interested in the development and implementation of new effective fraud monitoring systems that will minimize the risk of approving illegal transactions. The article presents the results of applying machine learning methods to detect fraudulent transactions with bank cards. The use of various classification methods in modeling the specified problem is investigated. Generalized algorithm for detecting fraudulent transactions has been developed, which makes it possible to detect atypical international money transfers in real time. Generalized algorithm for detecting atypical international transfers will allow timely detection of potential fraud cases, thereby reducing the total volume of losses from illegal transactions and minimizing the reputation damage caused to the organization.