The banking transaction monitoring system implements decision support
mechanisms for online payment control procedures for legal entities considering the dynamic
risk profile of the client. The system includes a set of algorithms for the intellectual analysis of
transaction parameters, including a text label for the purpose of payment, and decision support
for an employee of the financial monitoring unit. The development of algorithms for analyzing
textual labels for the purpose of payments allows us to clarify the dynamic payment profile of
the user and increase the validity of the recommendations of the monitoring system. A block
diagram of a system for identifying high-risk banking transactions based on data mining
algorithms has been developed. Algorithms for data mining of textual labels of the payment
purpose have been developed and the effectiveness of the proposed solution on field data has
been evaluated. An algorithm is proposed for the phased analysis of the text label of the
payment destination, including the stages of preprocessing, filtering, normalizing and
constructing a classifier based on a set of regular expressions and intelligent analysis
technologies. The difference between the algorithm is the use of adaptive category dictionaries
and the multi-pass application of heterogeneous classifiers, which makes it possible to increase
the validity of the decision on whether the transaction belongs to one of the selected classes
