Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

Data classification in presence of noise can lead to much worse results than expected for pure patterns. In paper was investigated problem of the research is the process of user recognition and identification in the video sequence. The main contributions presented in this paper are experimental examination of influence of different types of noise and to the increase the security of an IT company by developing a computer system for recognizing and identifying users in the video sequence. Based on the study of methods and algorithms for finding faces in images, the Viola-Jones method, wavelet transform and the method of principal components were chosen. These methods are among the best in terms of the ratio of recognition efficiency and work speed. However, the training of classifiers is very slow, but the face search results are very fast.