This article presents solutions to the person's fatigue recognition problem by the face's image analysis based on convolutional neural networks. In the present paper, existing algorithms were considered. A new model's architecture was proposed and implemented. Resultant metrics of the model were evaluated.

This work is related to the development of a high-tech popular science Internet portal "History of the Earth". The developed resource sets as its main goal the popularization of modern scientific geological knowledge using popular science multimedia content and software tools for interactive work with it. The Internet resource is intended for schoolchildren and students, as well as a wide range of Internet users.

The work is devoted to the description of the process of developing a universal toolkit for generating synthetic data for training various neural networks. The approach used has shown its success and effectiveness in solving various problems, in particular, training a neural network to recognize shopping behavior inside stores through surveillance cameras and training a neural network for recognizing spaces with augmented reality devices without using auxiliary infrared cameras. Generalizing conclusions allow planning the further development of technologies for generating three-dimensional synthetic data.

In the context of ever-increasing informatization, automation and digitalization of business, new schemes of unfair actions by both legal entities and individuals are emerging. In this regard, there is an acute problem of quick, effective and high-quality identification of information about a potential or current counterparty, which will allow you to quickly make the right management decisions.

The article describes one of the ways to solve this problem - the development of a system of information monitoring of counterparties, which will allow you to quickly identify and analyze information about their activities.

Currently, the major part of transmitted and stored data is unstructured, and the amount of unstructured data is growing rapidly each year, although it is hardly searchable, unqueryable, and its processing is not automated. At the same time, there is a growth of electronic document management systems. This paper proposes a solution for extracting data from paper documents considering their structure and layout based on document photos. By examining different approaches, including neural networks and plain algorithmic methods, we present their results and discuss them.

A method for assessing the risk of default of a corporate borrower at the monitoring stage based on a scoring assessment is proposed. This paper provides proof of the hypothesis that scoring methods for assessing credit risks can be used not only at the stage of initial assessment of a potential borrower when making a decision on lending, but also at the stage of its monitoring when accompanying a transaction. Monitoring is a periodic review of the credit quality of the corporate borrower with whom the loan agreement is concluded. This is done for the purpose of timely detection of negative signals, as well as timely response to threatening trends in the borrower's activities.

Some credit institutions save on monitoring by relying on the decision-making system, considering it flawless. However, this saving can be a fatal mistake, since many things change over time during the "life" of the enterprise. This is facilitated by both external factors (political, economic) and internal (incorrect development strategy of the organization, inability to assess its own credit capabilities, unscrupulous counterparties).

The proposed method is a system of automatic risk signals that have been tested for predictive ability, excluding manual procedures. The proposed solution includes markers (risk signals) that have a predictive ability above average, which can lead to a default of the corporate borrower. In addition, color marking is applied – red, yellow, green, which allows you to visualize the criticality of the identified risk signal depending on the predictive ability - a visual representation of the borrower's risks in order to facilitate interpretation.

The analysis of the developed method showed how much it is possible to speed up the monitoring process, which will allow for a prompt response to the identified risk signals, as well as to predict the likely deterioration of the borrower's credit quality in the loan or guarantee portfolio without compromising the quality of risk assessment.

A method for building end-to-end management accounting in a division of the Bank’s subdevision specializing in working with non-core assets is proposed. Has been proposed the process approach, an algorithm for building a database for the formation of key performance and control indicators.

Has been described the key stages of the department's work, the attribute composition of entities (set) arriving, enriched and transmitted at each stage of the department's work. By modeling the process has been built a role model, access and editing rights for employees. Data sources (reference books) for optimization and unification of the process of filling the database (tuple) are proposed. A method of accessing the database in the Power Query Microsoft Excel add-in is proposed, which allows you to collect data from files of all basic data types, process and refine the received data. In the interactive programming environment Jupyter Notebook, mathematical and financial models for data analysis (logistic regression, decision tree and discounted cash flow method) were built based on data in order to predict costs, the timing of asset exposure and make a decision on the optimal cost of putting property on the Bank's balance sheet and selling price. Based on ready-made libraries (matpotlib, seaborn, plotly), options for data visualization for management are proposed. Using the example of the Bank's division, the author describes the positive effects and opportunities that open up to the management of different levels in solving day-to-day tasks and planning the activities of the division. A technical task was proposed for the development of a showcase for the sale of non-core assets on the Bank's website as an environment for the accumulation of external data for making flexible management decisions.