The paper considers the features of the growth of “green finance”. The nuances of the development of the green economy of foreign countries are studied. The analytical data of the green market and issued instruments are presented in the form of figures, data for a number of years. Creating a greener economy requires significant investment, whether it’s financing a new electric bus network, building a solar farm, or building power plants powered by renewable energy. In this regard, responsible investing is being developed around the world, which is based on an approach to investing that seeks to include environmental, social and management factors (ESG factors) in the investment decision- making process for better risk management and a sustainable and long- term return on investment.

This article discusses the issue of “green bonds”. “Green” bonds are relevant and attractive for investors today. The features of “green” bonds are presented and the development practice in Europe is studied. The figures show the number of climate-aligned and green bonds worldwide in 2018 by region, the distribution of green bond issuance in Europe in 2019 by type of regional issuers, and the volume of green bond issuance in selected European countries in the first half of 2020.

The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opportunities that allow for cheating and using the use of outside help. Online proctoring based on artificial intelligence technologies in distance education is an effective technological solution to prevent academic dishonesty. This article explores the development and implementation of an online control proctoring system using artificial intelligence technology for conducting online exams. The article discusses the proctoring systems used in Kazakhstan, compares the functional features of the selected proctoring systems, and describes the architecture of Proctor SU. A prototype of the Proctor SU proctoring system has been developed. As a pilot program, the authors used this system during an online university exam and examined the results of the test. According to the author’s examination, students have a positive attitude towards the use of Proctor SU online proctoring. The proposed proctor system includes features of face detection, face tracking, audio capture, and the active capture of system windows. Models CNN, R-CNN, and YOLOv3 were used in the development process. The YOLOv3 model processed images in real time at 45 frames per second, and CNN and R-CNN processed images in real time at 30 and 38 frames per second. The YOLOv3 model showed better results in terms of real-time face recognition. Therefore, the YOLOv3 model was implemented into the Proctor SU proctoring system.