This article discusses one of the problems of using digital technologies, namely the complexity of assessing the effectiveness of their implementation. Since the use of digital twins at the enterprises of the fuel and energy complex (FEC) has recently become relevant, the authors have chosen the digital twins technology for consideration in this article. For the successful implementation of digital technologies, the authors propose a system of evaluation indicators that will measure the effectiveness of Digital Twins implementation and determine the benefits obtained. The advantages of digital twins include improved management and monitoring, optimization of production processes, prediction of equipment failures, as well as reduced maintenance costs and increased overall efficiency of FEC systems. As a methodological basis for the study, authors use the system of balanced indicators proposed by R. Kaplan and D. Norton, which served as the basis for the development of a set of performance indicators of the fuel and energy complex enterprise with the introduction of digital twins. As a result of the study, a list of indicators for monitoring the effectiveness of digital twins implementation was determined. The study identifies performance indicators for digital twin implementation, with future research aimed at quantitative assessments. The enterprise can implement a digital twin system with a WACC of 10.99%, payback period of 8.06 years, IRR exceeding the discount rate by 9.07%, a 3.5% reduction in harmful emissions, and a 2.5% efficiency increase.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

Telecommunications markets have a giant impact on countries’ economies. An example of this is the great potential offered by the internet service, which allows growth in various aspects such as productivity, education, health, and connectivity. A few companies dominate telecommunications markets, so there is a high market concentrations risk. In that sense, the state has to generate strong regulation in the sector. Models for measuring competition in telecommunications markets allow the state to monitor the concentration performance in these markets. The prediction of competition in the telecommunications market based on artificial intelligence techniques would allow the state to anticipate the necessary controls to regulate the market and avoid monopolies and oligopolies. This work’s added value and the main objective is to measure the current concentration level in the Colombian telecommunications market, this allows for competitive analysis in order to propose effective strategies and methodologies to improve competition in the future of Colombian telecommunications services operators. The main result obtained in the research is the existence of concentration in the Colombian telecommunications market.

Despite the efforts of public institutions and government spending, progress on the SDGs is mixed at the midpoint of the 2030 timeframe-some targets are off track and some have even regressed. ICT-related indicators, on the other hand, stand out for their strong progress. The author notes this progress, but questions its relationship to the implementation of the 2030 Agenda. He argues that the growth in internet and mobile network penetration is due to the economic characteristics of communications development. The objectives of the article are to review the impact of the ICT sector on economic growth, to consider the role of government spending in the development of this sector in the context of fostering the achievement of the Sustainable Development Goals, and to identify the prerequisites for significant progress towards SDG targets in communications. Achievement of these objectives will make it possible to determine whether this progress is a consequence of targeted efforts to achieve the SDGs, or whether, in accordance with the author’s hypothesis, it is based on the specifics of the ICT sector’s development, allowing for the accelerated spread of mobile communications and the Internet, which is reflected in the SDG indicators.

This research explores the intricate relationship between digitalization, economic development, and non-cash payments in the ASEAN-7 countries over a ten-year period from 2011 to 2020. Focusing on factors such as commercial bank branches, broad money, and inflation, the study employs panel data regression analysis to investigate their impact on automated teller machine (ATM) usage. The findings reveal that commercial bank branches significantly influence ATM usage, emphasizing the role of accessibility, services, and technological preferences. Broad money also shows a significant impact on ATM transactions, reflecting the interplay between fund availability and non-cash transactions. However, inflation does not exhibit a direct influence on ATM usage. The research underscores the importance of maintaining service quality and security in the banking sector to enhance digital financial inclusion. Future research opportunities include exploring diverse non-cash payment methods and extending studies to countries with significant global economic impacts. This research contributes valuable insights to policymakers aiming to enhance digital financial inclusion policies, ultimately fostering economic growth through the digital economy in the ASEAN-7 region.