Neural networks application in managing the energy efficiency of industrial enterprise

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

Transforming the energy sector to provide universal access to reliable and modern energy services is an essential task for Ukraine, one of the Eastern Partnership countries with heavy energy dependence. It will help accelerate the achievement of the Sustainable Development Goals. The paper is devoted to studying Ukraine’s readiness to generate sustainable energy compared to the EU and other Eastern Partnership countries and the development of an information base for monitoring the achievement of SDG7.
The data from the World Energy Council (WEC), the International Energy Agency (IEA), and the State Statistics Service of Ukraine are analyzed. Thus, the study proposed to expand the list of national monitoring indicators that more fully reflect the social, economic, and environmental results of SDG7 “Affordable and clean energy” in Ukraine. The development of an information monitoring base expands the opportunities to assess the availability, sustainability, and balance of national energy policy in green economic transformation. Furthermore, the indicators of energy intensity, carbon intensity, as well as access to sustainable energy for the population and business are emphasized. The findings are aimed to raise the level of awareness of government agencies and make balanced decisions to accelerate the achievement of SDG7 in Ukraine.

Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.