Place-bound memory and people-bound memory affecting destination loyalty in the food tourism industry

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.

The study aims to improve methodical approach for formalizing the sustainable development models for progressive countries by suggesting the relevant representative indicators. The study is performed using the statistical approach to determine the suitability of data for further modeling using indicators of variation, taking into account the normality of the population distribution as the main criteria of the data set quality. The study highlights the results of processing measurable quantitative economic, social, and environmental indicators of different countries that may be used for identifying possible changes in the world’s sustainable development. The authors select the indicators for scenario modeling of the sustainable development of Brazil, India, China, Republic of Korea, and the USA, as well as suggest a set of relevant affecting factors. To confirm the meaningful impact of different factors, such as biological balance, conflicts intensity, corruption perception and other, a neural network is developed, and its preliminary training on the test data is conducted. The obtained results can be used to predict economic changes in the world under the influence of specific economic, social, and environmental factors.

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.