The influence of financial technologies on the global financial system stability

This study investigates the Millennials and Gen Z perception toward Fintech services, their usage intention, and their financial behavior. The study took place in the Palestinian context with a global comparison among these generations. The authors used the questionnaire-based technique to meet the study objective. West Bank respondents were selected for this purpose; the study instrument was distributed through different social media channels. The findings show that reliability/trust and ease of use are the main issues in using a financial service. Millennials are more aware (48%) of Fintech services than Gen Z (38%), which is different from the global view where Gen Z is the highest. The smartphone penetration rate is 100% among both generations, while the financial inclusion ratio in Palestine is around 36.4%; these clear indicators are the main Fintech drivers to promote Fintech services in Palestine, and these are global indicators for Fintech adoption intention. Both generations (84%) intend to use e-wallet services, Millennials (87%) and Gen Z is (70%) prefer using real-time services. Half of the respondents see that Fintech plays a complementary role with banks. The majority see that Fintech services are cheaper than bank services. Wealth management, and robot advisor services, and both generations are looking to acquire them in the long run. The authors revealed that 85% of respondents from both generations trust banks, so it is recommended that banks digitize their financial services to meet the customers’ needs, considering that 90% of respondents see that promotions are a key issue in adopting Fintech services. Promoting e-wallet services by banks is highly recommended due to the massive rivalry with Fintech parties.

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.

FinTech startups and services are one of the most dynamic segments of the modern economy. New financial technologies have already attracted many investors and form millions of budgets. Changing the traditional financial services concept, FinTech companies formed a new niche within the financial services market, the dynamic development of which determines the relevance of the development and implementation of an effective regulatory and oversight system.
The purpose of the article is to develop an economic and mathematical model for forecasting the development of the FinTech market on the example of Ukraine. In order to study the development of the FinTech industry, a multiple regression model was presented. The model describes the dependence of the total investment value of FinTech from venture investments in financial technology, venture investments in other technologies and venture investments in online lending. Based on this model, the effect of attracting investments with new FinTech projects on the total volume of investments in the industry was clarified. According to the model, with a change in investments in FinTech by 1%, the total rate of venture investments decreases by 0.03, funds in new projects of other companies grow by 0.05, and venture investments in online lending increase by 0.89. According to the analysis of regulatory legislation in the foreign countries of the FinTech services sphere, it was found that the regulation of most of the risks associated with the development of FinTech services falls within the competence of different supervisory authorities, requiring cross-sectoral cooperation between public institutions.