Purpose – the research aims to find out the main factors that determine the change in the number of FinTech companies in Lithuania and predict the future development of this sector. Research object: FinTech business sector, described as the number of FinTech companies. Research methodology – scientific literature analysis and generalisation, comparative analysis, statistical data collection and systematisation, correlation-regression analysis, scenario method for forecasting. Findings – the main factors that may determine the growth of the number of FinTech companies in Lithuania were identified and several development scenarios of the segment forecasted. The first part analyses related scientific investigations and FinTech's impact on the financial system as a whole. In the empirical part, a correlation and regression analysis of the factors that may determine the change of a number of FinTech companies was performed and ten main factors identified. The possible evolution of the number of FinTech companies until 2024 under 5 scenarios has been predicted. Research limitations – the statistical information used in this research comes from different databases and different sources and may not always be free from certain discrepancies. Practical implications – the obtained results may be of interest for researchers who examine this issue and the Central Bank of Lithuania, performing surveillance of the financial market. The forecast under various scenarios of the economic situation enables the estimation of the impact the FinTech business may have and can be useful for the Ministry of Finance involved in strategic planning. Originality/Value – an attempt was made to fill in the scientific literature gaps and find out what factors influence the most FinTech sector development in Lithuania and how this sector might grow in the future.

The present paper embarks on an investigation of the main risks associated with agri-food supply chains. A total of 11 key risks, namely Natural disasters of a global or local scale; Workers’ strikes; Change in government regulations or safety standards; Supply chain disruptions due to social or political unrest; Short term raw materials or products (expiration issue); Seasonality; Food safety incidents; Lack of smooth interconnection with other chain participants and Market and pricing strategies, economic crises and seven root risks (Natural disasters of a global or local scale; Workers’ strikes; Change in government regulations or safety standards; Rapid deterioration of raw materials (expiration) due to seasonality; Food safety incidents; Fraud in the food sector; Market and pricing strategies, economic crises) are applicable to all four stages of the agri-food supply chains were identified. An expert survey together with the Best-Worst Multi Criteria Decision Making method was employed as the main research tools. The most important root risks for agri-food supply chains are natural disasters of a global or local scale; workers’ strikes; change in government regulations or safety standards; rapid deterioration of raw materials (expiration), seasonality; food safety incidents; fraud in the food sector; market and pricing strategies economic crises. The most appropriate risk mitigation measures for each of the root risks were derived and assessed.