Hadoop and Deductor Based Digital Ai System for Predicting Cost of Innovative Products in Conditions of Digitalization of Economy

The article represents theoretical foundations investigated for application of artificial intelligence systems in Big Data processing. The most comprehensive list of tools for data analysis and machine learning has been considered. A comparative Hadoop framework and Deductor analytical platform opportunity analysis has been performed. An AI-system has been proposed for predicting the cost of innovative products in the context of digitalization of the Russian economy. A hypothesis that a neural network makes it possible to obtain a forecast for the cost of innovative products in the Russian Federation has been put forward and proved. The neural network model included such parameters as GDP (billion rubles), key rate (%), RTS index, output of innovative products (billion rubles), costs of innovative products (billion rubles), dollar exchange rate (rubles), balanced profit (billion rubles), risk (σ), loans originated (billion rubles), VIX-Index and forecast for the volume of innovative products (billion rubles). The list of parameters presented reflects the development of both the economic sphere and Russia's financial sector quarterly for the period of from 2015 to 2018. Based on quantization and subsequent visualization of big data and using a multidimensional diagram, the artificial intelligence system developed allows revealing the GDP trend in Russia depending on the cost of innovative products and the VIX option stock-exchange quotation in the global economic landscape. The AI-system that enables prediction for the cost of innovative products using the "what-if" function in the Deductor platform has been developed.