Abstract
The need for improving methods of financial investment analysis in order to reduce risks leads researchers to exploit the modern scientific advancements especially in IT domain. Financial analytics require the ability to model and forecast future value of investigated financial parameters like currency inflation. In this paper, we analyzed the monthly inflation rate of Kazakhstan currency, using historical data from 1995 to 2020 by applying wide spread statistical and machine learning methods. The results show that the proposed research approach generates a solid forecasting accuracy and can be proposed to be included into financial investment analysis methods that could reduce inflation risk.