Using Artificial Intelligence to Monitor Inflation

The article proposes a new digital technology of inflation targeting based on daily monitoring of inflation processes in the country. The purpose of the study is to identify potential opportunities for implementation of neural networks in collecting and processing large volumes of data by statistical services. It will lead to improvements in the efficiency and accuracy of statistics provided, which will be shown on the example of the CPI. Thisresearch is based on the analysis of the use of artificial intelligence technologies applied in foreign countries in the financial market. The novelty of the study lies in the development of a neural network structure that will allow targeting both demand-pull inflation and supply-pull inflation simultaneously. It is assumed that the core of this technology will be a modular controlled recurrent neural network, the architecture of which was developed to solve this specific problem. Taking as input and processing in hidden layers daily values of macro- and microeconomic variables that can affect inflation dynamics, the neural network will calculate as output the daily value of the CPI. If this value turns out to be higher than the target inflation level set by the Bank of Russia, then the neural network, during backpropagation of the error, will issue recommendations for adjusting some of the neural network weighting coefficients and the values of the macro- and microeconomic variables themselves to reduce the inflation level. The paper concludes that only a comprehensive approach to combating such a complex economic phenomenon as inflation, which consists of taking into account not only demand-pull inflation but also supply-pull inflation, will bring tangible results.

1. Shibanov O.K. Lessons for Central Banks: Inflation 2021–2023. Journal of the New Economic Association. 2024;1(62):240–245. (In Russ.) Available from: https://doi.org/10.31737/22212264_2024_1_240-245.

2. Buchanan B.G. Artificial Intelligence in Finance. Alan Turing Institute. Seattle University; 2019. 45 p. Available from: https://doi.org/10.5281/zenodo.2612537.

3. Ernst E., Merola R., Samaan D. The Economics of Artificial Intelligence: Implications for the Future of Work. IZA Journal of Labor Policy. 2019;9(1):1–35. Available from: https://doi.org/10.2478/izajolp-2019-0004.

4. Kaliraj P., Devi T. Artificial Intelligence Theory, Models and Applications. New York: Auerbach Publications; 2021. 506 p. Available from: https://doi.org/10.1201/9781003175865.

5. Cho K. et al. Learning Phrase Representations Using RNN Encoder–Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics; 2014. P. 1724–1734. Available from: https://doi.org/10.3115/v1/ D14-1179.

6. Bahoo S. et al. Artificial Intelligence in Finance: A Comprehensive Review Through Bibliometric and Content Analysis. SN Business and Economics. 2024;4(23):1–46. Available from: https://doi.org/10.1007/s43546-023-00618-x.

7. Nabipour M. et al. Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms via Continuous and Binary Data; a Comparative Analysis. IEEE Access. 2020;8:150199–150212. Available from: https://doi.org/10.1109/ACCESS.2020.3015966.

8. Max R., Kriebitz A., Von Websky C. Ethical Considerations About the Implications of Artificial Intelligence in Finance. In: L.San-Jose, J.L.Retolaza, L van Liedekerke (eds). Handbook on Ethics in Finance. International Handbooks in Business Ethics. Springer Cham; 2021. P. 577–592. Available from: https://doi.org/10.1007/978-3-030-29371-0_21.

9. Jain R., Vanzara R., Sarvakar K. The Rise of AI and ML in Financial Technology: An In-depth Study of Trends and Challenges. In: A. Kumar, S. Mozar (eds). 6th Int. Conf. on Communications and Cyber Physical Engineering. Lecture Notes in Electrical Engineering. Singapore: Springer; 2024;1096:329–341. Available from: https://doi.org/10.1007/978-981-99-7137-4_32.

10. Bankins S., Formosa P. The Ethical Implications of Artificial Intelligence (AI) for Meaningful Work. Journal of Business Ethics. 2023;185(4):725–740. Available from: https://doi.org/10.1007/s10551-023-05339-7.

11. Cho O.H. Analysis of the Impact of Artificial Intelligence Applications on the Development of Accounting Industry. Nanotechnology Perceptions. 2024;20(S.1):74–83. Available from: https://doi.org/10.62441/nano-ntp.vi.390.

12. De La Rosa W., Bechler C.J. Unveiling the Adverse Effects of Artificial Intelligence on Financial Decisions via the AI-IMPACT Model. Current Opinion in Psychology. 2024;58:101843. Available from: https://doi.org/10.1016/j. copsyc.2024.101843.

13. Bareith T., Tatay T., Vancsura L. Navigating Inflation Challenges: AI-Based Portfolio Management Insights. Risks. 2024;12(3):1–16. Available from: https://doi.org/10.3390/risks12030046.

14. Theoharidis A.F., Guillén D.A., Lopes H. Deep Learning Models for Inflation Forecasting. Applied Stochastic Models in Business and Industry. 2023;39(3):447– 470. Available from: https://doi.org/10.1002/asmb.2757.

15. Masini R.P., Medeiros M.C., Mendes E.F. Machine Learning Advances for Time Series Forecasting. Journal of Economic Surveys. 2023;37(1):76–111. Available from: https://doi.org/10.1111/joes.12429.

16. Kalinin A.M., Volin I.A. Data Sources for CPI: Big Data of the Internet and the Systems of the Federal Tax Service of Russia. Voprosy Statistiki. 2022;29(1):44–51. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2022-29-1-44-51.

17. Mishkin F.S. The Economic Theory of Money, Banking, and Financial Markets. 7th Edition. Pearson, Addison Wesley; 2004. 850 p. (Russ. ed. Mishkin F.S. Ekonomicheskaya teoriya deneg, bankovskogo dela i finansovyh rynkov, 7-e izdanie. Moscow: I.D. Williams LLC Publ.; 2006. 874 p.

18. Ivanchenko I.S. Research of the Russian Transmission Mechanism of Monetary Policy. Finance and Credit. 2006;(14):2–11. (In Russ.)

19. Rashid T. Make Your Own Neural Network. Create Space Independent Publishing Platform, 2016. 222 p. (Russ. ed: Rashid T. Sozdaem nejronnuyu set'. St. Petersburg: Alfa-Kniga LLC; 2017. 272 p. Available from: https://datafinder.ru/files/AI/Sozdaem-nei-774-ronnuyu-setRashid-Tarik.pdf.

20. Ivanchenko I.S., Nivorozhkina L.I. Assessment of the Impact of the Money Supply on Russian GDP. Finance: Theory and Practice.. 2025;29(1):34–44. (In Russ.) Available from: https://doi.org/10.26794/2587-5671-2025- 29-1-34-44.