Machine learning in the field of business model platformisation

Abstract

The strong trend towards platformisation in many areas of the economy and public life allows us to consider it a fundamental concept known and understood for industrial or somewhat digital revolutions. Platform ecosystems are replacing traditional models, first in the IT field and then beyond, mainly due to the digitisation of products, services and business processes. In turn, this requires a fundamental change in strategic thinking. The platform owner must consciously manage its evolution within a specific model, allowing maximum convenience, speed of operations, and diversity of counterparties. We examined the processes of implementing machine learning in various financial modelling and decision-making systems. This paper introduces the industry context for machine learning in finance, discusses essential developments that have shaped the need for machine learning in the finance industry, and discusses unique barriers to adoption. The financial industry has embraced machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice.

Author Biography

Irina A. Karachun, Belarusian State University, 4 Niezaliežnasci Avenue, Minsk 220030, Belarus

PhD (economics), docent; head of the department of digital economy, faculty of economics

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Published
2022-06-01
Keywords: machine learning, crowd technologies, platform, business model, financial technology
How to Cite
Karachun, I. A. (2022). Machine learning in the field of business model platformisation. Journal of the Belarusian State University. Economics, 1, 79-88. Retrieved from https://journals.bsu.by/index.php/economy/article/view/4581
Issue
No 1 (2022): Journal of the Belarusian State University. Economy
Section
O. Economic Development, Innovation, Technological Change, and Growth

Copyright (c) 2022 Journal of the Belarusian State University. Economics

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