Information entropy of finance and mechanisms for overcoming it

Abstract

Aim. The presented study aims to determine the place of big data in modern finance and to analyze its role in the extreme saturation of the financial system.Tasks. The authors briefly overview the current state and prospects of big data use by financial institutions; provide a general description of the causal relationships of information flows and the transformation of the organizational structure of financial institutions in their transition to working with big data; outline the possibilities for assessing the information overload of financial institutions based on the concept of entropy, as well as the conditions and prospects for the transition towards information entropy management.Methods. As a methodological basis, this study uses general scientific research methods (analysis, synthesis, induction, deduction), including analysis of data management in financial institutions and information flows (collection, storage, processing, use, and reuse of data), as well as analysis of processes in the infosphere, elimination of noise problems, and risk accounting.Results. Major strategies and approaches for the transition to working with big data are presented. Methods for overcoming a number of problems hindering efficient management of the rapid growth of information in financial institutions are determined. A set of tools and procedures for analyzing information processes in financial markets and mechanisms for managing the restructuring of data management are proposed. An example of the transition to working with big data is given. The authors recommend applying the concept of entropy, which makes it possible to measure risk, uncertainty, and noise interference in financial markets and transactions and to assess the possibilities and scope of big data use by financial institutions.