Integration of big data technologies into the system of quantitative assessment of economic risks for e-commerce and e-trade enterprises under digital transformation

Анотація

The article examines theoretical, methodological, and applied aspects of integrating Big Data technologies into the system of quantitative assessment of economic risks for e-commerce and e-trade enterprises within the context of digital transformation of the global business environment. It is substantiated that the rapid growth of data volumes, transactional intensity, multichannel digital communication with consumers, and the expansion of online platforms are shaping a fundamentally new architecture of economic risks for both types of enterprises – e-commerce and e-trade. This architecture is characterized by high dynamism, nonlinearity, and forecasting complexity, which necessitates the use of Big Data analytics, machine learning algorithms, artificial intelligence tools, and predictive modeling for timely identification and mitigation of risk events. Particular attention is paid to the transformation of risk management approaches in e-commerce and e-trade enterprises, where traditional expert-based assessment methods are increasingly replaced by automated systems analyzing large volumes of structured and unstructured data. It is demonstrated that the use of Big Data enhances the accuracy of assessing financial, operational, behavioral, and reputational risks and enables the transition from a reactive to a proactive risk management model for both categories of enterprises. The purpose of the article is to develop a conceptual model for integrating Big Data technologies into the quantitative risk assessment system of e-commerce and e-trade enterprises, taking into account the specifics of the digital environment, high demand volatility, networked interactions, and the need for prompt managerial decision-making. The methodological foundation of the study includes systemic and process-oriented approaches, economic-mathematical modeling, big data analysis methods, regression analysis, clustering, correlation modeling, neural network algorithms, as well as elements of scenario and simulation forecasting. The use of an integrated approach allowed the formation of a multi-level risk assessment model combining analytical, predictive, and managerial components. The scientific novelty lies in the creation of an integrated risk analytics model that synchronizes internal operational and financial enterprise data, external market signals, macroeconomic indicators, and customer behavioral analytics into a single digital decision-support system for e-commerce and e-trade enterprises. The proposed approach allows the calculation of an enterprise’s integral digital risk index and enhances its resilience under conditions of economic digital transformation.

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