Research on AI driven Cross border E-Commerce Dynamic Pricing Model and Exchange Rate Volatility Response Strategy

DOI: https://doi.org/10.62517/jbdc.202601302

Author(s)

Cao Wei

Affiliation(s)

School of Economics and Management, Suzhou Agricultural Vocational and Technical College, China

Abstract

The digitalization of global cross-border e-commerce trade continues to deepen, and multi currency settlement and high-frequency exchange rate fluctuations have become the core disruptive factors for cross-border sellers' pricing and profitability. Traditional fixed pricing and manual price adjustment models have shortcomings such as lag, subjectivity, and profitability imbalance. This article takes the nonlinear fluctuation characteristics of exchange rates as the starting point, and combines machine learning and deep learning techniques to construct an AI cross-border e-commerce dynamic pricing model that is suitable for multiple overseas markets. It analyzes the transmission mechanism of short-term exchange rate fluctuations and medium - to long-term trend fluctuations on pricing costs, market sales, and platform profits. Based on the current business situation of small and medium-sized cross-border sellers, a tiered response pricing strategy for exchange rate fluctuations is formulated, taking into account product market competitiveness and exchange rate stability. The research conclusion can provide theoretical reference and practical path for intelligent pricing and exchange rate risk control of cross-border e-commerce enterprises, helping them reduce exchange losses and improve overall pricing returns.

Keywords

Cross Border E-Commerce; AI Dynamic Pricing; Exchange Rate Fluctuations; LSTM Model; Risk Response

References

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