Research on the Integration of Artificial Intelligence (AI) and Big Data in Corporate Financial Forecasting

Jing He

doi:10.6981/FEM.202507_6(7).0005

Authors

Jing He

DOI:

https://doi.org/10.6981/FEM.202507_6(7).0005

Keywords:

Artificial Intelligence (AI); Big Data; Financial Forecasting; Long Short-term Memory Networks (LSTM); Extreme Gradient Boosting (XGBoost).

Abstract

Traditional financial forecasting methods seem unable to cope with the challenges driven by artificial intelligence (AI). This study explores the integration of artificial intelligence and big data, builds a combined model of LSTM and XGBoost, optimizes data processing, feature engineering, and feedback, and improves the forecast accuracy and response speed of industries such as manufacturing, retail, and technology. This study focuses on key scenarios and influencing factors and provides suggestions for data foundations, talent, and management. The results show that fusion technology expands forecasting boundaries, shifts from passive accounting to active data-driven decision-making, and promotes enterprise intelligence and management upgrades.

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