Developing AI-Based Financial Forecasting and Cybersecurity Systems for the U.S. Digital Economy

Abstract

The digital transformation of the U.S. economy has intensified the need for robust financial forecasting and proactive cybersecurity solutions. This study integrates advanced machine learning techniques—Long Short-Term Memory (LSTM), Prophet, and Random Forest Regressor—for financial time-series forecasting across twelve leading U.S. companies. In parallel, a cybersecurity framework was developed using the CICIDS2017 dataset and trained with a Random Forest Classifier to detect cyber intrusions. The forecasting models demonstrated high accuracy, with Random Forest achieving the best performance across most companies, while LSTM showed strong consistency in sequential trend learning. The cybersecurity model achieved an accuracy of 99.89% and an F1-score of 99.72%, effectively identifying attack patterns. Finally, a conceptual “AI Risk Intelligence Decision System” is proposed, fusing financial risk indicators with cyber threat intelligence to support early decision-making in digital financial operations. The proposed framework enhances predictive accuracy and cyber resilience, demonstrating strong potential for integration into real-world financial and cyber risk monitoring systems.