Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA

Abstract

This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing supply chain operations and financial forecasting in the USA. The research examines how AI-driven predictive analytics can foster business growth and stabilize markets. A diverse set of ML models is employed to address various challenges: Long Short-Term Memory (LSTM) networks are used for sequence forecasting in financial and economic domains, while Logistic Regression, Random Forest, and Boosting techniques support fraud detection. Additionally, autoencoders and Isolation Forest algorithms are applied to identify unusual financial transactions, and ARIMA models forecast demand spikes and seasonality. For logistics optimization, Reinforcement Learning ( Deep Q-Networks) is used to improve route planning, and Neural Networks predict optimal restocking periods based on demand patterns. XGBoost is used to assess customer price sensitivity and optimize pricing strategies. The performance of forecasting models is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). In contrast, fraud detection effectiveness is measured through Precision, Recall, F1-score, and the Area Under the Curve (AUC-ROC). Logistics models are assessed by Total Delivery Time, Cost Reduction, and Efficiency Gains while restocking predictions are validated via accuracy, Mean Squared Error (MSE), and inventory turnover rates. Pricing strategies are evaluated based on Revenue Impact, Elasticity Metrics, and Customer Retention Rates.