Hybrid AI Models for Real-Time Decision-Making in Dynamic Business Environments: A Comparative Study of XGBoost, LSTM, and Reinforcement Learning - Iboro Akpan Essien; Geraldine Chika Nwokocha; Eseoghene Daniel Erigha; Ehimah Obuse; Ayorinde Olayiwola Akindemowo (International Journal of Scientific Research in Humanities and Social Sciences, 2025)
This explores the application of hybrid artificial intelligence (AI) models for real-time decision-making in dynamic business environments, focusing on a comparative analysis of three prominent techniques: Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL). Rapid technological advancements and increasing market volatility have intensified the need for advanced decision-support systems that can adapt to evolving business conditions. Traditional models often fail to capture the complex, non-linear, and temporal dynamics of modern business operations, necessitating more sophisticated AI-driven solutions. The research highlights the distinct capabilities and limitations of each model. XGBoost, a powerful ensemble learning method, excels in structured data analysis and predictive accuracy, offering robust performance in tasks such as credit scoring, sales forecasting, and fraud detection. However, its static nature limits adaptability in rapidly changing environments. LSTM networks, a class of recurrent neural networks, are designed for sequence prediction and effectively capture temporal dependencies, making them well-suited for applications such as demand forecasting, financial market prediction, and supply chain optimization. Nevertheless, LSTM models require substantial data and computational resources, and they can suffer from overfitting if not carefully tuned. Reinforcement Learning offers a fundamentally different approach, enabling adaptive learning through interaction with the environment. RL models are effective for optimizing dynamic decision processes, such as inventory management, dynamic pricing, and autonomous financial trading. However, their complexity, training instability, and requirement for extensive exploration pose significant challenges for practical deployment. This concludes that no single model universally outperforms others across all applications. Instead, hybrid approaches that integrate XGBoost, LSTM, and RL can leverage the strengths of each technique to enhance predictive accuracy, adaptability, and real-time responsiveness. This advocates for further research into hybrid AI frameworks, emphasizing the need for explainability, scalability, and ethical considerations in business-critical decision-making systems.
Keywords: Hybrid AI models; Real-time; Decision-making; Dynamic business; Environments; Comparative study; XGBoost; LSTM; Reinforcement learning