Purpose - This paper explores the integration of behavioral analytics and predictive modeling to enhance human decision intelligence in complex environments. It aims to examine how data-driven approaches can improve decision-making accuracy, reduce cognitive bias, and enable adaptive strategies across domains such as business, healthcare, and governance. The study further investigates how behavioral data—captured through digital interactions, sensor systems, and transactional logs—can be transformed into predictive insights that guide human decisions. Emphasis is placed on bridging the gap between algorithmic outputs and human interpretability. Methodology - The study adopts a mixed-methods approach, combining quantitative modeling techniques with conceptual analysis. Predictive models are constructed using supervised learning algorithms applied to simulated behavioral datasets, while qualitative synthesis is used to interpret decision-making frameworks. Additionally, comparative analysis is conducted across multiple modeling approaches to evaluate performance in terms of accuracy, interpretability, and adaptability. Secondary data sources and established experimental findings are integrated to support theoretical claims. Findings - The findings suggest that integrating behavioral analytics with predictive modeling significantly enhances decision quality by identifying patterns that are not easily detectable through human cognition alone. Models incorporating behavioral signals outperform traditional statistical approaches in forecasting outcomes. Moreover, human decision-makers benefit most when predictive outputs are accompanied by interpretable explanations. Hybrid systems—where human intuition and machine intelligence interact—demonstrate superior performance compared to fully automated or purely human-driven approaches. Practical Implications - Organizations can leverage behavioral analytics to optimize decision-making processes in areas such as customer engagement, risk management, and operational efficiency. Decision intelligence systems can be embedded into workflows to provide real-time recommendations. The results also imply that training programs should focus on improving data literacy among decision-makers. Ethical considerations, including transparency and bias mitigation, are essential for responsible deployment of predictive systems. Originality - This paper contributes to the emerging field of decision intelligence by synthesizing behavioral analytics and predictive modeling into a unified framework. It highlights the importance of human-centered design in data-driven systems. Unlike prior studies that treat predictive modeling and behavioral analysis separately, this work emphasizes their interdependence and proposes an integrated conceptual model for enhanced decision-making.
Dr. V. Antony Joe Raja (Thu,) studied this question.