Cross-Domain Trust-Aware Artificial Intelligence for Behavioral Risk Prediction in Pediatric Healthcare, Financial Systems, and Public Decision Environments

Abstract

Artificial intelligence is increasingly deployed in domains where automated decisions directly affect vulnerable populations, including pediatric healthcare, financial security, and public welfare systems. While predictive accuracy has advanced substantially, the absence of trust calibration, explainability, and cross-domain risk governance continues to limit real-world adoption. This research proposes a cross-domain, trust-aware artificial intelligence framework that integrates behavioral analytics, Internet of Things–enabled data collection, explainable decision modeling, and ethical risk governance. The framework unifies insights from pediatric autism care, financial fraud detection, cybersecurity, and public-sector decision systems to demonstrate how behavioral intelligence can be leveraged responsibly across socio-technical environments. Drawing on reinforcement learning–based autism monitoring, cloud IoT architectures, human-centered AI principles, financial behavior analytics, and ethical AI governance models, the study develops a unified methodology for trustworthy decision automation. Simulated evaluations demonstrate reduced false alerts, improved human trust, and stronger alignment between automated outputs and stakeholder judgment. The findings highlight the necessity of trust-aware, explainable, and ethically governed AI architectures for sustainable deployment in high-impact domains.