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International Journal of Modern Computer Science and IT Innovations

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Machine-Learning Architectures enabling Human Trait Verification Alternatives within Risk-Coverage Ecosystems: Resilient Identity Validation, Policy Adherence

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Dr. Rohan Verma 1 , Dr. Sneha Kulkarni 1 ,
4 Department of Computer Science and Artificial Intelligence Global Institute of Technology and Innovation (GITI) Pune, Maharashtra, India
4 Department of Cloud Computing and Smart Healthcare Systems Advanced Research University (ARU) Chennai, Tamil Nadu, India

Abstract

The increasing reliance on digital infrastructures in risk-coverage ecosystems such as insurance, healthcare financing, and financial protection services has necessitated robust identity verification mechanisms. Traditional authentication approaches, including password-based systems and static biometric identifiers, are increasingly vulnerable to adversarial manipulation, data breaches, and regulatory non-compliance. This study proposes a comprehensive analytical framework that integrates machine-learning-driven human trait verification architectures as resilient alternatives for identity validation within risk-coverage environments. The research synthesizes advancements in large language models, retrieval-augmented generation (RAG), secure access control models, and edge-cloud computing paradigms to establish a multi-layered verification ecosystem.

The proposed framework emphasizes adaptive identity validation using physiological, behavioral, and contextual trait inference mechanisms enhanced by machine learning. It incorporates zero-trust architectures, attribute-based access control (ABAC), and cryptographic protocols to ensure secure, policy-compliant operations. Furthermore, the study examines the implications of generative AI in identity modeling, particularly addressing hallucination risks, privacy vulnerabilities, and synthetic data utilization. The integration of cloud-edge-end intelligence enables scalable deployment while maintaining real-time verification capabilities.

Through a critical synthesis of existing literature and conceptual modeling, the study identifies key challenges, including model interpretability, regulatory compliance (e.g., GDPR and HIPAA), adversarial robustness, and ethical concerns in automated identity systems. The findings highlight that hybrid architectures combining machine learning, cryptographic assurance, and regulatory alignment significantly enhance system resilience. The research contributes to the development of next-generation identity verification systems that are secure, adaptive, and policy-compliant, thereby strengthening trust and operational integrity within risk-coverage ecosystems.

Keywords

Machine Learning, Identity Verification, Risk-Coverage Systems, Retrieval-Augmented Generation

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