Preventing Hiring Fraud and Workforce Risk: A Real-Time Candidate Identity Verification Framework for U.S. Enterprises

Abstract

Purpose: The rapid expansion of remote and digital hiring has increased incidents of candidate impersonation, credential fraud, and identity substitution within enterprise recruitment systems. This study proposes a real-time candidate identity verification framework designed to strengthen hiring integrity in U.S. enterprises.

Methodology: This research adopts a design science methodology to develop a conceptual, event-driven identity continuity framework. The study synthesizes digital identity standards (NIST SP 800-63), AI risk management principles, and HR technology architectures to construct a scalable fraud detection model embedded across recruitment workflows.

Findings: The proposed framework demonstrates that continuous identity assurance, behavioral signal correlation, and dynamic risk scoring can proactively detect hiring fraud prior to onboarding. The model integrates privacy-by-design, explainability, and human oversight mechanisms, reducing false positives while maintaining compliance with employment and data protection regulations.

Unique contribution to theory, practice and policy: This study contributes a novel reference architecture that bridges HR technology, cybersecurity governance, and responsible AI in talent acquisition. It advances theory by conceptualizing identity continuity as a lifecycle control rather than a point-in-time verification step, offering practical and policy-relevant implications for secure digital hiring.