AI-Augmented Time Theft Detection System
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P104Keywords:
AI, Time Theft, Behavioral Analytics, Payroll Fraud, Anomaly Detection, Remote Work, Task Verification, Micro-Behavioral Analytics, Timekeeping Systems, Machine Learning, Employee Monitoring, Compliance, Human-in-the-loopAbstract
With the rise in time theft & payroll fraud especially, the shift to remote & also hybrid work has presented fresh challenges in evaluating more employee productivity. With strong AI, the AI-Augmented Time Theft Detection System (TDS) offers a more complete solution meant to address such issues. This method uses behavioral analysis to find more evidence of time theft by tracking little symptoms as idle periods, regular clock-ins & device use habits. In actual time, the TDS detects abnormalities & also anomalous activity using ML techniques & also anomaly detection tools. It uses a human-in-the-loop component to enable continuous feedback to improve its detection models and raise accuracy, hence lowering false alerts. This approach ensures a fair and responsible workplace as well as increases operational effectiveness. Industries include healthcare, consulting, aviation, retail, and manufacturing where accurate timekeeping is critical would benefit much from this strategy. Delivering a complete solution to stop time theft, the AI-driven architecture guarantees reliability and accuracy in staff production monitoring
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