Workforce Compliance and Time Fraud Detection reporting using PowerBI
Description Mode: Online Date & Time: 26 Sept 10 AM IST Managing time and attendance across a global workforce requires organizations to balance regulatory compliance, corporate policies, and operational efficiency. Traditional approaches often rely on manual reviews and fragmented processes, making it difficult to consistently identify compliance risks and potentially fraudulent activities in a timely manner. This session presents a real-world success story of a Machine Learning-powered Working Hours Alert System (WHAS) for modernizing workforce compliance monitoring and fraud detection. The solution analyzes time and attendance data to identify compliance risks, detect anomalous working-hour patterns that may indicate fraud, and recommend schedule adjustments aligned with labor laws and organizational policies. The session focuses on the data and machine learning aspects of building an intelligent compliance monitoring solution. Attendees will learn how workforce data can be analyzed to identify meaningful patterns, design effective alerts, support anomaly detection, and enable proactive intervention. The discussion will also cover implementation considerations, transparency, human oversight, and lessons learned from deploying an AI-driven compliance solution in a global enterprise environment. By connecting machine learning with enterprise workforce data, the presentation demonstrates how organizations can reduce manual effort, improve consistency in compliance monitoring, strengthen governance, and support more informed operational decisions. Attendees will gain practical perspectives they can apply when designing scalable data and analytics solutions for workforce compliance and operational integrity.
