Anomaly Detection in HR data using variational autoencoders: A deep learning approach to fraud detection and performance outliers

Fraud detection in Human Resource Management is a critical issue because payroll fraud and performance anomalies will lead to loss and inefficiency. Traditional fraud detection methods are unable to detect complex data patterns, and therefore a reliance is made on machine learning methods. In this research, a deep learning-based framework with the integration of Variational Autoencoders and Sparse Autoencoders for HRM data anomaly detection is introduced. The model is trained on a fraud detection data set, picking up normal patterns of payroll transactions and employee performance metrics. Anomalies are detected by the model as having high reconstruction errors, which would be indicative of fraudulent activity or performance outliers. For evaluating the proposed method, extensive experiments were conducted on widely available fraud detection data sets. The results indicate that the VAE-based model achieved accuracy of 98.4%, precision of 96.9%, recall of 97.2%, and F1-score of 97.0% compared to standard anomaly detection models. The model was also able to reveal embedded patterns in HR data, reducing false positives to a minimum, and enhancing fraud detection validity. The research establishes how deep learning can be utilized to detect fraud in HRM systems as a fast and independent process for HR professionals. The future will also see the implementation of hybrid models as well as real-time anomaly detection to further advance fraud prevention.