Termination Anomaly Detection

Overview

Use Termination Anomaly Detection to monitor, analyze, and identify anomalies in employee termination patterns to assist the recruitment team in detecting irregular trends.

By using machine learning-based anomaly detection (Isolation Forest), unexpected spikes or drops in termination counts are flagged, enabling early intervention and proactive decision-making.

The model systematically tracks termination data based on location, position, and type (voluntary versus involuntary), providing insights such as termination count visualizations and anomaly breakdowns for in-depth analysis. Users can refine data using filtering options to conduct targeted investigations. This enhances workforce insights, allowing organizations to identify potential issues early and take necessary actions to optimize workforce stability.

Expected benefits

  • Early detection of irregularities: The system proactively identifies unusual termination patterns before they escalate into compliance or operational risks. It flags unexpected spikes or deviations in termination trends, allowing HR teams to investigate and take corrective actions in a timely manner.
  • Enhanced workforce planning and retention: By analyzing voluntary and involuntary termination trends, the system provides valuable insights into employee attrition patterns. This data-driven approach helps organizations develop targeted retention strategies to reduce turnover and improve workforce stability.
  • Reduction in legal and compliance risks: The system ensures that all termination events are aligned with company policies and regulatory requirements, mitigating legal and compliance risks. By providing a transparent audit trail, HR teams can demonstrate due diligence and adherence to labor laws.
  • Increased automation and operational efficiency: The system reduces the manual workload for HR personnel by automating the anomaly detection process. Automated alerts and reports allow HR teams to focus on strategic decision-making rather than manual data analysis.

Key features

  • Termination data monitoring: The system continuously tracks termination counts across different locations, positions, and termination types (voluntary versus involuntary). By identifying unusual patterns, it enables early intervention and informed decision-making.
  • ML-based anomaly detection: Leveraging the Isolation Forest algorithm, the system analyzes termination data based on position, location, and type. It detects unexpected spikes or drops, ensuring early anomaly identification and proactive workforce management.
  • Termination count by position visualization: A bar chart representation of termination trends highlights positions with higher termination rates. This visualization helps organizations pinpoint areas requiring further investigation.
  • Voluntary and involuntary anomaly details: The system provides graphical insights into flagged anomalies, allowing deeper analysis of voluntary and involuntary termination trends. These insights help in understanding workforce stability and potential risk factors.
  • Filtering and data exploration: Users can refine termination data by applying filters based on location, position, and termination type. This feature enables targeted investigations and a more granular understanding of workforce trends.
  • AI training and predictions: The model is trained on a rolling 12-month dataset and is retrained monthly to maintain accuracy. Weekly batch predictions, that are customizable, enhance anomaly detection and provide up-to-date workforce insights.