Termination Anomaly Insights

Overview

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

This solution leverages a machine learning-based anomaly detection technique, Isolation Forest, to flag unexpected spikes or drops in termination counts, enabling early intervention and proactive decision-making.

The model processes current month termination data by grouping terminations based on:
  • Location
  • Position
  • Termination type (voluntary versus involuntary)
  • Quarter of the year

For each group, it calculates the termination count for the current month and compares it against historical data from the same month over the previous two years. If a significant deviation is detected, either a spike or a dip, the group is flagged as an anomaly. These anomalies help users identify unusual trends and investigate the reasons behind the changes.

The system provides:
  • Termination count visualizations
  • Anomaly breakdowns for in-depth analysis
  • Filtering options to refine data and conduct targeted investigations

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 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. For involuntary termination trends, the insights support talent building, acquisition, and development by enabling data-driven decisions in workforce planning, optimizing hiring processes, and enhancing employee engagement initiatives.
  • 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.

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.