# Proactive Intervention to Downtrend Employee Attrition using Artificial Intelligence Techniques

**Authors:** Aasheesh Barvey, Jitin Kapila, Kumarjit Pathak  
**arXiv:** [1807.04081](https://arxiv.org/abs/1807.04081)  
**Date:** 2018-07-11  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1807.04081v1/

## Key points

- The paper aims to predict employee attrition in advance and enable management to take personalized preventive action.
- The authors propose the use of ensemble classification modeling techniques and linear regression to develop a model with over 91% accuracy in predicting employee attrition.
- The model provides insights into the lead-time in separation and individual reasons causing attrition.
- Managers can take proactive measures to retain employees or manage the business consequences of attrition effectively with prior intimation of employee attrition.
- The deployment of this model is expected to help downtrend employee attrition and enable managers to manage their teams more efficiently.
- The model does not cover natural calamities or unforeseen events at an individual level such as accidents or deaths.

## Summary

The paper titled "Proactive Intervention to Downtrend Employee Attrition using Artificial Intelligence Techniques" by Aasheesh Barvey, Jitin Kapila, and Kumarjit Pathak aims to predict employee attrition in advance and enable management to take personalized preventive action. The authors propose the use of ensemble classification modeling techniques and linear regression to develop a model that can accurately predict employee attrition with over 91% accuracy. This model not only predicts the likelihood of attrition but also provides insights into the lead-time in separation and individual reasons causing attrition. By providing prior intimation of employee attrition, managers can take proactive measures to retain employees or manage the business consequences of attrition effectively. The deployment of this model is expected to help downtrend employee attrition and enable managers to manage their teams more efficiently. However, it should be noted that the model does not cover natural calamities or unforeseen events at an individual level such as accidents or deaths. Overall, this research offers valuable insights into using artificial intelligence techniques for predicting employee attrition and highlights the importance of proactive intervention in managing workforce dynamics.