Employee Turnover Prediction in Banking

The turnover prediction system provides human resource managers with the chance to follow employee behavior and precisely analyze satisfaction levels, even in very big organizations.

Today, employee turnover is emerging as a key concern for financial institutions. However, the bigger the organization, the more difficult the task of keeping an eye on every person. Recent achievements in the field of machine learning allow automatic recognition of the employees who are the most likely to quit. BitRefine group offers solutions, providing accurate predictions to enable companies to take proper action for retention beforehand.

We use more than 40 attributes in our model:

  • Individual attributes including age, education, experience, position, etc.
  • Company attributes including manager’s experience, company growth ratio, etc.
  • Environment attributes, unemployment rate, inflation, salary rate in the industry, etc.

Technically, the turnover prediction classification model is similar to customer churn prediction. However, the difference between the two systems is the data availability. Today, banks and insurance companies collect enormous amounts of data about their customers – way more than the amount of data collected about employees. Typically, the first step toward building an employee turnover prediction system is to organize a diverse internal data mining system. As soon as the dataset is available, we start working with neural networks or other more suitable models, refining accurate information out of this data.

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