Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data
This work addresses the need for automated tools to assess employee risk in the retail industry, though it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of employee theft in retail by developing machine learning models to predict employee liability, using feature selection and dimension reduction to handle high-dimensional data and improve interpretability.
Employee theft and dishonesty is a major contributor to loss in the retail industry. Retailers have reported the need for more automated analytic tools to assess the liability of their employees. In this work, we train and optimize several machine learning models for regression prediction and analysis on this data, which will help retailers identify and manage risky employees. Since the data we use is very high dimensional, we use feature selection techniques to identify the most contributing factors to an employee's assessed risk. We also use dimension reduction and data embedding techniques to present this dataset in a easy to interpret format.