Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data

TL;DR

This paper develops machine learning models to predict employee risk in retail using high-dimensional data, employing feature selection and visualization techniques to aid in risk assessment and management.

Contribution

It introduces optimized regression models and novel visualization methods tailored for high-dimensional retail employee data, enhancing risk analysis capabilities.

Findings

Effective feature selection identified key risk factors.

Dimension reduction improved interpretability of complex data.

Models achieved accurate risk prediction performance.

Abstract

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.