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Employee theft and dishonesty is a major contributor to loss in the retailindustry. Retailers have reported the need for more automated analytic tools toassess the liability of their employees. In this work, we train and optimizeseveral machine learning models for regression prediction and analysis on thisdata, which will help retailers identify and manage risky employees. Since thedata we use is very high dimensional, we use feature selection techniques toidentify the most contributing factors to an employee's assessed risk. We alsouse dimension reduction and data embedding techniques to present this datasetin a easy to interpret format.

Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data