Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem

Protocols to identify disgruntled communications are developed and a highly accurate naive Bayes model predicts whether messages contain disgruntled communications, providing extremely relevant information not otherwise likely to be revealed in a fraud audit.

Abstract

Occupational fraud is a $652 billion problem to which disgruntled employees are a major contributor. Much security research addresses reducing fraud opportunity and increasing fraud detection, but detecting motivational factors like employee disgruntlement is less studied. The Sarbanes–Oxley Act requires that companies archive email, creating an untapped resource for deterring fraud. Herein, protocols to identify disgruntled communications are developed. Messages cluster well according to disgruntled content, giving confidence in the value of email for this task. A highly accurate naïve Bayes model predicts whether messages contain disgruntled communications, providing extremely relevant information not otherwise likely to be revealed in a fraud audit. The model can be incorporated into fraud risk analysis systems to improve their ability to detect and deter fraud.