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

Authors:
Carolyn Holton
Affiliations:
Management Information Systems, College of Business and Legal Studies, Southeastern University, 1000 Longfellow Blvd, Lakeland, FL 33801, United States
Venue:
Decision Support Systems
Year:
2009

Citing 15
Cited 11

Scatter/Gather: a cluster-based approach to browsing large document collections

SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Some advances in transformation-based part of speech tagging

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Coping with systems risk: security planning models for management decision making

MIS Quarterly
Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Concept decompositions for large sparse text data using clustering

Machine Learning
Morality and Computers: Attitudes and Differences in Judgments

Information Systems Research
Understanding the Offender/Environment Dynamic for Computer Crimes: Assessing the Feasibility of Applying Criminological Theory to the IS Security Context

HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
The Value of Intrusion Detection Systems in Information Technology Security Architecture

Information Systems Research
Independence and commitment: assumptions for rapid training and execution of rule-based POS taggers

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Matching information security vulnerabilities to organizational security profiles: a genetic algorithm approach

Decision Support Systems - Special issue: Intelligence and security informatics
Access control and audit model for the multidimensional modeling of data warehouses

Decision Support Systems
Network externalities, layered protection and IT security risk management

Decision Support Systems
Predicting going concern opinion with data mining

Decision Support Systems
Design science in information systems research

MIS Quarterly
The role of behavioral research and profiling in malicious cyber insider investigations

Digital Investigation: The International Journal of Digital Forensics & Incident Response

Making words work: Using financial text as a predictor of financial events

Decision Support Systems
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

Decision Support Systems
Mining economic sentiment using argumentation structures

ER'10 Proceedings of the 2010 international conference on Advances in conceptual modeling: applications and challenges
Assessing the severity of phishing attacks: A hybrid data mining approach

Decision Support Systems
Detecting complex account fraud in the enterprise: The role of technical and non-technical controls

Decision Support Systems
A clustering study of a 7000 EU document inventory using MDS and SOM

Expert Systems with Applications: An International Journal
Polarity analysis of texts using discourse structure

Proceedings of the 20th ACM international conference on Information and knowledge management
Soft computing based imputation and hybrid data and text mining: The case of predicting the severity of phishing alerts

Expert Systems with Applications: An International Journal
Business intelligence in risk management: Some recent progresses

Information Sciences: an International Journal
Mining Deviations from Patient Care Pathways via Electronic Medical Record System Audits

ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
Knowledge discovery in inspection reports of marine structures

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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 naive 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.