Reed Olsen and Dr. Conan C. Albrecht, Information Systems

“Albert Miano, a manger at Reader’s Digest responsible for processing bills from painters and carpenters, embezzled $1 million over a 5-year period. Miano forged the signatures of a superior on an invoice for services never performed and submitted it to accounts payable. He forged the endorsement on the painter’s account and deposited it in his own account. ” How did this fraudulent activity go undetected for so long?

Companies large and small are all subject to fraud in its many forms. If these companies have access to a wide variety of fraud detection tools, they can be better prepared to prevent and detect fraud schemes. Forensic accountants and professional auditors have a battery of analysis tools at their disposal to detect corporate fraud. However, these tools are much too complex and are often unavailable to interested users.

Another option for auditors and fraud examiners is to use an open-source data analysis platform. As an open-source program, people throughout the world can add features and improve the overall functionality of the software. Following the principles of synergy, a set of tools that is developed by a greater number of users will be more robust and versatile.

My goal was to provide users and developers with specific information about fraud schemes and allow them to create powerful analysis tools. For each fraud scheme, I outlined how the crime is perpetrated, how it can be detected, and provided supplemental data about fraud scheme.

One of the challenges that I faced was finding a collection of fraud schemes that could be termed comprehensive and applicable to our method of detection. At the beginning of my research, I planned to study 10-20 fraud schemes in detail. However, at the time of writing, nine have been identified and studied in depth. Part of the problem of collecting a large sample of applicable fraud schemes arises from the fact that any researcher is faced with hundreds of candidates for analysis. The challenge is to identify which schemes can be detected by specific methodologies; certain types of fraud can be identified by discrepancies in quantitative data, while others are limited to qualitative inconsistencies. While both are important in the detection of fraud, I favored those schemes that could be detected via quantitative data analysis.

Through my research, I gained a greater understanding of what types of challenges an auditor or fraud examiner faces in their field of work. I plan to further my research by contributing subsequent findings to the development community.

References

• Romney, Marshall B., and Paul Steinbart. Accounting Information Systems. Upper Saddle River: Pearson, 2006.
• Raymond, Eric S. The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. O’Reilly, 2001.