Department of Conflict Resolution Studies Theses and Dissertations

Date of Award

2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Arts, Humanities and Social Sciences – Department of Conflict Resolution Studies

First Advisor

Dustin D. Berna

Second Advisor

Neil Katz

Third Advisor

Urszula Strawinska-Zanko

Keywords

corporate fraud, corruption, embezzlement, financial fraud, non verbal cues, verbal cues

Abstract

From the earliest financial scams of the seventeenth century, through the headlinegrabbing Wall Street scandals of our time, financial fraud and embezzlement have damaged both domestic and global economic systems. Preventative measures are the best way to reduce fraud. Fraudsters are adaptive and will find ways to circumvent such measures. Detecting fraud is essential once the prevention mechanisms have failed. This dissertation investigated the inherent problems of financial fraud detection for high stake fraudsters in the corporate and political fields in the United States, China and Taiwan. Both verbal and non-verbal signs of deception were examined in the case studies of Bernie Madoff, Taiwanese ex-president Chen Shiu-Bian, and former Chinese politician Bo Xilai.

I was interested in determining what factors are key to the success of a highstake liar? What are the behaviors to look for in liars? Do these behaviors apply to Madoff, Chen and Bo? What was it about their communication styles that convinced their followers and clients to believe them and ignore signs of fraud? Do high-stake liars have different or the same verbal cues and non-verbal cues across the American, Chinese and Taiwanese cultures? Are there consistent patterns and indicators of their body language? If there are, can we apply these same patterns to predicate the next high-stake fraud? My research results show that while observing both verbal and non-verbal communication styles, patterns develop, and these patterns can be used as indicators to help business intelligence and to predict future financial fraud.

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