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When Lying Does Not Pay: How Experts Detect Insurance Fraud

Danielle E. Warren () and Maurice E. Schweitzer ()
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Danielle E. Warren: Management & Global Business, Rutgers Business School –Newark & New Brunswick
Maurice E. Schweitzer: The Wharton School, University of Pennsylvania

Journal of Business Ethics, 2018, vol. 150, issue 3, 711-726

Abstract: Abstract A growing literature has focused on understanding how to detect and deter unethical consumer behavior. In this work, we focus on a particularly important type of unethical consumer behavior, consumer insurance fraud, and we analyze a unique dataset to understand how experts investigate suspicious claims. Two separate but related literatures inform the process of investigating suspicious insurance claims. The first literature is grounded in field research and emphasizes the importance of secondary sources. The second literature is grounded in laboratory studies that emphasize the importance of interpersonal interactions. Here we draw upon both literatures to consider the importance of claimant interviews within the context of many investigative actions and the potential for claimants to avoid interviews. In an empirical study using qualitative and quantitative data from auto insurance claim investigations, we analyze investigative chronologies conducted by skilled experts. In doing so, we find that even when investigators can access information from a variety of sources such as witnesses, databases, and physical evidence, claimant interviews are the most important step in determining whether or not claims are denied or paid. Furthermore, we identify interpersonal avoidance as an important signal of unethical claimant behavior. Our findings inform deception detection theory and practice. We identify implications for deception detection in business, particularly for consumer unethical behavior and insurance fraud investigations.

Keywords: Consumer ethics; Customer dishonesty; Deception detection; Insurance fraud; Interpersonal avoidance; Investigations (search for similar items in EconPapers)
Date: 2018
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