Fraudulent Claim Behavior - Thesis Example

Building on this theory Hart et al. (2009) suggest that lies are used for self-protection, avoiding conflict and tension, and the minimization of ill will and hurt feelings. However, there is also a criminal element to deceptive behavior; to obtain a reward not otherwise readily obtainable, and such is the case in insurance fraud. Fraud comprises the deliberate practice of deception for the purpose of securing unlawful and unfair gain (Bolton & Hand, 2002) therefore it could say that the act of committing fraud is closely associated with an array of characteristics elicited by an individual engaging in deception (Ramamoorti, 2008).

As previously discussed in chapter 2, businesses employ a variety of decision support systems in their efforts to detect claims fraud. The systematic review of the literature in chapter 2 identified a general lack of applied behavior cues from the psychological literature to detect deception within a counter-fraud environment.The history of psychological measures of deception has centered around the use of the polygraph however this approach would be highly inappropriate for a commercial business.

As such, Voice Stress Analysers (VSA) are heavily promoted as an alternative and numerous analyzers are available on the market which typically claims that VSA is successful, however, no research to date has demonstrated that VSA actually works (Gamer et al., 2006; Vrij, 2008).It is not just voice characteristics that lack a clear relationship with deception; virtually all nonverbal cues suffer the same fate. As argued by Gamson et al. (2012) and Kapardis (2003), verbal cues have generally been accepted as being more reliable than non-verbal cues in detecting lies.

Hence, it would be critical for the listener to identify any verbal cues that could lead to the identification of deceit however, the success of these interventions depends on understanding reliable cues.Stromwall et al. (2004) wrote about improving accuracy in deception detection after concluding that the most common reason for low accuracy rates was a mismatch between reliable indicators and what people perceive to be indicative of deception.