Applying the Asymmetric Information Management Technique to Insurance Claims

Research has previously examined the ability of the Asymmetric Information Management (AIM) technique to help distinguish between truth tellers and liars in police interview settings. The study investigates the AIM technique’s ability to detect fraudulent insurance claims submitted online. The AIM instructions inform claimants that, inter alia, more detailed statements are easier to accurately classify as genuine or fabricated. These instructions encourage truth tellers to be more detailed than they would normally, while encouraging liars to suppress more information. The statements are then coded for overall detail as part of the AIM technique process. Truth tellers (n = 55) provided an honest statement about an item worth £100-1,000 that was lost or stolen within the previous 3 years. Liars (n = 53) provided a false claim and pretended that they lose an item within the previous 3 years. All claimants were randomly assigned to either receive the control form or the AIM form which included the AIM instructions. Truth tellers provided (and liars withheld) more information in the AIM condition (compared to the control condition), and discriminant analysis classificatory performance was improved. Therefore, the AIM technique appears
useful for distinguishing between genuine and fabricated insurance claims.