Applying the asymmetric information management technique to insurance claims

Abstract

This study investigates the Asymmetric Information Management (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. To test this, truth tellers (n = 55) provided an honest statement about a lost or stolen item, while liars (n = 53) provided a false claim. All claimants were randomly assigned to either receive the control or AIM instructions. We found that truth tellers provided more information in the AIM condition (compared to the control condition), and discriminant analysis classificatory performance was improved slightly. Unfortunately, the AIM instructions had little effect on the amount of information liars provided. Thus, the AIM technique is useful for supporting truth tellers to be more detailed, but more work needs to be conducted to assess why liars in this study did not adapt a withholding strategy.