Trace data—users’ digital records when interacting with technology—can reveal their cognitive dynamics when making decisions on websites in real time. Here, we present a tracedata method, analyzing movements captured via a computer mouse, to assess potential fraud when filling out an online form. In contrast to existing frauddetection methods, which analyze information after submission, mousemovement traces can capture the cognitive deliberations as possible indicators of fraud as it is happening. We report two controlled studies using different tasks, where participants could freely commit fraud to benefit themselves financially. As they performed the tasks, we captured mousecursor movement data and found that participants who entered fraudulent responses moved their mouse significantly more slowly and with greater deviation. We show that the extent of fraud matters such that more extensive fraud increases movement deviation and decreases movement speed. These results demonstrate the efficacy of analyzing mousemovement traces to detect fraud during online transactions in real time, enabling organizations to confront fraud proactively as it is happening at internet scale. Our method of analyzing actual user behaviors in real time can complement other behavioral methods in the context of fraud and a variety of other contexts and settings.
The Path of the Righteous: Using Trace Data to Understand Fraud Decisions in Real Time (Open Access)
Received: July 30, 2020
Revised: March 30, 2021; August 24, 2021; September 24, 2021
Accepted: October 8, 2021
Published Online as Articles in Advance: Forthcoming
Published in Issue: Forthcoming
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
|Markus Weinmann, Joseph S. Valacich, Christoph Schneider, Jeffrey L. Jenkins, and Martin Hibbeln
|trace data, mousecursor movements, fraud, cognitive dissonance, Bayesian analysis