Academic Journal
Are You, You? Seamlessly Fighting Identity Fraud with Keystroke Dynamics.
| Τίτλος: | Are You, You? Seamlessly Fighting Identity Fraud with Keystroke Dynamics. |
|---|---|
| Συγγραφείς: | Kim, David1 (AUTHOR) david.kim7@tcu.edu, Valacich, Joseph S.2 (AUTHOR) valacich@arizona.edu, Jenkins, Jeffrey L.3 (AUTHOR) jjenkins@byu.edu, Wilson, David W.3 (AUTHOR) davidwilson@byu.edu, Kumar, Manasvi4 (AUTHOR) ma.kumar@northeastern.edu, Weisgarber, Paul2 (AUTHOR) paulweisgarber@arizona.edu |
| Πηγή: | Information Systems Research (INFORMS). Sep2026, Vol. 37 Issue 3, p1854-1872. 19p. |
| Θεματικοί όροι: | *Identity theft, *Data security failures, Biometry, User experience, Fraud investigation |
| Περίληψη: | In September of 2017, Equifax disclosed a data breach that exposed the personal information of 147 million people, including names, Social Security numbers, and addresses. Such high-profile data breaches have rendered traditional forms of identity verification—especially knowledge-based authentication (KBA)—worse than useless: fraudsters have a 92% success rate in KBA screenings, compared to just 46% for genuine customers. In the face of these challenges, digital platforms are turning to sparse alternative data sources and overt verification technologies, often to the detriment of the user experience. The objective of this research is to design and build a novel approach to identity verification for new platform users using digital behavior data—features that describe how users type and interact during account setup. The system (1) evaluates identity fraud risk for all first-time users, and (2) minimizes the impact on the new user experience by seamlessly analyzing behavior during a platform's existing onboarding experience. We evaluated and improved the design in four experiments, culminating in an identity fraud detection tool that effectively detects identity fraud for first-time users and supports seamless user experiences. Identity fraud—the deliberate use of synthetic or stolen identity details for gain—affects an increasing variety of digital platforms. With traditional methods of identity verification compromised because of high-profile data breaches, platforms are turning to sparse alternative data sources and overt verification technologies, often to the detriment of the user experience. Drawing from established deception and motor learning theories, we follow the design science method to design, build, and iteratively refine a novel approach to identity verification for new platform users using keystroke dynamics. The system (1) evaluates identity fraud risk for all first-time users, and (2) minimizes the impact on the new user experience by seamlessly analyzing keystrokes from a platform's existing onboarding form. We evaluated and improved the design in four experiments, culminating in an identity fraud detection tool that effectively detects identity fraud for unknown users and supports seamless user experiences. The system and our lessons from the design and evaluation process have important implications for both research and practice. History: Jason Thatcher, Senior Editor; Heng Xu, Associate Editor. [ABSTRACT FROM AUTHOR] |
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