Volume 4 • Issue 2 • PP: 01–08 • 2024
The Fraud-Protection Paradox in Consumer Payments
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Consumer-payment risk is hard to measure because instruments that have the highest incidence of reported fraud are often still in high usage and trusted. The key debate is when to break down the line between weak security and a protection ecosystem that will minimise the impact consumers suffer following an incident, through monitoring, liability allocation, dispute handling and recovery. This paper constructs a framework for risk alignment between instrument-level fraud incidence, perceived security, and realized payment use, and breaks down the difference between persistent instrument-level differences and short-run changes within instruments. Official U.S. estimates for cash, checks, debit cards, and credit cards are arranged in a balanced panel for the years 2015-2020 and an extended descriptive sample for 2022. Combined results of pooled regression, two-way fixed effects, first differences, and leave-one-year-out ridge validation to ascertain if fraud exposure can be predicted after accounting for stable instrument characteristics and common annual shocks. Regulators and payment providers need to address this measurement challenge since a narrow focus on incident counts can divert investment focus from detection, liability limits, recovery, and reimbursement capabilities that build consumer confidence and payment-system robustness. Credit-card payment share rose from 18.3% in 2015 to 31.3% in 2022 while 10.3% of adopters reported loss, theft, or fraud in 2022; the pooled fraud coefficient of 3.86 (p < .001) fell to 2.20 (p = .185) under instrument and year fixed effects, and lagged fraud did not predict annual share change (−0.27, p = .401). The validation using leave-one-year-out results in an R2 = .873 and MAE of 2.90 percentage points, whereas the instrument-history validation produces an MAE of 2.71.
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