Catch Me If You Can: Combatting Fraud in Artificial Currency Based Government Benefits Programs
CoRR(2024)
摘要
Artificial currencies have grown in popularity in many real-world resource
allocation settings, gaining traction in government benefits programs like food
assistance and transit benefits programs. However, such programs are
susceptible to misreporting fraud, wherein users can misreport their private
attributes to gain access to more artificial currency (credits) than they are
entitled to. To address the problem of misreporting fraud in artificial
currency based benefits programs, we introduce an audit mechanism that induces
a two-stage game between an administrator and users. In our proposed mechanism,
the administrator running the benefits program can audit users at some cost and
levy fines against them for misreporting their information. For this audit
game, we study the natural solution concept of a signaling game equilibrium and
investigate conditions on the administrator budget to establish the existence
of equilibria. The computation of equilibria can be done via linear programming
in our problem setting through an appropriate design of the audit rules. Our
analysis also provides upper bounds that hold in any signaling game equilibrium
on the expected excess payments made by the administrator and the probability
that users misreport their information. We further show that the decrease in
misreporting fraud corresponding to our audit mechanism far outweighs the
administrator spending to run it by establishing that its total costs are lower
than that of the status quo with no audits. Finally, to highlight the practical
viability of our audit mechanism in mitigating misreporting fraud, we present a
case study based on the Washington D.C. federal transit benefits program. In
this case study, the proposed audit mechanism achieves several orders of
magnitude improvement in total cost compared to a no-audit strategy for some
parameter ranges.
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