When AI Agents Pay: A Rollback Governance Framework for Fraud and Execution Failure in Agentic Stablecoin Payments

Stablecoin payment rails and agentic commerce are converging toward an environment in which autonomous AI agents initiate and settle payments on behalf of users without human authorisation of each transaction. This exposes a governance gap: chargeback mechanisms are too slow and too broad for machine speed execution failures, settlement finality precludes open-ended reversibility, and reversible-token standards supply mechanisms without a governance framework for their use. This paper develops such a framework for rollback, a risk-bounded recourse mechanism for high-confidence fraud and execution-failure events, specifying the first-level eligibility and routing logic of a three-layer recourse architecture (prevention, rollback, adjudication) through five governance dimensions formalised as propositions: trigger authority, rollback-eligible conditions, evidence standards, temporal boundaries, and outcome pathways. Developed through conceptual framework analysis informed by design science research, it is evaluated through scenario analysis across five payment-failure cases and a formative expert consultation. The scenarios route consistently, including a deliberately ambiguous case sent to adjudication under the framework’s own logic; the consultation provides preliminary support for all five dimensions. The framework is presented as a first-level eligibility and routing layer; the enabling institutional layer that must operate it (adjudicator accountability, appeal rights, liability allocation, and remedy funding) is delineated but deliberately not specified.

Authors

Institutions

Publication Details

Journal
FinTech
Published
2026-09-01
DOI
https://doi.org/10.3390/fintech5030075
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

When AI Agents Pay: A Rollback Governance Framework for Fraud and Execution Failure in Agentic Stablecoin Payments

Kamal Al‐Sabahi, Yousuf Khamis Al Mabsali
FinTech
Ethics and Social Impacts of AI
article

When AI Agents Pay: A Rollback Governance Framework for Fraud and Execution Failure in Agentic Stablecoin Payments

Kamal Al‐Sabahi, Yousuf Khamis Al Mabsali
article en

Abstract

Stablecoin payment rails and agentic commerce are converging toward an environment in which autonomous AI agents initiate and settle payments on behalf of users without human authorisation of each transaction. This exposes a governance gap: chargeback mechanisms are too slow and too broad for machine speed execution failures, settlement finality precludes open-ended reversibility, and reversible-token standards supply mechanisms without a governance framework for their use. This paper develops such a framework for rollback, a risk-bounded recourse mechanism for high-confidence fraud and execution-failure events, specifying the first-level eligibility and routing logic of a three-layer recourse architecture (prevention, rollback, adjudication) through five governance dimensions formalised as propositions: trigger authority, rollback-eligible conditions, evidence standards, temporal boundaries, and outcome pathways. Developed through conceptual framework analysis informed by design science research, it is evaluated through scenario analysis across five payment-failure cases and a formative expert consultation. The scenarios route consistently, including a deliberately ambiguous case sent to adjudication under the framework’s own logic; the consultation provides preliminary support for all five dimensions. The framework is presented as a first-level eligibility and routing layer; the enabling institutional layer that must operate it (adjudicator accountability, appeal rights, liability allocation, and remedy funding) is delineated but deliberately not specified.

FinTechVol. 5(3)
OQ (Oman) (OM)
Peace, Justice and strong institutions
Openalex Percentile: Top 7%
Ethics and Social Impacts of AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.