Supply chain finance smart contracts incorporate rules for order confirmation, financing disbursement, performance verification, and fund settlement into on-chain code. This approach shortens the time for information confirmation and fund processing among multiple parties, but also makes fund security more dependent on the correctness of the code logic. Rule-based static testing struggles to identify cross-function state errors, while individual model checks are susceptible to state space expansion. To address these issues, this study proposes a collaborative method combining graph neural network initial screening and formal verification. Taking order financing as an example, the study constructs a state machine for "order registration—confirmation—credit granting—disbursement—performance—settlement," and extracts core constraints such as fund conservation, role-based permissions, and duplicate withdrawals. The system integrates abstract syntax trees, control flow, data dependencies, and call relationships at the code layer to locate high-risk functions; the system then selects computation tree logic reductions based on the code structure pattern and uses NuSMV to verify key fund paths. The study also elucidates the screening rules for publicly available data sources and real-world deployed contracts, as well as a layered comparison scheme, providing a reproducible research path for pre-deployment auditing of supply chain finance smart contracts.

Jiayi Sun. Vulnerability Detection and Formal Verification Methods of Blockchain Smart Contracts in Supply Chain Finance. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 1-9. https://doi.org/10.38007/SESR.2026.070201.

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