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Let’s speak the same language: a formally defined model to describe and compare payment system architectures

Kees van Hee et al.

Journal of Financial Market Infrastructures2025https://doi.org/10.21314/jfmi.2025.004article
AJG 1
Weight
0.37

What the paper says

Proposals for new payment system architectures abound. To understand their opportunities and challenges, it is paramount to be able to describe and compare them in a consistent and standardised manner. This paper therefore proposes a formally defined model to represent three key functions of payment system architectures: issuance/withdrawal, holding and transfer of funds. The model defines payment diagrams, using a precisely defined syntax. We illustrate the application of these diagrams for domestic and cross-border account transfers, as well as cash, card, e-money and stablecoin payments. However, the payment diagrams can be used for any type of funds and can be applied across different payment system architectures. We also demonstrate how the diagrams correspond to the balance sheet approach commonly used in economics, and that it offers added value by providing an end-to-end visualisation of every stage of the payment journey. Our model provides a tool for central banks, regulators and the payment industry to better understand and compare existing and new payment system architectures.

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https://doi.org/https://doi.org/10.21314/jfmi.2025.004

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@article{kees2025,
  title        = {{Let’s speak the same language: a formally defined model to describe and compare payment system architectures}},
  author       = {Kees van Hee et al.},
  journal      = {Journal of Financial Market Infrastructures},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.21314/jfmi.2025.004},
}

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Let’s speak the same language: a formally defined model to describe and compare payment system architectures

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.