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The topology of card transaction money flows

Author

Listed:
  • Massimiliano Zanin
  • David Papo
  • Miguel Romance
  • Regino Criado
  • Santiago Moral

Abstract

Money flow models are essential tools to understand different economical phenomena, like saving propensities and wealth distributions. In spite of their importance, most of them are based on synthetic transaction networks with simple topologies, e.g. random or scale-free ones, as the characterisation of real networks is made difficult by the confidentiality and sensitivity of money transaction data. Here we present an analysis of the topology created by real credit card transactions from one of the biggest world banks, and show how different distributions, e.g. number of transactions per card or amount, have nontrivial characteristics. We further describe a stochastic model to create transactions data sets, feeding from the obtained distributions, which will allow researchers to create more realistic money flow models.

Suggested Citation

  • Massimiliano Zanin & David Papo & Miguel Romance & Regino Criado & Santiago Moral, 2016. "The topology of card transaction money flows," Papers 1605.04938, arXiv.org.
  • Handle: RePEc:arx:papers:1605.04938
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    References listed on IDEAS

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    1. Hu, Mao-Bin & Jiang, Rui & Wu, Qing-Song & Wu, Yong-Hong, 2007. "Simulating the wealth distribution with a Richest-Following strategy on scale-free network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 467-472.
    2. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    3. Wang, Yougui & Ding, Ning & Zhang, Li, 2003. "The circulation of money and holding time distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(3), pages 665-677.
    4. M.-B. Hu & W.-X. Wang & R. Jiang & Q.-S. Wu & B.-H. Wang & Y.-H. Wu, 2006. "A unified framework for the pareto law and Matthew effect using scale-free networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 53(2), pages 273-277, September.
    5. Beyeler, Walter E. & Glass, Robert J. & Bech, Morten L. & Soramäki, Kimmo, 2007. "Congestion and cascades in payment systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 693-718.
    6. Adrian A. Dragulescu & Victor M. Yakovenko, 2002. "Statistical Mechanics of Money, Income, and Wealth: A Short Survey," Papers cond-mat/0211175, arXiv.org.
    7. Reed, William J., 2001. "The Pareto, Zipf and other power laws," Economics Letters, Elsevier, vol. 74(1), pages 15-19, December.
    8. Wu, Fang & Huberman, Bernardo A. & Adamic, Lada A. & Tyler, Joshua R., 2004. "Information flow in social groups," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 327-335.
    9. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    10. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
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    Cited by:

    1. Mikrajuddin Abdullah, 2022. "Introducing Cashless Transaction Index based on the Effective Medium Approximation," Papers 2209.13470, arXiv.org.
    2. Massimiliano Zanin & Miguel Romance & Santiago Moral & Regino Criado, 2018. "Credit Card Fraud Detection through Parenclitic Network Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, May.
    3. Iglesias Pérez, Sergio & Moral-Rubio, Santiago & Criado, Regino, 2021. "A new approach to combine multiplex networks and time series attributes: Building intrusion detection systems (IDS) in cybersecurity," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    4. Carolina E S Mattsson & Teodoro Criscione & Frank W Takes, 2022. "Circulation of a digital community currency," Papers 2207.08941, arXiv.org, revised Jun 2023.
    5. Sergio Iglesias Perez & Regino Criado, 2022. "Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs," Mathematics, MDPI, vol. 11(1), pages 1-24, December.

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