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Improving the Efficiency of Payments Systems Using Quantum Computing

Author

Listed:
  • Christopher McMahon
  • Donald McGillivray
  • Ajit Desai
  • Francisco Rivadeneyra
  • Jean-Paul Lam
  • Thomas Lo
  • Danica Marsden
  • Vladimir Skavysh

Abstract

High-value payment systems (HVPSs) are typically liquidity-intensive because the payment requests are indivisible and settled on a gross basis. Finding the right order in which payments should be processed to maximize the liquidity efficiency of these systems is an NP-hard combinatorial optimization problem, which quantum algorithms may be able to tackle at meaningful scales. We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays. Despite the limitations in size and speed of today’s quantum computers, our algorithm provides quantifiable efficiency improvements when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering each batch of 70 payments as they enter the queue, we achieve an average of Can$240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceed Can$1 billion. This algorithm could be incorporated as a centralized preprocessor into existing HVPSs without entailing a fundamental change to their risk management models.

Suggested Citation

  • Christopher McMahon & Donald McGillivray & Ajit Desai & Francisco Rivadeneyra & Jean-Paul Lam & Thomas Lo & Danica Marsden & Vladimir Skavysh, 2022. "Improving the Efficiency of Payments Systems Using Quantum Computing," Staff Working Papers 22-53, Bank of Canada.
    • Handle: RePEc:bca:bocawp:22-53
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    References listed on IDEAS

    as
    1. Marius Jurgilas & Antoine Martin, 2013. "Liquidity-saving mechanisms in collateral-based RTGS payment systems," Annals of Finance, Springer, vol. 9(1), pages 29-60, February.
    2. Francisco Rivadeneyra & Nellie Zhang, 2022. "Payment Coordination and Liquidity Efficiency in the New Canadian Wholesale Payments System," Discussion Papers 2022-3, Bank of Canada.
    3. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Technology for Economists," Contributions to Economics, Springer, number 978-3-031-50780-9.
    4. Rodney J. Garratt, 2022. "An Application of Shapley Value Cost Allocation to Liquidity Savings Mechanisms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(6), pages 1875-1888, September.
    5. Francisco Rivadeneyra & Nellie Zhang, 2020. "Liquidity Usage and Payment Delay Estimates of the New Canadian High Value Payments System," Discussion Papers 2020-9, Bank of Canada.
    6. Bech, Morten L. & Garratt, Rod, 2003. "The intraday liquidity management game," Journal of Economic Theory, Elsevier, vol. 109(2), pages 198-219, April.
    7. Davey, Nick & Gray, Daniel, 2014. "How has the Liquidity Saving Mechanism reduced banks’ intraday liquidity costs in CHAPS?," Bank of England Quarterly Bulletin, Bank of England, vol. 54(2), pages 180-189.
    8. Fred Glover & Gary Kochenberger & Yu Du, 2019. "Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models," 4OR, Springer, vol. 17(4), pages 335-371, December.
    9. Enghin Atalay & Antoine Martin & James J. McAndrews, 2010. "Quantifying the benefits of a liquidity-saving mechanism," Staff Reports 447, Federal Reserve Bank of New York.
    10. Norman, Ben, 2010. "Liquidity saving in real-time gross settlement systems: An overview," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 4(3), pages 261-276, September.
    11. Galbiati, Marco & Soramaki, Kimmo, 2010. "Liquidity-saving mechanisms and bank behaviour," Bank of England working papers 400, Bank of England.
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    Cited by:

    1. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    2. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    3. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.

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    More about this item

    Keywords

    Digital currencies and fintech; Financial institutions; Financial services; Financial system regulation and policies; Payment clearing and settlement systems;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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