Abstract

The increasingly complex economic and financial environment in which we live makes the management of liquidity in payment systems and the economy in general a persistent challenge. New technologies make it possible to address this challenge through alternative solutions that complement and strengthen existing payment systems. For example, interbank balancing and clearing methods (such as real-time gross settlement) can also be applied to private payments, complementary currencies, and trade credit clearing to provide better liquidity and risk management. The paper defines the concept of a balanced payment system mathematically and demonstrates the effects of balancing on a few small examples. It then derives the construction of a balanced payment subsystem that can be settled in full and therefore that can be removed in toto to achieve debt reduction and payment gridlock resolution. Using well-known results from graph theory, the main output of the paper is the proof—for the general formulation of a payment system with an arbitrary number of liquidity sources—that the amount of liquidity saved is maximum, along with a detailed discussion of the practical steps that a lending institution can take to provide different levels of service subject to the constraints of available liquidity and its own cap on total overdraft exposure. From an applied mathematics point of view, the original contribution of the paper is two-fold: (1) the introduction of a liquidity node with a store of value function in obligation-clearing; and (2) the demonstration that the case with one or more liquidity sources can be solved with the same mathematical machinery that is used for obligation-clearing without liquidity. The clearing and balancing methods presented are based on the experience of a specific application (Tetris Core Technologies), whose wider adoption in the trade credit market could contribute to the financial stability of the whole economy and a better management of liquidity and risk overall.

Highlights

  • This paper provides the mathematical foundations of an algorithm discussed in a recent companion paper that presents liquidity-saving in payment systems through the analysis and visualization of an empirical data set (Fleischman et al 2020)

  • We focus on a particular implementation of the multilateral set-off of obligations between companies, the centralized software application Tetris Core Technologies (TCT) developed by Be Solutions, which has been running uninterrupted in Slovenia since 1991 in support of the trade credit market (Schara and Bric 2018)

  • From an applied mathematics point of view, the original contribution of the paper is two-fold: (1) the introduction of a liquidity node with a store of value function in obligation-clearing; and (2) the demonstration that the case with one or more liquidity sources can be solved with the same mathematical machinery that is used for obligation-clearing without liquidity, including the application of the Minimum Cost Flow optimization method (Király and Kovács 2012) to Liquidity-saving mechanisms (LSMs)

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Summary

Introduction

This paper provides the mathematical foundations of an algorithm discussed in a recent companion paper that presents liquidity-saving in payment systems through the analysis and visualization of an empirical data set (Fleischman et al 2020). The BeSolutions TCT algorithm is proprietary, we feel that obligation-clearing as a financial instrument is so important for supporting cash-strapped small and medium-sized enterprises (SMEs) that the mathematical logic underpinning it should be disseminated widely and made as accessible as possible, in preparation for eventually opening it up. Most payment systems are becoming more open to a greater number of direct participants, and are leveraging centralized architectures to implement advanced liquidity-management tools. This evolution is necessary for the payment systems to be able to keep up with the development of the economy. This evolution is necessary for the payment systems to be able to keep up with the development of the economy. Galbiati and Soramäki (2010)

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