The paper was accepted for a presentation at the 2020 WFE Clearing & Derivatives Conference, organized by the World Federation of Exchanges (WFE), the global industry group for both central counterparties (CCPs) and exchanges.
The conference aimed to bring together academics, policy makers and practitioners to share original research and to exchange ideas on the future of central and bilateral clearing in light of regulatory reforms, changes to market structure, and technological developments. It was planned to be held in March 2020 and was canceled due to the Covid-19 pandemic. The accepted papers were published as a part of a special JFMI issue.
Emerging technologies, such as artificial intelligence (AI) and distributed ledger technology, are increasingly being adopted by financial institutions, promising functional efficiency and cost reduction to stakeholders and users. However, the structural and functional changes associated with the technological transformation of software platforms pose significant operational risks. While some aspects of these risks are well known and studied (such as AI trustworthiness, data privacy and consistency, platform availability and information security), others are underestimated. The extreme complexity and nondeterministic nature of existing technology infrastructures still need to be addressed, as they will soon be inherited by the platforms built with new technologies. The only way to mitigate these risks is extensive endto-end professional testing. This paper discusses the software-testing challenges of traditional central counterparties as well as the risks, biases and problems related to new technologies. It also outlines a set of requirements for an end-to-end validation and verification solution aimed at the new generation of clearing platforms.
Focusing on one of the most common use cases in the capital markets industry, this paper considers the challenges posed by the introduction of blockchain and AI into the post-trade area. Keywords: central counterparty clearing; post-trade platform; test automation; software testing; distributed ledger technology (DLT); artificial intelligence (AI).
Elena Treshcheva, Rostislav Yavorsky and Iosif Itkin
The authors are very grateful to Alyona Bulda for her extensive contributions to the test automation approach for DLT-based post-trade platforms. We would also like to thank Alexey Zverev, who orchestrates test automation at Exactpro, for sharing his ideas with us. Special thanks goes to Vladimir Panarin, who led the development of the framework prototype described in this paper.