Toward reducing the operational risk of emerging technologies adoption in central counterparties through end-to-end testing

Elena Treshcheva; Rostislav Yavorsky; Iosif Itkin

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Emerging technologies that are widely adopted by financial institutions promise functional efficiency and cost reduction, but also pose a number of risks. Extreme complexity and non-deterministic nature of the existing technology platforms are commonly underestimated and need to be addressed, as they will be imminently inherited by the platforms built with the new technologies.
Potential risks associated with traditional technology platforms in the financial services industry stem from the challenges posed by their multicomponent structure, large number of endpoints and system interdependencies, participant structure complexity, multitude of asset classes and associated life cycle events and their system schedules, variety of protocols and APIs, complex calculations, and distributed multithreaded architecture.
The risks induced by the existing complexity of FMIs are amplified by some of the characteristics of the emerging technologies. Infusing traditional CCP technology stack with DLT leads to significant platform transformations and associated interoperability issues at the confluence of traditional technology components and those built with DLT. In its turn, AI transformation, in addition to obvious technical challenges of data collection and preprocessing as well as building a trustworthy model, requires additional attention to avoid biases and ensure regulatory compliance.
To address these challenges, a robust software testing approach is needed. Stochastic processes related to multi-threaded distributed processing across multiple nodes and uncertainties related to machine-learning models require sophisticated testing methods to ensure resilience and trustworthiness of mission-critical software platforms.
The proposed approach suggests incorporating both active and passive testing techniques reinforced with the statistical analysis of test execution data. High-volume automated testing of distributed clearing systems helps to expand the test coverage and create production-like conditions.
Test automation framework described in the paper emulates the nodes in CCP infrastructures, generates API calls, and triggers transaction ﬂows. The verification process of bi-directional message ﬂows suggests that the framework stores all the messages, sent or received to/from the non-blockchain parts of the hybrid system alongside the data extracted from the ledger to enable passive testing and property-testing over many random cases. The framework provides a platform for building an extensive regression testing library covering functional and non-functional aspects of clearing platforms of any complexity in order to reduce operation risk involved in their implementation and ongoing exploitation in the live service.

Abstract

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.

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