The financial services landscape is undergoing a profound transformation, partly driven by the rapid evolution of distributed ledger technology (DLT) and artificial intelligence (AI). As these technologies continue to advance, they have the potential to revolutionise post-trade processes.
However, despite this promise, market participants have been slow to fully invest in these innovations, often adopting a siloed and tentative approach that fails to fully capitalise on their potential.
In the post-digital era, a more holistic and strategic approach to innovation and adoption will be essential. This requires acknowledgement of the interconnected nature of DLT and AI and harnessing their synergies to address the most pressing challenges in post-trade. These challenges include reducing the number of exceptions, minimising risks, complying with evolving regulations, and ensuring the seamless integration of new technologies with existing systems.
It’s also true that innovation introduces novel risks that require careful management and prudent regulation. By embracing a comprehensive process design and integrated approach, financial institutions can unlock the true revolutionary potential of DLT and AI, and achieve improved levels of efficiency, transparency, and security in their post-trade operations.
Efficiency & Automation
Key evolving challenges in this area include enhancing data quality and reducing manual interventions in trade matching and settlement processes. Firms must also address the need for faster settlement windows, e.g., T+1, T+0.
The convergence of AI and DLT in post-trade processes will significantly enhance efficiency and automation by reducing manual intervention and streamlining operations. DLT will provide a single, immutable ledger that eliminates the need for reconciliation between different parties, significantly speeding up settlement times and reducing errors. Importantly, through consensus protocols and programmable payments, we could see use cases such as unilaterally provable atomic settlement.
AI, particularly generative AI (GenAI), has the capability to automate complex tasks such as trade matching, lifecycle oversight, and real-time data analysis. Examples of this include continuous matching, predictive modelling to enable better resource allocation, and predictive risk profiling to reduce settlement risk failures (e.g., validating SSI details, forecasting bond scarcity, and predicting settlement failure rates). Improved efficiency and processing may also support current and future regulatory requirements, including accelerated settlement cycles like T+1 and T+0.
This combination will reduce operational costs, minimise human error, and enable real-time processing, making post-trade operations more scalable and efficient without the need for complete architecture redesign.
Risk Management
Key evolving challenges in this area including counterparty risk, liquidity management, and mitigating operational errors and failures, which often result in significant regulatory penalties and resolution expenses. Firms must address the need for enhanced compliance, including the detection of anomalies and fraudulent activities, to ensure the integrity of the trade lifecycle.
The integration of AI and DLT will enhance risk management by providing real-time monitoring (e.g., transaction monitoring for AML purposes) and predictive analytics to identify potential settlement risks, thereby reducing the likelihood of settlement failures and financial losses. DLT ensures a transparent, secure record of transactions, which will reduce settlement risk and enhance security.
Additionally, AI will automate compliance checks against regulatory requirements, minimising compliance breaches and ensuring adherence to evolving regulations. When applying rules-based logic, AI may be able to automate certain post-trade functions, e.g., halting asset transfers if outflow velocity exceeds a certain threshold.
One of the most compelling examples of the synergy between AI and DLT in post-trade is the use of AI-enhanced zero-knowledge proof mechanisms. This innovative approach leverages tokenisation to protect sensitive personal information whilst maintaining the integrity of the transactions. AI systems then analyse and validate these zero-knowledge proofs, providing a digital ‘stamp of approval’ once the necessary checks are satisfied.
This fusion of AI and DLT ensures maximum privacy, efficiency, and trust in the post-trade environment, paving the way for more secure and reliable financial transactions.
Interoperability & Integration
Key evolving challenges in this area include liquidity management (e.g., T2(S), RTGS), settlement finality and system integration (e.g., SWIFT), and maintaining value integrity. Firms must address the need to harmonise data across different systems, automating the integration process, and ensuring the seamless integration of new tech with existing systems.
Combining AI and DLT can revolutionise post-trade processes by tackling two major pain points: interoperability and integration. By leveraging AI’s data harmonisation capabilities and DLT’s decentralised architecture, these technologies can seamlessly connect disparate systems, providing the structures and frameworks better suited to the post-digital world, whilst automating the integration processes and ensuring data consistency across the board.
This synergy can transform the landscape, evolving scaling challenges into scaling benefits. As more participants join the network, the collective value and efficiency of the system increases, fostering a powerful network effect that amplifies the benefits of the AI-DLT integration.
DLT can be integrated with existing post-trade infrastructure to enhance its capabilities without requiring a complete overhaul; both traditional and digital securities are supported. This supports legacy processes (e.g., adherence to standards and integrations such as T2(S) and RTGS), as well as future-proof mechanisms (e.g., ledger-ledger interoperability). In parallel, AI can facilitate integrations by harmonising data across different systems and automating the process. Through integration with oracles, AI can ensure that the information supplementing on-chain operations is clean, secure, and accurate, as ultimately, a DLT solution represents integrity but not necessarily truth.
This ensures that all market participants can operate on a unified platform, reducing fragmentation and promoting a more cohesive market environment. We’ll touch more on this in our next article on Mutualisation & Shared Innovation.
Summary
The future of post-trade innovation lies in adapting to the demands of the post-digital world, and that includes adopting a holistic and strategic approach to AI and DLT. By leveraging the strengths of both technologies, financial institutions can unlock unprecedented efficiency, transparency, and security in their post-trade operations.
It is crucial for post-trade service providers to think beyond the silos of AI and DLT and instead, focus on harnessing their synergies to create a more robust, efficient, and scalable market infrastructure for the post-digital world. It’s also essential to consider when the technology would not be appropriate to be deployed: consideration should be given for feasibility, and associated implications.
In the rest of this series, we’ll explore how other DREAM-C technologies relate to the post-digital post-trade world.
This article has been written by Niamh Kingsley (Director, Head of Product Innovation & AI), Olivia Godon (Assistant VP, Post Trade) & Lars Müller (Managing Consultant, DLT Product Development)
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