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Post-Trade Automation

The drive for efficiency and transformation.


Whereas the cost pressures caused by the financial crisis of 2008 compelled many banks and brokers to increasingly outsource their non-core operational activities – such as settlement processing - to offshore locations, COVID-19 is an altogether different challenge. This offshoring model – in various instances – was exposed by weaknesses during the pandemic’s peak, prompting many firms to rethink the ways in which they try and maximise synergies and obtain economies of scale.

Scaling back on manual intervention

In the short-term, it is vital that banks and brokers take steps to become less reliant on manual intervention. Not only does this create inefficiencies, but it multiplies the risk of errors, especially if the reference data is inaccurate, unstructured or being procured from multiple sources. One area where automation is urgently required is in trade settlement, not least because the EU’s Central Securities Depositories Regulation (CSDR) – due to take effect in February 2022 – will impose a settlement discipline regime on market participants. In summary, settlement fails could result in counterparties to those transactions being hit with heavy cash penalties or subjected to mandatory buy-ins.

By improving their underlying data management processes and automating settlement activities, the risk of trade fails can be mitigated. If the underlying data quality and analytics (through the incorporation of artificial intelligence [AI]) are improved and automated systems are put in place, then it is possible for organisations to identify problems arising in the trade settlement process well in advance of them actually materialising. This type of AI-led predictive analytics will play a key role in helping financial institutions avoid settlement fails and costly CSDR fines/buy-ins.

Driving efficiency through streamlined multi-asset flows

It remains the case that many firms are still running separate silo-based post-trade processes that rely on separate technology systems and operational workstreams to process transactions involving different financial instruments, whether it be equities, fixed income, money markets or derivatives. Moving to a centralised and consolidated multi-asset solution with a consolidated workflow and view across all assets will help optimise processing times and exception management, while also reducing the cost, complexity and risks associated with running multiple operations and technology silos. And by taking advantage of a central view of holdings, this multi-asset approach supports a more efficient and effective financing and collateral management function, while also reducing exposure to costly buy-ins to cover settlement shortfalls.

Maximising potential through mutualisation

Market participants differentiate themselves through their business propositions and front office capabilities, so when it comes to post-trade there is a lot of overlap, synergy and repetition – at an industry level. A lot of the clerical or non-revenue generating back office services provided by banks on behalf of clients are duplicative, which is highly inefficient. Instead, firms are increasingly turning to mutualised services – shared services based on best-practice technology and operational models and deployed through a SaaS and/or managed service approach that delivers scale economies, instant access to capacity on demand, and resilient service support made possible through a deep pool of resources and expertise. At the leading end, mutualised solutions are founded on advanced technology that supports scale and adaptability, enabling faster and dependable response to changing market dynamics.

Taking innovation to the next level

With a growing need to respond to cost pressures, increasing competition and shifting customer expectations, financial firms are increasingly seeking ways to turn technology innovation into practical business advantage. Indeed the new normal is now driving even greater urgency for firms to implement next-gen technologies as they adapt to new challenges, leveraging what we know as the ABCDs of Innovation – namely AI, blockchain, the Cloud and digital - to transform their operating models and get ready for what’s next.

At its core, AI is about data and scale, so that data can be transformed into actionable insights, while blockchain provides a network of participants and peer-to-peer connections to forge better security and transparency. The Cloud enables an agile approach to innovation, and a hybrid cloud strategy, such as that deployed by Broadridge, makes it possible for firms to scale with confidence in any environment. Finally, digital innovation can transform everyday communications into personalised engagements that fuel growth and deliver greater customer value.

To stay competitive in this current climate, firms must ultimately leverage technology to evolve their operating model and achieve transformational levels of post-trade efficiency. 

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