White papers

As a number of countries have successfully transitioned to the compressed settlement standard, many are still to follow suit. In the meantime, market infrastructure operators on both sides of the transformation are impacted by occasional incongruent workflows, mostly resulting from the discrepancies between post trade and the related processes, such as FX management or securities lending – taking place across several time zones. It is curious to see what adjustments are happening on the side of the technology teams tasked with validating the quality of new post-trade platform setups. Does the impact compare in significance to the changes that clearing and settlement organisations have made or are about to undergo?

In the environment dominated by pervasive process and service digitalisation, financial institutions and firms find themselves heavily reliant on technology transformations affecting both their peripheral and core functions. In just 3 years, cloud migration rose from 37% (in August 2020) to 91% in (August 2023) and, just within this past year, North America has moved to the T+1 settlement cycle, spurring unprecedented levels of optimisation in clearing and settlement processes. General-purpose and narrow AI models are being widely deployed in anticipation of “significant” cost savings in the long term. The whirlwind of innovation spans operations, asset classes and the supporting infrastructure.

Throughout the entire existence of Exactpro, adhering to the principle of 'Exitus Acta Probat' – where it is the outcome that validates the actions taken – has been the team’s guiding force. As a company, we firmly believe in persistently pursuing our goals, no matter how challenging, until they are achieved.

In this paper, we describe a case study illustrating this principle: this piece tells a story of a great collaboration between an Exactpro’s local branch and a newly established certification body that helped forge a new professional community transforming the IT landscape in Georgia.

In the fast-evolving landscape of financial technology, many industry players find themselves facing the question of whether they need to respond to the latest trends and harness the power of artificial intelligence (AI) to keep their competitive edge. In the financial services domain, data plays a crucial role, and the abundance of data makes a strong case for leveraging AI in most of the numerous use cases. Whether it is transactional data, market data, customer data, or other financial datasets, AI can extract valuable insights and boost efficiency in the associated tasks.

‘Digital twins’ are known to be a reliable means of system simulation across industries: automotive, space, manufacturing – used to obtain valuable insights about a system’s quality, analyse its performance, and even prevent or remediate breakdowns.

Perhaps the most striking characteristic of today’s financial services landscape is its rapid expansion towards new technologies, new use cases and their implementations. The speed of innovation goes hand in hand with growing complexity: year after year, financial technology platforms become more sophisticated – multi-threaded, distributed, handling increasing volumes of data and aiming at faster processing speeds. A wide variety of emerging technologies creates a new reality, in which new approaches and technology solutions reshape existing subject domains and cause a significant requirements shift. To claim their place in the industry, fintech players – technology providers and end-user institutions alike – have to respond to this trend. On the one hand, they need to rapidly innovate in order to keep their competitive edge. On the other hand, organisations must fulfil their obligations to their clients by ensuring the reliability and robustness of the solutions they build.