The modern financial system has come a long way since the Dutch East India Company sold the first public shares. As capital markets developed and diversified, the complex interplay between sellers and buyers of stocks, bonds and other securities matured into a codified method for exchanging assets and financial products.
This method is the trade lifecycle, and it covers everything from trade execution and transaction settlement to reporting. The trade lifecycle is how buyers and sellers interact with each other across markets and firms.
The trade lifecycle can be looked at from a number of different angles. It can be viewed as the interaction between the buy side and sell side of a market transaction, but it can also be split into trading activity and operational activity. Trading activity is everything that happens on the front end of a trade, covering human interactions and automated trading. The operational activity covers everything behind the scenes, such as validation, settlement and reporting.
Banks manage trade lifecycles using personnel and processes supported by data and application information technology. This combination is known as a trade architecture.
Technology drives markets, and the effects of improving technology are easy to see in the trading landscape. Messengers were replaced by telegraphs and telephones, and the stock ticker was replaced by television monitors. Eventually, all of it succumbed to the amazing transistorized computer and the Internet.
With every step, new technology increased the speed at which the trade lifecycle was executed. Every element, from data storage and communication to digital banking, has impacted the way the modern market works. Today, a single trade can be executed in a market from the other side of the world in the blink of an eye.
As the trade lifecycle has accelerated and new instruments proliferated, so too has regulatory complexity. The aftermath of the Great Depression started the wave of modern market regulation, beginning with Glass-Steagall and the Securities and Exchange Act in the 1930s. More legislation is constantly adding and changing regulation, and the trend is global. In the EU, firms are now dealing with European Market Infrastructure Regulation (EMIR) and Markets in Financial Instruments Directive II (MiFID II).
The constantly evolving compliance requirements have placed a burden on trade architectures as technology has struggled to keep up. Efforts expended on shaky architectures result in weak performance, sub-optimal decisions, dissatisfied customers and financial losses. In addition, poorly integrated technology leads to inaccurate, incomplete or inconsistent dirty data.
Flexible, agile database architectures help firms get ahead of constantly evolving regulatory compliance requirements. The technology must adapt to data needs and must evolve from reactive post-trade compliance to proactive trade surveillance.
As the global financial system matured, it has naturally led to consolidation and remarkable growth. This continuing development brings new technological challenges. Growth and consolidation lead to more complicated trade architectures as trading and operational activity touch more systems. Trade data can be spread across disparate storage systems, resulting in data silos.
Add to the burden the weight of history. Maintaining historical data and legacy systems can slow down and destabilize a trade architecture just as much as data silos. Along with slowing trade efficiency, consolidated and aging trade architectures create stumbling blocks for regulatory compliance and automating trade reconstruction.
A legacy trade architecture results in sub-optimal data governance, leading to many downstream application issues. Here are three signs that your trade data architecture may be holding you back:
Lines of business, such as Finance and Risk Management, all need their specific views of the trade data and its relationships. You have multiple and siloed applications providing these views, reducing operational efficiency and increasing your costs.
You can’t be sure that your trade data is complete and accurate, because you need to move it across systems to support your decision-making and risk analytics. You cannot establish relationships between data sets in real time nor track how the data has evolved.
It’s harder and harder for you to keep up with the unprecedented growth in new regulatory mandates. You have siloed controls and inflexible technologies that make it difficult to respond to changing requirements to store, manage, search and report data within tight deadlines. Non-compliance results in hefty fees.
Instead of constantly struggling to keep up every quarter, enterprise architects with responsibility for trading platforms need to look to the future. A superior trading architecture will possess three important characteristics: flexibility, singularity and availability.
In terms of data architecture, flexibility means that data is quickly and easily available to be used in new and innovative ways. New ways of modeling data reveal complex relationships, while better methods for storing granular information about trades provide for stronger research and analysis. Flexibility also means that business logic needs to be easily adaptable. When new methods of analysis are required, complex code changes shouldn’t be needed.
An architecture focusing on singularity tears down the barriers thrown up by data silos. This is a wide-reaching feature that affects the philosophy behind both data storage and applications. A singular architecture needs to be the sole point of authority for data elements, which means that applications need to act as front-end services and eliminate unique individual data stores.
Finally, availability means that data, applications and services are always accessible and usable. Modern architecture supports low-latency storage and access techniques that should be leveraged to provide near-real-time processing of trade data. Key elements of operational activity shouldn’t be left to batch processing—a superior trade architecture can handle trade processing immediately.
Organizations that seek to maximize these three key aspects of a superior trade architecture have found that they need modern software and hardware to implement. Legacy systems do not provide the feature set or processing power to power today’s trade architecture, let alone tomorrow’s!
Banks have been given a golden opportunity to get their trading houses in order and to set the stage for all the advanced technologies (robotic process automation, smart workflows, machine learning, and so on) that are so thoroughly remaking the industry.”
McKinsey & Company
The adoption of Artificial Intelligence (AI) and machine learning will be instrumental in taking trade lifecycle management to the next level. These advanced analytical programs are required to drive continued improvements in business performance by enabling increased operational efficiency through automation, combined with improvements in client servicing and trading strategies. AI is already being used in various client-facing and post-trade functions, but this is only the “tip of the iceberg.” Many AI programs are delayed or discontinued as a result of myriad data quality issues that must be addressed before moving forward.
Without an optimal framework and technology for enabling the highest level of data access and quality management, many AI programs will fall short of expected outcomes. Technology leaders at investment banks recognize this fact and have continued to prioritize near-term investments in data and analytics, but may need to look deeper within their trade architectures to find solutions that improve return on those investments.
In the next part of this series, we will look at the future of trading architectures: the trade data hub. Today’s technology is ready to optimize the trade lifecycle while preparing financial firms for future regulatory compliance.
Like what you just read, here are a few more articles for you to check out or you can visit our blog overview page to see more.
Learn about data bias in AI, ways technology can help overcome it, why AI still needs humans, and how you can achieve transparency.
Successfully responding to changes in the business landscape requires data agility. Learn what visionary organizations have done, and how you can start your journey.
Sharing data can be relatively easy. Sharing our specialized knowledge about data is harder – and current approaches don’t scale.
Don’t waste time stitching together components. MarkLogic combines the power of a multi-model database, search, and semantic AI technology in a single platform with mastering, metadata management, government-grade security and more.Request a Demo