Securities trading is one of the foundations of the global financial system, having significant impacts on liquidity, economic growth, and risk management. In turn, financial services firms must be able to execute trades instantly because delays of even a few milliseconds can result in lost opportunities for clients.
Today, many firms still experience significant trade processing lag when trading volumes spike, such as in response to major financial or geopolitical news, because trade execution speed depends on processing huge amounts of data, and when trading spikes, so does the amount of data that needs to be processed. To maximize the benefits for their clients and themselves, financial services firms need to up their data speed and scale capacity.
Data-Intensive Financial Trade Management
Portfolio managers and individual stock traders are accustomed to selecting a trade order, answering a few questions, clicking on a purchase icon, and watching a portfolio update. Behind the scenes, however, trades in the highly regulated financial services industry must pass many compliance and risk exposure checks, including:
- Regulatory limits, such as position limits and reporting thresholds
- Internal compliance policy checks, such as ethical standards and client-specific investment restrictions
- Regulatory requirements, such as know your customer (KYC), anti-money laundering (AML), and market abuse, such as insider trading
To process all these checks, financial services firms use a financial trade management system, a framework for ensuring efficient and compliant trade processing throughout the trade lifecycle. The automated systems comprising the framework, including a purpose-built order management system (OMS), perform the required checks, reduce operational risk, ensure continuity, and maintain accurate financial records.
A key step in the financial trade management lifecycle is pre-trade validation. When the OMS receives a trade order, the order is immediately processed based on the following general workflow.
- The order is received and placed in a queue.
- Relevant data is fetched from various databases, including customer data, market data, and compliance rules.
- The compliance and risk assessment checks are performed using rule-based engines, pre-configured algorithms, and historical data.
- A decision engine then determines whether the order can proceed, needs modification, or should be rejected, and the decision is sent back to the OMS.
This workflow is extremely data and compute-intensive as each trade requires access to multiple databases, the execution of complex business rules, and the movement of data across different systems. And all this activity must be accomplished instantly for millions of security trades every day.
How to Achieve Optimal Financial Trade Management at Any Scale
The GridGain unified real-time data platform delivers the performance and scale financial services firms need to ensure trades are processed instantly, even when trade volumes soar. By running all computations in GridGain’s distributed in-memory layer to implement parallel processing and co-locate business logic with data, GridGain can:
- Consume trade and market data at extremely high velocity.
- Execute complex rules based on corporate or regulatory guidelines on the trade request and incoming trade data.
- Dynamically scale the number of nodes or partitions as needed, enabling continued high performance during trading spikes without the need to maintain expensive infrastructure that often sits idle.
- Provide reliability and redundancy by running fully synced backups of data placed in RAM on different VMs placed locally in a datacenter or stretched to the cloud to avoid downtime.
To learn more about how GridGain’s unified real-time data platform can accelerate trade processing at scale, read this article for a quick primer, download this eBook, or contact us directly.