Custom trading software development

One particular set of regulations, the Basel Regulatory Framework, stands out in its impact on the trading systems’ computing needs. To adhere to the Framework, organizations must keep their trading and banking books separate and follow the Fundamental Review of the Trading Book (FRTB).

To comply with FTRB, a trading system will need, according to some estimates, between three and ten times more computing resources. FTRB also requires multi-scenario risk and valuation analysis and comprehensive reporting. These requirements increase the data volumes the system has to process tenfold as well.

The increases in the compute workload and data volumes leave trading platforms no choice but to look beyond legacy systems and into the cloud. Cloud computing allows for instant on-demand scalability; so, organizations can expand or reduce their resources based on their needs. It is also more cost-efficient compared to on-premises solutions.

Some organizations may be tempted to use trading and risk systems themselves for enterprise data management (EDM) purposes. However, we don’t advise this approach since these systems aren’t designed for EDM and aren’t suitable for executing a centralized EDM strategy.

What we recommend instead is implementing a separate EDM system that aggregates data from all available sources, including trading and risk systems. This system serves as the single source of truth for the whole organization, eliminating the risk of having multiple versions of the same data entry.

An EDM system can also enhance data quality by automatically flagging confidential data or removing duplicate data entries, for example. And coupled with AI-powered data analytics, an EDM can derive specific insights necessary for well-informed decision-making at all management levels.

Overall, risk-based requirement prioritization saves you time, money and effort during development. Here’s how:

• It ensures all requirements are clear to the development team
• It reveals untestable requirements and allows for making them into testable ones before requirement changes become too costly
• It forces all stakeholders to focus on the testing processes before production kicks off
• It helps you pinpoint critical requirements and ensure their quality with more thorough testing
• It keeps all stakeholders updated throughout the project
• It quantifies the risks your project carries, allowing you to put mitigation measures in place
• It can optimize user acceptance testing by removing it for low-priority requirements (only if you agree to it)
• It streamlines the entire testing process, making it more efficient and reducing costs

There are four regulatory challenges that banks have to continuously deal with as the legal requirements evolve:

• Regulatory reporting. Regulators require more and more complex calculations to assess risk and valuation. It increases the data volumes banks need to process to comply with those requirements
• Risk assessment. Some banks may have to work with a fragmented set of systems and applications built with different technologies. This complicates data collection and analytics required for multi-scenario risk assessment
• Anti-money laundering (AML) and know-your-customer (KYC) requirements. As they grow in complexity, they can no longer be met using only manual labor. Banks have to introduce automated workflows to enforce AML and KYC
• Trade surveillance. Making sense of the rising mountains of data is impossible without the right solution in place. And leveraging this data is key to gaining insights into customers and operations, preventing fraud and successfully handling regulatory audits