Becoming smarter with the management of data is key to driving efficiency, accuracy, and innovation in most industries, via rules-based automation.
However too often data complexity, caused by integration issues, incompatibility and poor visibility, makes it difficult for Capital Markets organisations to move forward. Instead, they continue to struggle with operational inefficiencies that risk manual data handling errors which, in financial markets, could also mean lost trades.
These inhibitors are especially frustrating given that advanced technologies are geared towards intelligent automation – be they AI-driven analytics, machine learning, blockchain and, in the not too distant future, quantum computing.
The more that organisations can use the infinite brainpower, speed and efficiency, and meticulous accuracy of computers to combine, manage, analyse, validate and report on what’s going on, the better the outcomes and the less the demand on humans for routine data processing.
In Capital Markets trading, automation based on intelligent data management promises breakthroughs on numerous levels. These include the chance to:
- assess and act on more opportunities at speed;
- take an agile approach to
- trading platforms and market data feed integration
- regulatory reporting across multiple jurisdictions; and
- analyse and bring down the cost of trades and compliance, to increase yields.
At a foundational level, intelligent automation also offers to transform data reconciliation, validation and reporting – by managing all of this continuously using rules-driven workflow and, in future, machine intelligence to learn what success looks like and deliver more of it.
Why stop at transaction capture?
Straight-through processing (STP) has been the main thrust of automation until now, eliminating the need to enter trading data manually between systems. The main goals have been to speed up the transaction process, and remove the scope for errors and their implications for financial performance measurement and regulatory reporting.
But the more that trading parties routinely capture and record, manage and monitor data electronically, the greater the scope for using that data in advanced ways to add value for the business and clients. This could be by tracking costs and yields to improve performance, or by identifying common anomalies and errors so that these can be eradicated.
The scale of what’s possible will be limited only by firms’ data architectures – that is, whether these have been set up in a particular way to fulfil a specific requirement, or whether they are sufficiently agile and adaptable to support a range of different use cases – both now and in the future.
It goes without saying that investing once to support multiple scenarios will yield a far greater payback than a piecemeal data strategy geared to a series of discrete purposes.
Key considerations as companies prepare for a smarter data future include:
- Building an agile integration infrastructure
- Creating a central viewing facility
- Establishing clear audit trails
- Supporting intelligent analytics
It’s with this potential in mind that Inforalgo has produced a practical new eBook: Beyond STP. Transforming trading efficiency: 5 steps to intelligent automation. Download it here.