Christopher Adeyeri is associate director – head of Technology at Astbury Marsden.
Financial services businesses have for many years accumulated a wealth of market and client data to help them do what they do better, faster and more efficiently.
However, while banks and asset managers have been using techniques such as Value at Risk modelling to measure and quantify the level of financial risk on a portfolio of assets since the 1980s, investment banks and asset managers have been slower to embrace today’s definition of “big data analytics” than many consumer retail businesses, technology businesses, and even retail banking.
So what has changed and why is ‘big data analytics’ now reaching into every sector and function of the global economy?
Firstly, the kind of data that is collected and analyzed by financial services business, including asset managers and fund managers, has shifted fundamentally from structured data – such as economic or trading data – to unstructured data. Unstructured data can include sources such as web pages, open and unopened e-mails, word processing documents, social media posts, and marketing material.
Secondly, at financial services business – and the large investment banks in particular – big data analytics teams have now become an established stand-alone functional department rather than a series of small subsets of internal business units, communicating to senior-level executives key insights on how they can improve profitability.
How Asset Managers are using big data analytics
Asset managers have recognized how big data can improve portfolio management and ultimately improve profitability. Improved data visibility is also helping firms to improve their regulatory compliance to better manage risk and measure performance.
A wave of new regulation has introduced stricter reporting requirements for asset managers and the need for increased transparency. AIFMD, the Dodd-Frank Wall Street Reform Act and EMIR, amongst other directives, are placing new demands on asset managers to demonstrate the robustness of their business models, decision-making process and quality of client care. Traditional data management systems do not allow for a quick or clear overview of operations.
Asset managers need to demonstrate that they fully comprehend and can account for all that goes on within their business. Big data analytics can help businesses meet stricter regulatory requirements by allowing them to better identify non-compliant activity in their firm as well as meeting their obligation to report suspicious transactions.
For instance, firms can use big data analytics to identify unusual activity – a particularly high turnover of clients, or an unexpected number or type of transactions.
Asset management companies operating in the commercial and residential mortgage-backed securities space are some of those now seeing the potential value of big data analytics. Information on property sales, credit ratings data, dealer inventory and bids-wanted- in-competition can be aggregated and cross-referenced to inform the position taken on a specified tranche or security.
Many asset managers are now also mining social media for trading insights. Public sentiment can raise red flags on a company where no other indications of future problems yet exist. A key challenge, of course, is to judge when it is appropriate to act on a recommendation drawn from an unfiltered source. Filtering out what counts as ‘noise’ and assessing how much weight should be applied to such insights requires detailed contextual and market knowledge.
The importance of big data specialists
Turning data into knowledge to drive innovation and harness untapped business potential, requires big data specialists with the right skills-set.
What asset management firms need are people who can ‘bridge the gap’ between the vast amount of data now being generated and the business decision-making.
Financial services firms are now hiring big data and analytics specialists to fill two main, but significantly different roles:
• Big Data Engineers
• Data Scientists / Analytics / Insights