Big data overdrive hurting bank profits

With more and more data available, making sense of vast amounts of content efficiently can boost profits by at least five percent a year.

Sell-side banks operating in the FICC markets are producing more and more data, and it is widely acknowledged that there is tremendous minefield of value often hidden within this data that can be of great use to an institution’s trading, regulatory, audit and compliance functions.

But for many institutions, aggregating and gauging this data to make sense of key trends accurately remains a significant challenge. So far, it is proving to be timely, costly and hurting banks’ profits.

The 2016 global study released by Qlik and Wall Street Journal (WSJ) surveying financial service companies about the usage of data and analytics revealed 57% found data information too complex to process, analyse and disseminate in a timely fashion. Yet nearly 80% of respondents believed that leveraging insights from data could boost revenues by at least five per cent annually.

As Duncan Ash, a Senior Director of Global Financial Services at Qlik, says: “Analytics is still the most prevalent in head-office functions, and the people in the field that need it the most are getting it the least.” He added that “firms struggle with the volume and complexity of data, and with the basics of communications and data management.”

This potential rewards on offer has led to a rise of specialist technology vendors that scrutinise and standardise data and do the hard work for their clients. One such as example is Mosaic’s MSX platform, which aggregates multiple sources of transaction data into a singular resource. This enables banks to meet regulatory requirements by building a more comprehensive view of client’s trading activity while creating better audit trails for regulators.

Steven Hatzakis, a financial services industry consultant and a registered Commodity Trading Advisor, said in a column on Finance Magnates that as analytic tools have evolved, so have visual dashboards. “These include not just numbers but adding colors or other variables that indicate changes as reporting and related gauges become dynamic. This is a common trait seen within trading platforms in capital markets and it is used in order to make it easier for technical data to be comprehended quickly.”

As Diane Castelino of Mosaic Smart Data says, “The next and most advanced stage is breaking into the field of predictive analytics and machine learning, where the ability to predict future client trading behaviour based on historical patterns sets institutions streets ahead of their peers.

“In what has become a challenging trading environment for all, the real winners in the race to harness and utilise big data will be those institutions that partner with the technology specialists that deliver expertise and innovation on a cost effective, modular basis and educate staff to use the technology effectively.

Diane’s whitepaper on big data and going beyond the hype can be read here.

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