Build vs Buy – a new twist on the age-old debate
So, the role of data in achieving efficiency is unanimously agreed upon by experts such as Gartner. But for many banks, completely reinventing themselves to be a data-centric institution by hiring data scientists and software engineers is completely out of reach and out of step with the trend towards slimming down headcounts. Similarly, with budgets and margins under real pressure even at top-tier investment banks, cost has become a more crucial factor in any decision related to technology. As institutions are constantly looking for ways to eliminate risk from business operations, the unpredictable cost and resource demands of in-house technology projects have contributed to an increase in the levels of outsourcing.
Thankfully, innovative and user-friendly fintech technology exists to perform the same functions more effectively and at a fraction of the cost, giving banks the opportunity to grow market share, protect their niche segment and drive greater profitability in the markets in which they are active.
While some financial upstarts have built technologies that could eventually cut into the relationship-driven work that investment banks are used to doing, others have built data analytics tools to support banks in increasing their relationship-building capabilities through a stronger understanding of trends, market activity and client needs.
Once a bank’s data has been aggregated and normalized, cutting-edge technologies such as complex statistical analysis, machine learning and natural language processing technologies can then be applied, for example, to automatically produce highly personalised research reports. And the end product is far superior from the typical research team’s “buy” or “sell” recommendation, and produced with far greater efficiency.
Gartner’s report also highlights ‘decision intelligence’ as a key area of focus for banks in 2022, predicting that by 2023, more than a third of large organizations will have analysts practicing decision intelligence, including decision modeling. It states: “Decisions can be influenced by a multitude of experiences and biases, but in a world of rapid change, organizations must make better decisions, faster. Decision intelligence improves organizational decision making by modeling decisions through a framework. Fusion teams can manage, evaluate and improve decisions based on learnings and feedback. Integrating data, analytics and AI allows the creation of decision intelligence platforms to support, augment and automate decisions.”
Using such technologies, FICC trading banks can retain a competitive edge in their chosen niche markets by complementing their human expertise with the ability to understand and mine vast quantities of internal and external data at the click of a button. Armed with a comprehensive overview of their clients’ trading activity, they can then make informed decisions and appropriate recommendations.