Investment banking institutions have been much slower to embrace data science techniques when compared to their retail counterparts who regularly use analytics to evaluate churn, make product recommendations, and more. Legacy systems and legacy thinking have long held investment banks back when it comes to analyzing and extracting deeper insights from the wealth of data they collect. These systems have limited the speed and sophistication of recommendations to customers to manage risk and even build new, valuable data products. New competitors who are leveraging data science are changing this landscape.
The continuous adoption of data science in order to collect and analyze large sums of data continues to revolutionize organizations across all industries – and that includes the investment banking sector.
Investment Banks, like many other industries, face the risk of disruption by upstart companies, such as LearnVest and Mint, leveraging data to build new products and services. In order to compete with these upstarts, these banks will need to turn their insight into production analytics to mitigate the risk of disruption.
Big Data Entering Investment Banking
As we move into the future, there’s no doubt big data will continue to transform and shape the financial services industry as a whole. Here’s a look at how data science is creating huge changes in the investment banking sector.
Data Allows more Accurate and Timely Decisions
The success of an investment bank depends on optimizing various business decisions in a day which are based on data that changes quickly with every activity. For this reason, many decisions must be codified and executed automatically.
From risk management to capital allocation, improving the quality and flexibility of these decisions can help drive profitability. For example, one major European investment bank estimated it could save upwards of $1 billion per year in capital allocation costs if it were able to have a more real-time view of capital needs across lines of business.
Big data systems allow investment banks the ability to enable real-time decision making. The volumes and pace of trading activities have increased exponentially over the past years, and the data that comes with these activities is arriving through a range of different channels, often in an unstructured form.
Investment banks are increasingly turning towards data science solutions to handle these volumes of data and turn them into more accurate and timely recommendations for both business leaders and their customers.
The Challenge: The Big Data Talent Shortage
There is a data science talent shortage across many industries, and banking is no different. The unique and specific qualifications needed to be a data scientist at a bank makes it even more difficult to find talent. The banking industry is frantically trying to recruit data scientists as benefits of using big data continue to soar.
While the search continues to fill this skills gap, some banks are training internally to fill these positions. Companies such as Wells Fargo have stated that they have no plans to hire data scientists but rather to train employees on uses of predictive analytics. The company plans to seek tools that give employees easy-to-use data visualization software along with automated analytics. This make vs. buy strategy aims to ease employees into statistics and analytics with user-friendly tools with the hope that eventually they use this software in their everyday job tasks.
As data science continues to revolutionize traditional business models, investment banks, too, will need to invest heavily in modernizing their processes with data-driven solutions. This will require skilled personnel, or data scientists, who possess the right expertise to analyze and extract valuable insights from all of the data that these banks collect.
Dr. Ken Sanford is the US lead Analytics Architect for Dataiku. He is a reformed academic economist who likes to empower customers to solve problems with data. In addition, Dr. Sanford teaches courses in Applied Forecasting, Stress testing and Big Data Tools for Economists at Boston College. He has a Ph.D. in Economics from the University of Kentucky in Lexington and his work on price optimization has been published in peer-reviewed journals.