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You must Bank on Insights – Every time, Everywhere

February 17, 2017 - Venkatesh


Data science has been evolving ever since it has come into the picture for businesses and the year 2017 is no different. This evolution is driven by the sheer volume of data being generated and affordable computing power along with maturing algorithms. Business analytics is borrowing analytical advances from pure science and is making strides in generating actionable outcomes.

While earlier analytics meant making sure of the data integrity and managing the various sources of data, now it is more about predictive and prescriptive analytics. Analytics now is characterized by fast data, and the need to gather customer, product, location and user insights, among others, and act upon it in real-time. As current use cases are getting more mature, there are newer use cases emerging to harness the power of data – a certain financial services organization improved the way it used text analytics on incoming customer communication by leveraging algorithms that were originally built for matching DNA sequences. This helped it to prioritize and redirect messages to the right service personnel.

In 2017 most of the analytics investments will be open source – banks in the past have struggled with finding a sizable return on investment for big data & analytics, but now this cost is driven down due to open source technologies such as Hadoop. It is no surprise then that in the Infosys Finacle – Efma Innovation in Retail Banking report 64% of the banks indicated that they were open to investing in open source stacks for application development.

Banks are also realizing the importance of analytics being available to all personnel and not just the top management. Actionable insights driven by analytics not only provide valuable inputs to the human decision-making process, but also provide the right feedback for self-learning machines. There is now an awareness amongst banks on how analytics can be used to teach AI platforms to deal with human interactions as well as detect fraudulent transactions in real time; and this has been the primary driver for making analytics capabilities accessible to all functions within the organization.

As 2017 progresses, banks will move on from descriptive, to prescriptive analytics as algorithms mature within the organization. Banks will empower their employees to harness the power of data, based on which an enterprise analytics strategy will be defined. Furthermore, analytics will be built into the operational framework of the banking organization to make it more agile.

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