You must bank on insights – every time, everywhere

“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Senior Vice President, Gartner Research, Gartner Symposium/ITXpo, 2011

Why does analytics, which has been around for years, still figure in the list of trends for 2017? It is because data science continues to evolve ferociously, fueled by the availability of ever increasing data on the one hand and affordable computing and maturing algorithms on the other. What’s more, business analytics is also benefitting by borrowing analytical advances from pure science.

Today, banks are anticipating analytics’ third wave. During the first wave, when information resided in a variety of silos, banks’ chief data challenges were of integrity and integration. In the second, the big challenge was to manage Big Data from different sources; here banks were helped by the decrease in storage costs. The third wave 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. New use cases are emerging even as known ones mature. Consider this – one 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.

Heading into 2017, we expect a significant part of analytics investments taking the Open Source route. The latest EFMA Infosys Finacle study on Innovation in Retail Banking found that 66 percent of banks plan to invest big in Big Data and Analytics, which they believe are the most disruptive technologies at present. One problem though, is that most banks have struggled to achieve adequate returns on such investments in the past. But now, Open Source technologies, such as Hadoop, are bringing down the cost of analytics dramatically. So it is not surprising that 64 percent of banks in the EFMA Infosys Finacle survey are considering investing in Open Source stacks on which they can build applications.

We also look forward to analytics being available to all – partners and customers included – and not just banks’ top management. There is a dawning realization among banks that analytics, besides offering valuable input to human beings, must also feed machines and self-learning software. Now banks need to act on that realization by developing analytics models that can teach machines how to deal with complexity, and be more aware of their context.

Today, banks also have a better sense of analytical purpose, an understanding of how and where to use data, and this is leading to the implementation of more analytics models. For instance, historical information and the insights of predictive analytics are now being fed into AI for the purpose of modeling transactions and identifying the fraudulent ones among them, in real-time.

In the age of AI, banks will need to know how analytics can enable machines to learn, predict, and adapt continuously. A good example here is Google Maps, which combines a travel itinerary with traffic information to alert passengers when it is time to leave for the airport. Google page ranking, which lists various pages according to their relevance to the search, is one more.

In addition, we recommend that banks assess their current level of maturity in analytics in order to leverage it fully in their customer and operational journeys going forward. The goal should be to continually progress from a descriptive and diagnostic state – understanding what happened and why – to a state where they can predict what will happen and prescribe the right next steps for the individual user or the organization.

Next, banks must empower everyone – employees, partners, customers, and even machines – with analytics capability. This calls for an enterprise analytics strategy. In 2017, we ask banks to invest in analytics applications that work predictively and prescribe recommendations to organizations and users based on predictions.

And finally, banks must integrate analytics within the operational framework to improve and enable a variety of decisions in customer service, predictive maintenance, inventory management, credit approval etc. In 2017, we also hope to see many smart systems with inbuilt predictive analytics capability.