Venkatesh Vaidyanathan, Vice President, Product Management & Analytics, Infosys Finacle
From a sluggish economy to disruptive digitization, banks have managed to survive and evolve along with the current environment. Most of it has been possible due to progressive banks showing the way and adapting to the environment for a truly digital transformation. Of all the major trends for banking transformation, analytics has made a consistent appearance year after year; and it is no different in 2017. As the hype surrounding big data and analytics has matured, banks are looking at ways to implement technologies to be more effective in terms of return on investment (ROI) and business value generated from analytics.
In the past year, there has been an unmistakable shift towards fast & real-time data and its adroit management. Skillful manipulation of data has become more important in this day and age of rapid digitization and fast-paced consumer lifestyle.
Banks now have clarity in terms of where and how to use data; and this focus in terms of analytics implementation will be the foundation for disruptive technology, such as artificial intelligence (AI), internet-of-things (IoT) etc.
It is evident to banks that as data moves from being descriptive to prescriptive, a competent execution of an enterprise analytics strategy is required to leverage technologies for a complete banking transformation.
Analytics has become pervasive across all functions within an organization and there has been a definite move towards its democratization – i.e. all employees within an organization, partners, and customers get access to insights acquired from analytics. This in turn will allow organizations to serve their end-users better and create unparalleled business value.
2017 and beyond will see big data and analytics interweaved into every level within an organization.
These key factors – consumers, technology, and insights for all – will be the drivers for the next wave for analytics implementation in banks:
Consumer expectations and technology advancements will drive banks’ analytics investments
As a result of the rapid digitization, consumers have become used to a fast-paced life and they expect personalized, contextual products or services instantly. The challenge that banks face at this point is that they can no longer depend on descriptive or diagnostic analytics to dwell on the whys. Now banks have to shift their focus towards the “hows” and use fast and real-time data that can predict the consumer journey and provide its consumers with relevant products or services.
With the latest technology in the form of AI or smart devices banks can look to provide personalized consumer experiences based on context and life-events to please even the ficklest consumers.
For example, imagine a banking application that understands consumers, their expenditure patterns, saving habits, and social preferences; now imagine this app will also provide a comparison between similar demographics and provide financial advice based on this comparison. This kind of tailored customer experience is now made possible, built on the foundation of analytics and will provide banks with an unbeatable competitive advantage.
A simple use-case for personalized customer experience may be in the form of banking apps that most millennials access from their mobile phone to carry out payments and other transactions. When a user logs in, analytics helps the banking application understand what the consumer is most likely to do, and creates a user experience on-the-fly that is optimal for this consumer at this point in time. This would be based on machine learning algorithms powered by predictive modeling, understanding the consumer’s financial habits, as well as social preferences.
Themes based on personas can be another use case for analytics implementation for customer experience.
Analytics helps with understanding the life events of consumers. This is used to determine the optimal user experiences for them at a certain point in time.
For example, customers with college going children would see themes based on savings for higher education, whereas getting married would see themes related to travel and vacations.
Technology will be powered by analytics
It has started to dawn on banks that while analytics offers valuable inputs to humans for business decisions, there is a certain section of technology that benefits from it as well. And insights powered by data and improvement in automation have made sophisticated AI technology easily accessible – more so for institutions that weren’t able to implement it initially due to lack of internal resources and dearth of R&D skills. With analytics and process automation as the driving force behind it, the modern AI platform has the ability to transform traditional banking institutions for the digital era. Progressive banks are already looking to effectively leverage data and advanced analytics modeling that will put them in a position to capitalize on newer technologies such as machine learning and automation.
For example, Wealthfront utilizes AI capabilities to understand how consumers are investing or spending, and then provides pertinent financial advice to them. Sentient Technologies continuously uses AI powered by insights to create investment strategies for users. Banks such as RBS have implemented AI in the area of customer service in the form of Luvo. It is a smart assistant that supports service agents who are answering customer queries. Luvo can search at higher speeds through a database to provide faster answers; it can also continually learn over time from gathered data to be more efficient with each interaction.
Banks also have to be aware of the fact that it is not only the internal processes that need a facelift in the digital era.
With the number of smart devices flooding the market, there will be an increased need for newer model-driven analytics implementations.
As the number of consumers with connected devices increased, banks will get access to more data than ever. This, in addition to the payments data arising from a decided push towards cashless economies, will make it an imperative for banks to implement a unified analytics strategy across all functions.
Insights for all – every time, everywhere
Till recently the insights derived from data were available to only the top management. But with changing customer behavior and technology that is driven by data, it is important that data is made available to all for optimizing internal as well as external processes. If banks want to cultivate a culture of innovation with analytics, it is important that they implement an enterprise analytics strategy. This in turn means that everyone should be provided with significant data management capabilities, and a talent base within the organization that will assist in deriving insights from data. For example, a few bank leaders came together to find solutions for difficult policy issues through a crow-sourced effort to use new data sets. The Bank of England has hired a Chief Data Officer, and has established guidelines around the instatement of an advanced analytics group and a bank-wide data community within the organization. It has also created a data lab to understand how these various streams of data can be combined to form actionable insights.
And it is not only internal processes that can be improved with analytics. A case can be made for making analytics capabilities available for customers too. US Bank’s Payments division had created an application, InfoApp, that allowed their small business customers to analyze their expenditures and other corporate payments. It provided the small business owners with a consolidated view of their finances, as a result of which this app was an instant hit.
This just goes to show, how democratization of analytics is just another avenue for providing a differentiated and personalized customer experience; and this in turn allows banks to stay relevant in today’s digital world.
Even though the consumers, technology, and insights for all are the key factors for driving success with analytics, banks will need to understand that the ultimate goal of any analytics implementation is to simplify the consumer’s life. To do this efficiently, all these initiatives will have to apply analytics models to create seamless connections between various solutions. A good example of this is Uber that has managed to integrate data obtained from location apps, real-time pricing analytics, and payment interfaces to provide a frictionless transportation experience.
2017 will be the year when banks will look towards gaining the competitive edge through investments in big data and analytics. While previously the cost of investment was one of the barriers for enterprise wide analytics implementation, it no longer is the case with open source technologies, such as Hadoop. As these open source stacks bring down the cost of investment, banks will start to see effective ROI with these investments in analytics. But it does not end with investments in analytics alone; banks will have to cultivate a robust, analytics driven culture within their organizations and foster a bent of mind that will be insight driven. The success of these implementations will of course depend on the competent execution of technology, employee empowerment, and democratization.