Role of Advanced Analytics in BFSI
Like every other industry, the banking and financial services industry too is feeling the burn of disruption due to the unprecedented rate of technological innovation that has precipitated what can only be described as a revolution in the world of banking. So much so, that with customers looking far and wide to best satisfy their financial needs, the very concept of “needing” a bank is being questioned at a fundamental level.
Armed with advanced and sophisticated data analytics software making it possible to collect large volumes of customer data and convert them into valuable insights to be used to create business opportunities to design powerful and personalised customer experiences that add value to their daily life, the booming FinTech sector is all set to topple the way the banking industry has so far functioned. In addition to personalisation, data analytics is also uniquely positioned to power machine learning and leverage its value to offer value-added services. On-the-go customers are no longer beholden to banks, with FinTech companies outdoing themselves to provide smart, user-friendly solutions to their problems.
These avalanche of impending change is enabled and lent momentum by regulators, and an increasing acceptance of open banking regulations among economies around the world. There is a growing sentiment in favour of compelling large banking groups to use open Application Programming Interfaces (APIs) that give third parties easier access to customer data and the ability to develop services that plug directly into the banks’ systems. While this might provide value to the customer in their day-to-day use of banking services, it loosens the banks control because they no longer have exclusive access to customer information.
In such a turbulent environment, a bank’s ability to use data to its own advantage, and create personalised products that will keep customers satisfied with the services being offered in-house, instead of looking around to mix and match, and cherry-pick from a metaphoric buffet of service providers is what will ensure its survival in the coming decade. Given that the banking industry has traditionally relied on cross-selling of higher margin products to customers due to the advantage of an existing, often long relationship, banks are now faced with the challenge of maintaining market share even as their business model is now at grave risk. According to a 2017 research by Marketforce Business Media and Earnix, 73 percent bankers expect open banking to diminish the advantage of existing customer relationships and make it difficult for banks to cross-sell, while 75 percent felt banks would have to majorly overhaul pricing and value models in the next five years to retain customers.
It is evident that advanced analytics and machine learning hold the key to wooing customers in the era of hyper competition. Fortunately, traditional banks, at the moment, have a clear head start. According to a 2016 report by Accenture, 70 percent customers say they would not trust a third party as much as their bank to handle sensitive information.
But this advantage will not last forever. Sooner or later, competitors will be able to convince consumers that not only can they be trusted to keep data secure, but will deliver superior value by using it. Banks need to move fast to not just own data, but to use it intelligently to create products that are entrenched in their customers’ lives. With the massive volume of data banks currently own, they can create technologies that not only help customers manage their finances, but their whole lives more effectively. 83 percent of banking executives surveyed by Marketforce Business Media and Earnix expect banks to provide data analytics-based services to customers to manage their lives more efficiently in the next five years.
At the same time, machine learning too is gaining momentum. By simulating human thought processes, smart machines are able to mine customer data though videos, photos and social media content for qualitative data about customers. Combining this kind of qualitative data with the quantitative data provided by analytics can open up unimaginable opportunities for banks to understand customers and interpret and anticipate their needs before their competitors. 80 percent of Marketforce Business Media and Earnix’s respondents expected machine learning to interpret unstructured data for banking purposes in the next five years, while 52 percent believed it was going to happen within two years!
Advanced analytics is clearly the future of banking, but the success of banking depends on how quickly and how intelligently the industry can put it to use. A 2014 McKinsey report says that when businesses invested heavily to reach full analytical maturity, they enjoyed significant returns 90 percent of the time, while those at medium-level maturity only benefitted 30 percent of the time.