Let me begin with a disclaimer. I believe that what I’m about to tell you is true, but have no way of proving it. Apparently, a grocery superstore in the U.K. has become so adept at mining transaction data into insight that it can predict which couple is heading for divorce, based on what and how they’re buying!
That’s the power of predictive analytics, a science, which has made deep inroads into several industries and is now doing the same in banking.
So, what’s new? Haven’t banks – the producers and consumers of more data than most other industries, been mining it since ages?
Yes, but data mining and analytics aren’t the same thing. To cut a long story short, the latest analytics solutions have the ability to process petabytes of data into predictive insights, in near real time. This means that in theory, banks can derive key insights into the outcome of an action, even as they execute it. In practical terms, this could mean the difference between stopping fraud in mid-transaction or raising the alarm after the deed is done.
That being said, fraud prevention and risk management aren’t predictive analytics’ only charms. For some time now, banks have leveraged predictive analytics to acquire and retain customers, manage campaigns and improve cross sales. Now they have the opportunity to refine customer understanding to a different level, with the help of a type of analytics, which improves customer centricity. This is a bigger deal than it sounds, and I’ll explain why. In a recent podcast, a senior executive in Accenture’s financial services practice said that under the current banking model, only 10 percent of customers generate more than 50 percent of profitable revenue, in a significant departure from the 80-20 rule. With the profit potential of their top customers nearly maxed out, banks have to necessarily turn to the other 90 percent for further growth. Customer centricity-oriented analytics can help banks understand these customers better and discover new models of business to swell the ranks of profitable customers. Indeed, experts believe that banks, which exploit the power of real time predictive analytics to improve user experience (among other things), will be the high performers of the future.
More good news awaits such organizations. Thanks to the Cloud, provisioning the massive compute power required for predictive analytics is no longer an issue. In fact, predictive analytics on the Cloud is all win-win – not only is it fast, scalable and economical, but the Cloud is where several Big Data repositories happen to reside. Seems like even the sky is no limit for predictive analytics!