Analytics has come a long way from being just a requirement to being something that is completely non-negotiable. Banks and financial institutions today, set aside a huge chunk of their investment budgets for data analytics and related technologies, on an ongoing basis.
But, getting the most out of these investments is still something that can be questioned? As per a McKinsey report, more than 90 percent of the top 50 banks around the world rely on advanced analytics; however in most of the cases, banks just have a one-off success from these advanced analytic insights, they don’t have something concrete that can help them scale up.
Thus, the fact remains that organizations even though are investing heavily on analytics, their RoI figures aren’t quite impressive, especially for those that follow a traditional approach to technology.
Many financial firms still rely on conventional data sets to gain key market insights.
According to Bala Parthasarathy, CEO & co-founder, MoneyTap, “The most significant data challenge that most traditional banks and financial services companies face is identifying and leveraging the right data. Currently, most BFSI players utilize conventional data points such as credit history to gauge creditworthiness. Relying heavily on the historical data restricts access to credit for a large percentage of creditworthy borrowers from the unbanked and new-to-credit segments.”
To overcome this restricted credit access and the fear to lose potential new customers, many new fintech start-ups are treading the uncharted domain, looking for solutions beyond the traditional data sourcing methods to bring out the best from analytics. “We address this challenge by analyzing traditional and non-traditional datapoints to create more accurate borrower profiles,” Parthasarathy adds.
Recently, this past couple of years, predictive analytics has gained much interest among the firms in the BFSI sector. They’re now bringing in predictive analytics to better their understanding about consumers. Buy studying critical data from these predictive analytics models, they make accurate predictions about future consumer behavior. Predictive analytics, helps these firms identify the right customers to market products, along with the right channels and the right time to market them.
“What these big data models have helped achieve is a whole new way of sending targeted and personalized communications at scale. Effectively, these models help the firms get much more out of their marketing budgets, which is always finite,” says Sethu Chidambaram – Head, Analytics & Cross Sell – Bank Bazaar.
However, many companies, at this stage, are realizing that the success of a predictive analytics model depends on multiple factors, primary around data collection.
“One significant challenge that most banks are grappling with today is how to integrate disparate datasets about their customers which tends to reside in a number of legacy databases and create a unified view of the customer. A decision model that is run on a database that has this unified customer view is usually better by an order of magnitude than say, a decision model which is built only on Savings Account transaction data,” Chidambaram adds.