Go beyond traditional data points to get better RoI from analytics
Posted on 14 June 2019
According to McKinsey, more than 90 percent of the top 50 banks around the world are using advanced analytics; however most are having one-off successes but can’t scale up. Despite the increasing investment, the number of firms that are seeing greater RoI from analytics isn’t quite impressive, especially those firms that have a traditional approach to technology. Many financial services firms still rely on conventional data sets. “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,” says Bala Parthasarathy, CEO & co-founder, MoneyTap.
To this end, many new-age players and fintechs are going beyond the traditional methods of data sourcing to effectively leverage analytics. “We address this challenge by analysing traditional and non-traditional datapoints to create more accurate borrower profiles,” Parathasarathy says. Fintechs thus seem to have been able to realize better RoI from this technology. “A good number of Fintech companies these days are leveraging data analytics to achieve truly phenomenal results. Undertaking analytics projects have proven to enhance ROI and scale up operations by reaching a larger segment of potentially eligible consumers,” confirms Aditya Kumar, Founder & CEO of Qbera.com.
The goal of predictive analytics is to help identify the right customers to market products, the right channels to market them, and the right time to market them. Hence, focusing on past patterns and previous approaches to analytics may turn out to be detrimental when it comes to predictive analytics, expects point out.