The Trellance Data Blog

The Recommendation Engine: Using Big Data to Improve Mobile Banking Applications

Posted by Austin Wentzlaff on Sep 11, 2014 8:42:00 AM

“…credit union members are being conditioned every day to expect more personalized information at their fingertips through all aspects of their lives. This information expectation will soon, if not already, be a top strategic priority…” – Paul Ablack, Founder and CEO of OnApproach

Mobile banking is growing at an explosive rate as financial institutions and their members realize its convenience and potential (Figure 1). To keep up with demand, financial institutions are focusing investments on mobile banking rather than opening new physical locations. The amount currently being invested might not be enough, however. As users access mobile banking more frequently and demand more sophisticated applications, mobile application developers, such as FI-Mobile, are being pressured to explore new ways to satisfy their growing needs.

Active US Mobile Banking Users 2008-2013

Figure 1. Active US Mobile Banking Users 2008-2013 Source: TowerGroup

To deliver next level applications that better satisfy users’ needs, mobile application developers should consider utilizing Big Data. Data is growing at an exponential rate (Figure 2) as more people tap into the virtual world for endeavors that were once only available offline. This data, if handled properly, can yield enormous value.

Expected Data Growth 2008-2020

Figure 2.) Expected Data Growth 2008-2020 Source: Oracle, 2012

Financial institutions have a great opportunity to capture that value with the plethora of member data they have such as auto loans, mortgages, deposits, net worth, and most importantly spending information. Together, this data provides valuable insights into member behavior, past, present, and future. Ok, that’s great, but how can mobile applications be optimized with this data?

Advanced Geocoding

Geotracking comes equipped with most smartphones and identifies a mobile users’ physical location, enabling applications to do some pretty cool stuff. Geotracking, which less than a few years ago was viewed as invasive, an infringement on our privacy, and downright creepy, is now demanded and even expected by many groups, such as millennials. We are being conditioned to expect more personalized information delivered instantly to our mobile devices. This is a demand that financial institutions have been laggards at compared to their online retailer counterparts. By using big data, financial institutions will be able optimize mobile applications with geocoding in the following ways:

Next Best Product – Loans are a major source of revenue for financial institutions but with interest rates at historical lows, differentiating one loan from another is difficult. So how do you ensure someone uses your financial institution for their next loan? By being the first image they see when they decided to make their next big purchase. With advanced geocoding and geotracking, financial institutions can send out the most accurate and timely marketing alerts.

Imagine walking onto a car dealership and receiving an alert from your mobile banking app that automatically tells you how much financing you’ve been approved for. The app does three things in this scenario:

  1. Gives the purchaser a more realistic view of what they can afford, which improves their vehicle shopping experience and consequently their life.
  2. Increases the probability of that specific financial institution doing the financing for that vehicle.
  3. Allows the user to fill out the loan application instantly and with less effort. By already having the majority of the information needed, the mobile app could autofill the application for the purchaser.

Targeted Marketing – There are already several applications that use geocoding/geotracking to find your location and offer “deals nearby”, but do you actually care about any of the deals being presented? With data from financial institutions incorporated, a mobile app could send out more intelligent deals by accessing data from credit and debit cards. This data identifies what you really spend your money on and the deals you would like to receive.

For example, if you drive by a McDonalds, Starbucks, Subway, and Chipotle at the same time, your mobile app will intelligently organize the deals in an order personalized to you. If you’re a millennial and you’ve grown up disliking McDonalds and loving Chipotle, your app, with the assistance of big data, will know that and send you alerts for Chipotle and hide the alerts from McDonalds. This is a win-win for mobile banking app developers. It allows them to deliver a next-level application and also provides them with a new source of revenue from advertisers and merchants that use the app to deliver deals.

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Topics: Big Data, Credit Unions, Marketing, Digital