Leveraging the Power of “Alternative Data”

610x409 Alternative data

By Sujatha Venkatramanan, Consulting Director, Decision Analytics

For some time now, there’s been a lot of buzz around using ‘alternative data’ to assess creditworthiness of loan or credit card applicants. Whilst lenders are excitedly exploring cues from FaceBook, Twitter or Google data, their usage has primarily been in finer targeting of marketing offers.

In Asia, China has been at the forefront of this development, with peer-to-peer lenders (P2P), large e-commerce players, such as Alibaba and Webank, from the instant messaging provider, Tencent, all exploring such non –traditional data for underwriting loans. Who’s next, then? India? It has 34% internet penetration (462million users), yet online sales account for less than 1% of total retail sales in the country.

Indonesia, with over 78 million internet users, 69 million active Facebook accounts and its reputation of being the Twitter capital of the world, could be the market where this development plays out next.  Add to that, the fact that close to 30% of online sales come through social media sites and we have a potent mix of all the enablers for a small revolution in lending, which genuinely leverages “alternative data”.

A key concern for most traditional lenders has been to understand just how reliable and stable such attributes might be, when used in credit scoring models. Criteria used to accept / deny credit are subject to regulatory (and often, public) scrutiny. So many lenders tread cautiously in this new space.

Look around and you will see that alternate data abound, well beyond Facebook and Twitter – ranging from hard-to-access rental data to easier-to-access telecom and e-commerce data.

Telecom data has been invaluable in creating the first, albeit small, credit footprint for millions – mostly students, first-jobbers and immigrants, all new to the formal financial system. In markets as diverse as Belgium, the U.S., Peru and Africa, “positive” telecom data (a history of paying on time) serve to improve credit scores.  In contrast, rental bureaux exist in just a few countries, notably S. Africa and UK, while rental data is available for parts of the U.S.

The real treasure trove of insights, however, comes from e-commerce data.  Many facets of customer behaviour are revealed from transaction data such as the product categories most often bought, the frequency and amounts of purchases, the time of day or day of the week when purchased, the devices used –whether mobile, tablet, or desktop and the social channels engaged. All these and more are proving to be enlightening not just about lifestyle choices about also about credit behaviour.

Customers with higher levels of digital engagement often use multiple devices, switching several times during the day from mobile to laptop to tablet and back, even for the same purchase.  So, a customer may start browsing on a tablet, read reviews or investigate comparison sites on a desktop and finally buy through a mobile app.

Banks and non-bank lenders which allow customers to seamlessly transition from one device to another are already a step ahead of the crowd.  If they can now access the rich transaction data available with e-commerce portals, they can make very solid assessments for both retail consumers and the small businesses that supply goods / services to the portal.

Billing and returns data, as well as reviews, provide deep insights into the sales cash flows of portal vendors, allowing banks to lend to them faster and more confidently. Similarly, consumer patterns of purchase allow banks to make a better judgement of their credit worthiness, their life style and even their likelihood to defraud!

Attractive as the prospect seems, something is holding back lenders from jumping onto the bandwagon. And that is the very real apprehension of how to deal with the deluge of “big data”. Most banks are not yet ready to access, store and statistically manipulate vast reams of information from multiple sources, much less ensure that it is used to make lending decisions in real time.

For that, they need a whole new world of infrastructure, with distributed storage such as Hadoop to enable high speed, parallel processing plus a team of  analysts with new machine learning skills. No mean task, it is, to benefit from the potential of big data!

M-commerce and e-commerce firms are nimbler, without the legacy systems that hold back the banks.  And so it is with Telcos and e-commerce players, not all of whom wish to enter the highly-regulated, capital intensive world of finance.

As such, in the coming months and years, we can expect to see banks partnering with e-commerce and Telco players in a big way and not just for marketing!

Join Experian’s Consulting Director, Sujatha Venkatramanan with more than 30 years of regional banking experience as she discusses more on this topic with you. Register today at http://ow.ly/4nbEX6 for Engage Asia 2016 on 20th May Shangri-La Hotel Jakarta!

More resources from Experian:

  • Clicks to Coin | Clicking. Browsing. Searching. Chatting. Online, mobile, offline. Read how c-level executives are handling the deluge of data stemming from the enormous volumes of data in Asia.
  • The Economics of Fraud | Do you know what the fraud hotspots in Asia Pacific are? Read how you can leverage these results and more to help see where your company stands when it comes to fraud prevention measures.
  • Digital Consumer View 2015 | Compiled from the responses of more than 1,200 digital consumers across 6 markets in Asia, this is a comprehensive study on channel and device behaviour as well as content preferences of the Asian digital consumer.