3 Ways Big Data Is Making Big Changes in Consumer Lending

consumer lendingAmid the era of digital transformation, credit unions and financial institutions are finding profit-driving applications for the treasure troves of data that have been at their disposal for several decades. This data is providing a new perspective in consumer behavior, preventing disasters and thefts, and unlocking the secrets of money movement. And now, big data is poised to make big changes to consumer lending. Let's take a closer look at three ways it's already happening.

Utilizing Consumer-Permissioned Data for a More Holistic View of Credit Worthiness

The traditional methods used to determine creditworthiness can make it very difficult — if not impossible — for the estimated 91.5 million consumers in the U.S. with thin credit files or no established credit to find lending products. This even applies to credit unions that have traditionally used different, potentially less stringent, lending approval criteria for their members.

One analytics trend poised to transform the lending industry is the use of consumer-permissioned data to influence credit decisions. Consumer-permissioned data is defined as the account-level and transactional information a consumer authorizes a business to access on their behalf — often for the application of extending credit. Examples of consumer-permissioned data include:

  • Investment accounts
  • Online banking and bank transactions
  • Utility bill payments
  • Mobile phone payments
  • Streaming service payments

And this move could be monumental. According to Equifax, an estimated 5.5 million consumers in the U.S. may matriculate from a subprime credit score or an unscorable status into prime or near-prime scores when financial institutions consider this alternative data.

Banking On the Unbanked with Mobile Data

According to a 2019 FDIC survey, a staggering 7.1 million people, or 5.4% of U.S. households, were "unbanked," which refers to households without a savings or checking account at a bank or credit union. These individuals are likely to use mobile devices and apps to exchange money, such as PayPal, Zelle, Venmo, Cash App, and the like. While there may not currently be an established infrastructure to support these consumers, microlending may check the box.

For example, in Sub-Saharan Africa, the payment platform M-Pesa offers consumers access to a digital wallet for Safaricom smartphones. Users can borrow funds, transfer money, and even purchase products from within the service. And a similar alternative lending business model can be used stateside to reach the large swath of the unbaked population.

Moving forward, lenders interested in reaching unbanked populations may forge partnerships with firms that own the mobile data. This new insight can be used to influence lending decisions for those with no credit, thin credit, or inaccurate data in traditional bureau files.

Unlocking Artificial Intelligence & Machine Learning to Influence Lending Decisions

In the days of yore, lenders required written applications and reviewed volumes of physical documentation to determine creditworthiness. This process was subject to human error and lengthy. Today, however, the lending process has become more digital thanks to predictive power, artificial intelligence (AI), and machine learning (ML). And all of these innovations are being actively employed by fintechs to provide lending solutions to underserved and underbanked populations.

According to estimates by the Boston Consulting Group, more than $160 billion in digital loans were issued by fintechs from 2017 through 2020. This number is projected to exceed $220 billion. By leveraging treasure troves of data — such as telco data, pay stubs, digital tax documents, etc. — and advanced algorithms, lenders are better suited to confidently evaluate risk.

Get Smarter with Your Credit Score Decisions

With increasing competition for the hearts, minds, and wallets of your existing and potential members, it's imperative for credit unions to work smarter and more efficiently. This means using detailed analytics to help identify loan opportunities for members based on your lending criteria and their credit profile.

To identify loan opportunities for your members based on their credit profile and your lending criteria, FLEX and SavvyMoney® have partnered to deliver a smarter credit score solution for your online and mobile platforms which will offer loan recapture programs and use detailed analytics to help increase cross-selling opportunities and improve member engagement.

Learn more about ways your credit union can use detailed analytics and loan recapture programs to help identify loan opportunities for your members, through our eBook. Download it here!

FLEX & SavvyMoney Integration eGuide

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