Shedding Light on Credit Indivisible With Big Alternative Data

Big data has become an immense part of any sort of sophisticated decision-making tool for financial institutions and beyond. The importance of consolidated structured records on customer financial (and not only) behavior is difficult to overestimate as it provides companies an opportunity to make accurate business choices and stay relevant in the market.

However, big data can also be applied to matters of social concern – like life quality improvement by income smoothing. A substantial part of the world’s population remains unbanked and unable to plug into the global financial system due to various reasons. And while traditional financial institutions are looking for reasons to deny someone of access to financial services, tech companies like Smart Token Chain, BanQu and others, are looking for reasons to expand connectivity and open new opportunities for those excluded from the global financial system. Those companies aim to leverage available records into the credible profile to bridge the gap between the financial system and ‘invisible’ households.

As Consumer Financial Protection Bureau (CFPB) Director Richard Cordray noted, A limited credit history can create real barriers for consumers looking to access the credit that is often so essential to meaningful opportunity— to get an education, start a business, or buy a house. Further, some of the most economically vulnerable consumers are more likely to be credit invisible.

In fact, only in the US in 2015 there were 26 million credit invisible consumers, according to the report by CFPB. In addition, CFPB suggests that ~8% of the adult population has credit records that are considered unscorable based on a widely-used credit scoring model. Those records are almost evenly split between the 9.9 million that have an insufficient credit history and the 9.6 million that lack a recent credit history.

Although those people are credit-invisible for traditional institutions, their everyday activity and alternative records represent a meaningful characteristic of the level of sustainability and credibility. Certainly, big data can become a tool for malpractice, but big alternative data can also be a bridge for the underbanked to a higher quality of life.

Where can meaningful alternative data be found?

Mike Mondelli, SVP of TransUnion Alternative Data Services, listed property, tax, deed records, checking and debit account management, payday lending information, address stability and club subscriptions being among the sources for alternative data. As he emphasized, These alternative data sources have proven to accurately score more than 90% of applicants who otherwise would be returned as no-hit or thin-file by traditional models.

Alternative data provides a better lens with which to evaluate all consumers, giving lenders who can score them a competitive advantage, he added.

In recent years, technology companies have been discovering other alternative sources of data to determine creditworthiness, among which are Web search history, phone usage, social media and more.

Web search history is a goldmine of hints about product interests, services, hobbies and much more. Sophisticated analytical algorithms that are able to extract necessary flags out of massive unstructured search data can probably become the next big thing in microloans from e-commerce giants and in banking. So far, Chinese search giant Baidu and Californian big data underwriting company ZestFinance are on the path of using Web search history to extend microloans to China’s underbanked.

As for phone usage, experts believe that smartphones can dramatically reduce the cost of lending because the apps they run generate huge amounts of data—texts, emails, GPS coordinates, social-media posts, retail receipts, and so on—indicating thousands of subtle patterns of behavior that correlate with repayment or default. The WSJ brought up examples of data pieces like the frequency of charging, the number of incoming messages in comparison to outgoing, the number of miles traveled in a given day or how users enter contacts into their phone—the decision to add the last name correlates with creditworthiness—can bear on a decision to extend credit.

Social media has been recognized by Wharton as an important data source for credit scoring back in 2014, although the practice of judging a stranger based on his/her social environment is not really new. One of the core ideas is that who you know matters. Companies like Lenddo, FriendlyScore, ModernLend use non-traditional data to provide credit scoring and verification along with basic financial services. Those companies are creating alternative ways to indicate creditworthiness. The information contained about a person in social networks can provide some sort of verification that the person exists at all and who that person is.

As Wharton professionals suggest, if you’re a good person you must have some good connections with people around you, you must have a certain number of friends, and you must have had this account for a certain period of time. Technology companies focusing on alternative lending and alternative credit scoring can gather so much more information about a person using social media than by looking at their financial data.

For businesses, it's about reducing the costs

At the end of last year, Paul Christensen, a clinical professor of finance at the Kellogg School, shared insights on alternative scoring, mentioning an important positive implication for companies leveraging alternative data to make a credit decision.

For companies, alternative credit rating is about reducing transaction costs, Christensen said. It’s about figuring out how to make profitable loans that are also affordable for most people—not just business owners. He also added that it is a way of addressing the problem of information asymmetry, which he calls one of 'the definitional causes of market failure' and one of the biggest threats to traditional microfinance.

Christensen believes that if alternative credit scoring can help drive down costs and lower interest rates from 30% to 10%, that makes a big difference.