Social Data: The Missing Link for Thin-File Credit Candidates
In the United States alone, there are an estimated 45 million1 thin or no file credit candidates. As we’ve discussed in a previous post (INCLUDE LINK), the subprime credit scoring of these individuals in many cases has nothing to do with their creditworthiness. These individuals may pay their rent and utility bills on time, save money, and otherwise be highly responsible citizens, but if they pay for purchases mostly in cash, have no credit history, or are only recently entering the credit market (as is the case for many millennials), their credit score will remain low nonetheless.
In emerging economies, this challenge is even more widespread, with approximately 2 billion2 individuals entirely unbanked.
Why does this matter?
Because without access to formal banking through a trustworthy institution, people transact in ways that are risky and time consuming. Imagine storing your cash savings beneath your mattress, approaching a known criminal to help fund your business, or waiting in line for hours on end to pay for your child’s tuition in cash. In short, accounts provide a safe way for people to take control of their money. But for the billions of people worldwide who are unbanked, or have thin or no credit files, gaining access to such accounts, or credit, will remain essentially impossible unless alternative methods of scoring become more readily available.
Enter Social Data
Particularly for the younger generations, what is lacking in credit history can be found in their rich online lives; in their social data. Social media accounts such as Instagram, LinkedIn, Facebook, and Snapchat offer an immense amount of information on credit-invisible individuals that has the potential to lead to greater accuracy in assessing creditworthiness.
Hence why many third-world countries are beginning to mine social data and utilize this information in developing greater credit scoring abilities. These efforts have been notably criticized3, largely because of their lack of consent. In today’s marketplace, in which consumers are highly concerned about their right to privacy, any credit scoring infrastructure that utilizes social data must be built on the solid foundation of trust. Allow us to state clearly here that the only ethical way to utilize this data is by first obtaining the subject’s volunteer consent of use. Once consent is clearly and freely given, the immense power of social data in increasing the accuracy of creditworthiness can fully be unleashed.
How accurate is social data?
A study4 conducted by the National University of Singapore established that there is an impressive tie between the sharing of social data and an increased accuracy of credit assessment.
In the study, anonymized backend data was received from a company that offers microfinance loans. The lender encouraged borrowers to share their social networking accounts as a method to increase access to credit. In the study, the loan repayment records were compared to the borrower’s Facebook profile and full history of interactions on the platform.
The first group of social data that was considered consisted of the borrower’s stated interests (page “likes”), and the Facebook groups to which they belonged. Based on this information alone, it was found that the prediction rate of creditworthiness was improved by a staggering 18%! From there, the borrower’s connections to other borrowers online was used to regressively analyze a connection to creditworthiness. The use of weighted social ties alone predicted repayment for more than 60% of borrowers!
The Trust Science Early Adopter Program (EAP)
Up until now, financial institutions in the west have not had access to consent-based alternative data scoring assistance, including the use of social data. Enter Trust Science. The Trust Science EAP is an unprecedented program that uses our patented artificial intelligence and machine learning technology, combined with traditional data (FICO score, credit history), alternative data (employment, public records), and volunteer, consent-based mobile and social online data to provide a rich and dynamic profile on the creditworthiness of thin/no file lending customers.
Companies belonging to the Trust Science EAP have the ability to shape the future of credit scoring, and influence the accuracy and ethics of using social data to assess creditworthiness. To learn more about the program, and how to qualify, speak to a Trust Science representative today at 1 (866) 687-8789, or email us at email@example.com.
- Quovo, “Enriching credit scores with alternative data”, https://www.quovo.com/finserv-blog/insights/enriching-credit-scoring-with-alternative-data/
- World Economic Forum, “2 billion people worldwide are unbanked- here’s how to change this”, https://www.weforum.org/agenda/2016/05/2-billion-people-worldwide-are-unbanked-heres-how-to-change-this
- Big Think, “How China’s ‘social credit score’ will punish and reward citizens”, http://bigthink.com/stephen-johnson/a-look-at-chinas-orwellian-plan-to-give-every-citizen-a-social-credit-score
- Semantic Scholar, “Social Media-Driven Credit Scoring: The Predictive Value of Social Structures”, https://pdfs.semanticscholar.org/2f1c/e382e2be6ff6c70e2a43e0197d89426992c9.pdf