ePrivacy and GPDR Cookie Consent by Cookie Consent

4 Myths about Alternative Data Busted

January 22, 2020

Lending to the unbanked segment, individuals and households with low income and small firms has been a constant challenge. How do you extend loans to borderline applications? What data do you use to profitably extend credit to thin file, no file, unbanked and underbanked customers?  How do you turn consumers misclassified by traditional credit assessments as ineligible for loans into profitable customers? Predictive insights into these questions is crucial in order to improve lending decisions. However, traditional credit scores fail to provide a comprehensive picture of an individual’s creditworthiness. This negatively impacts credit decisions, including prescreen and approval process, credit line, and account management.

The way out? Alternative credit scoring based on privacy consented and anonymous smartphone metadata. This approach helps gain deeper behavioral insight and provides unmatched speed and predictability for better lending decisions, potentially helping raise the credit scores of people with lower-income and also customers who are typically locked out of traditional credit lines. This in turn helps turn them into valuable customers and grow credit approval rates overall. However, despite the obvious numerous benefits of alternative data application to credit scoring, misconceptions concerning the approach prevail. Read on to know more about the top four alternative credit scoring myths and why they are not true.

Alternative credit data is unreliable  

Since traditional credit data has been the quintessential go-to for decades, it has reigned supreme as the reliable source for lenders. However, according to credit information provider Experian, market practices have slowly evolved, with 65% of lenders saying they already use some information beyond the traditional credit report to make better lending decisions. Alternative credit data points which assist in credit scoring can help lenders get a sense of the loan seeker’s financial reliability. Whether combined with traditional credit bureau scores or used alone, alternative credit scores provide a more holistic picture of the loan seeker’s credit worthiness. Moreover, rapid advances in artificial intelligence (AI) help convert alternative metadata into reliable credit scores. With alternative credit data, creditors are able to identify new lending opportunities, improving overall portfolio performance.

Alternative consumer data is only used for consumer financing

Alternative credit data goes far beyond consumer financing. Advances in fintech lending and the use of big data have changed the way small businesses secure financing. After the 2008 financial crisis, credit scores of small businesses, who were left behind by traditional financial institutions, took a significant hit. That’s when fintech lenders became an increasingly popular financing resource for small shops in need of a loan.  Fintech lenders use a variety of alternative data sources including digital data, PoS information, and surrogate data including utility and bill payments so that previously underserved consumers now have access to credit.

Alternative credit data violates customer privacy

For better credit decision making, every bit of information counts – and applicants are more than willing to share this data. An Experian survey found that 70% of consumers are willing to provide additional financial information to a lender if it would lead to fairer credit decisions. With increasing data privacy regulation and protection, permissioned smartphone metadata allows consumers to choose to share their data for specific credit scoring purposes without worrying about data breach and violation of their privacy.

Alternative credit data means only social media data

Though a number of lenders are exploring the use of social media scoring in their credit underwriting process, it is not the only data available for credit scoring. For instance, leading Fintech company Credolab leverages bank-grade algorithm to analyses nearly 10million features from opt-in and permissioned smartphone metadata to find the most predictive behavioral patterns before converting them into credit scores , in a matter of seconds. This enables companies to assess the risk level, and the repayment behavior and creditworthiness of their applicants with greater accuracy before granting them credit or loans.

The future of alternative data

Not too long ago, most traditional lenders had not idea of the possibility of leveraging data from rental history, utility payments history, bank accounts or social media to make credit decisions. As a result, millions lacked access to credit. According to a 2018 World Bank report on the use of financial services, globally, 1.7 billion adults remain unbanked, yet two-thirds of them own a mobile phone that could help them access financial services. The report further states that that alternative data could help provide formal financial services to up to 100 million more adults globally.  Alternative data is definitely finding its place alongside traditional qualifiers for credit. It is a start to reducing credit invisibility and will increase credit approval traction and lead to a massive shake up of the credit industry and its time-tested processes in the coming years.