The Alternative Data Revolution in Banking

By Yamini Kona

We have all been at that place where we had to gather a ton of evidence to prove our financial credentials every time we needed a banking service such as a mortgage, auto or personal loan or a credit card. A vast majority of individuals – known as the credit invisibles, the thin file, no-file or no-score customers such as immigrants, youngsters yet to enter credit market, the unbanked and the underbanked – cannot provide much supporting data for various reasons. However, there may be light at the end of the tunnel for them thanks to alternative data. 

What is Alternative Data?

Alternative data can be loosely defined as financial information that isn’t typically collected by credit reporting agencies or usually provided by customers while seeking credit. It covers a wide spectrum of sources ranging from bank account cash flow analysis, credit card usage patterns to non-banking financial facts such as payday loans, rent, utility bills like gas, electricity, payment history of cable TV, cell phone and Wi-Fi bills etc. Public records such as education and employment background, asset ownership, online and social media activities are other notable sources. 

But how do these unconventional sources vouch for an individual’s financial credibility? Paying rent and utility bills on time demonstrates responsible behavior; good academic background shows employment potential. A steady job for a reasonable length of time indicates reliability and regular source of income. Asset ownership such as purchasing a retail item on installments and making timely payments proves respect for financial commitments. While none of them make a strong case individually, aggregation of such innocuous details can present a responsible individual who might otherwise be dismissed as risky and unqualified.

How Can Banks Leverage Alternative Data?

Alternative data enables banks to make informed decisions and helps the credit invisibles to join mainstream financial ecosystem. In fact, using alternative data is no longer just a benevolent option for banks; they are increasingly feeling compelled to do so because their fintech competitors are aggressively leveraging it for business expansion. 

Fintechs have made a head start by using APIs and combining them with AI tools to come up with predictive models to gauge customer creditworthiness and risk potential. Some of them have gone a step further and are experimenting to create alternative credit scoring process. This can be a win-win as they can increase customer base while the underserved can secure better terms as against pricier or no loans under traditional credit system. 

Alternative data can be especially useful in case of immigrants who may have respectable credit history in their home country but are yet to build one in their host country. A global scoring model can quickly convert this segment into prime customers. 

While it takes 30-45 days for FICO score and credit score across all three major credit bureaus to refresh, alternative data is more up-to-date and can be collected anytime, even real time, without compromising credit score due to frequent or hard enquiries. 

While the underserved are desperate for credit inclusion, banks bombard prime customers with credit offers though not all of them may qualify after in depth credit enquiry. Alternative data read together with structured financial data can be an effective equalizer by opening doors for the untapped segment and exposing subtle red flags for prime customers whose credit may prima-facie appear healthy. For example, analyzing the digital footprint of a supposedly solid customer may reveal loan stacking or credit card churning which banks tend to frown upon though they are not illegal. Money transfers made via social media apps such as Facebook messenger or Skype may uncover under the radar money laundering. 

Breaking down the monthly cash flow of a bank account may paint a different picture of the financial capability of a prime customer and banks may reconsider their lending decision after such analysis. Actual disposable personal income of the customer after segregating income and expenses in a given billing cycle may be far lesser to justify the credit limit. Further analysis of expenses into essential and discretionary may even reveal reckless financial behavior. 

The Challenges

For alternative data to be effective, first and foremost, customers should be willing to give permission for third parties to use their data. It is a challenge especially in case of the financially uneducated who are more likely to be apprehensive about how banks may use their information. 

Secondly, alternative data being highly distributed and unstructured adds another layer of complexity. Lenders should have secure API technology, effective aggregation tools, dynamic analytics capability and a transparent scoring methodology in order for alternative data to be reliable. 

Technology related challenges aside, if banks start policing spending behavior patterns and negatively score indiscriminate purchases, it may impact their decision and lead to unnecessary discrimination or rejections. It will be a tightrope walk for banks balancing between facilitating credit inclusion while identifying potential risks. 

Regulatory Aspect 

Five federal US financial regulators gave their blessings through a formal joint statement that lenders can leverage non-traditional financial data to determine the credit worthiness of customers provided they ensure responsible use of such data without violating existing consumer protection and fair lending regulations. The fact that banks, credit unions and other non-financial lenders do not have to worry about compliance with a new legislation to use alternative data clears the path further. 

While big banks are concerned about individual privacy encroachment and reputational risk, regulators believe that using valid alternative data may not be any riskier than financial data used in conventional credit evaluation and underwriting process. They added that forbidding or tightly reining the use of alternative data may turn out counterproductive by hurting the chances of the underserved.     

Alternative data may never replace the formal credit sourcing system but it can be an effective equalizer for the underserved. Alternative data will be the future of financial inclusion and banks that leverage it will have a competitive edge over those relying strictly on traditional credit metrics

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