Is Big Data Scaring Banks?

Borrowing money has always been very challenging for startup businesses. However, the trend is changing these days. Preference among startups to seek loans from smaller lending institutions and not banks is emerging as a nationwide trend.

Alternative Lending

Startups, including those that have just begun, are finding it easy to fund their dream projects. Lending is coming rather easily to this segment, including Subprime and Payday loans. The trend is also being referred to as Alternative Lending. This is essentially small business lending that has confused analysts with its unprecedented growth. It seems that this trend is largely fueled by a changing underwriting approach. Conventionally, underwriting has strictly been a standard-driven niche. Credit scores, like FICO scores, have had unchallenged acceptance across the lending sector. Now big data is challenging this typical landscape.

Why this shift?

We believe that this has something to do with the credit crisis that followed the 2008 slowdown. Banks have suffered a lot at the hands of Subprime mortgages. The pessimism continues with banks being apprehensive about anything that doesn’t guarantee repayment. On the other hand, smaller lending institutions have understood the potential of start-ups. They are ready to offer attractive interest rates. Secondly, smaller lending institutions are increasingly using analytical tools to retrieve actionable information from bigger volumes of data.

This big data shift is not as common in the banking sector. Based upon the big data indicators, new underwriting models have surfaced. Start-ups don’t mind their repayment potential being evaluated on the basis of their social media handles or email communication. These individual pieces of data form a part of a widespread big data structure that thrives on using data derived from unconventional resources. This approach is working for startups that are ready to share even more information, as long as the privacy of their business model is not compromised.

Startup Payday Loans Don’t Stretch for Decades

These loans have another advantage over traditional lending instruments like mortgages. An average mortgage might take 15-30 years to be realized. This is too long a period to derive decision driving data that can be used to curate more home loans. However, a bad loan emerging from the startup sector can be analyzed much sooner. Here, the average length of a loan is a few weeks or months. Credit factors that were under evaluated can be quickly identified and used to improve the current underwriting model.

Startup Payday Loan Underwriting isn’t So Demanding

The documentation and repeated screening by the underwriting team can test the consumers’ patience. However, payday loans aren’t as demanding. With little documentation and proofs to establish the borrower’s profile, a payday loan can be executed much quicker. Using analytics to improve this already simplified loan sanctioning process isn’t very difficult.

Payday Reputations are Not so Vulnerable

There is another risk associated with loans to not-so-worthy, newly launched businesses. In case of a default, the bank’s reputation is at risk. Even a few days of bad press can compromise the faith of customers in the bank. However, smaller lending institutions aren’t so sensitive about this issue.

Big Data Costs can be Challenging but Solutions are Emerging

It would be foolish to assume that creating a functional big data infrastructure is without any casualties. The traditional concept of learning as you proceed applies here as well. Using big data doesn’t guarantee the absolute removal of bad loans. Initially, a lender needs to test its big data parameters and their accuracy to observe patterns that can be used for future loan applications. The default rates at the start of the big data journey can be high.

Yes, a big data led underwriting model can be expensive, but another solution has surfaced recently. This is in the form of different business models that use elaborative data. Now lending institutions have the advantage of comparing their organizational structure or lending processes with similar business environments. They can choose a big data model that seems more appropriate for their business environment.