Can Big Data Rescue People with Bad Debt Cycles?

Big data adoption is sweeping across the financial sector, gaining widespread acceptance among lending institutions as well. While some banks might call themselves pioneers of this movement, it is the medium and small scale lenders who are really driving this trend. The lending marketplace is abuzz with how big data is helping to decode more risk-appropriate lending profiles. Data analytics is being used to increase the quality of customer service and arrest cases of fraud.

Big Data Not Always Associated with Bigger Businesses

The adoption of big data doesn’t have a uniform pattern. Some of the bigger, more established financial institutes might be collecting more data from conventional points of customer interaction only. However, lending agencies handling smaller loans are more likely to analyze poor credit histories to ascertain the overall chances of recovery and the ability to repay another loan. Here, the idea is simple—take a calculated, manageable risk by offering smaller loans to credit-worthy consumers.

Big Data Can be Seemingly Insignificant Data

The indicators used to evaluate borrower profiles are breaking new ground with regularity. Even social media channels that carry employment information or email accounts with a regular history of making payments or receiving funds are being analyzed. The emphasis is on looking beyond the typical buying and payment history of a consumer. Instead, the focus is on the present and near-future ability of the borrower to repay a small loan. Having a stable job over the past few months can be a stronger argument against the non-payment of a small loan years ago. Similarly, small businesses paying taxes on time and interacting with customers on Facebook, Twitter, or LinkedIn are more likely to be approved for a loan.

Yes, demographic information and credit history are still important. However, these conventional parameters took a severe beating in the aftermath of the recession. People with otherwise good credit histories too were caught in this mess. Therefore, credit histories that show positive signs of complete recovery are worthy of being given another chance. When analyzed further with big data analytics, many such deserving borrowers can be identified.

Will big data overturn debt cycles that cripple households?

The answer lies in how you perceive the question:

Do you look at it as technology coming to the aid of people who are conventionally not credit-worthy? (unlikely)

Or,

Do you perceive it as traditional evaluation parameters being improved by using contemporary technologies? (practical and feasible)

The ideal way to look at this argument is accepting that big data provides a better way to identify consumers who can make monthly payments despite a somewhat-flawed credit history. Big data is not the magical cure that some families might expect it to be.

Similarly, big data cannot guarantee loan payments. It is essentially a relief to a market that is still recovering. It cannot alleviate poverty or the healthcare crisis. However, big data will ensure that a larger part of the credit-deserving population is well served. Much of the information collected as a part of the big data strategy is already in the public domain. If the reputation of a neighborhood or money earned during seasonal employment is used to determine credit reliability, is someone being harmed?