On Deck is not yet another run-of-the-mill business money lender. This is a New York based company that uses big data analysis to analyze the digital footprint of companies that it wants to lend money to. Using data from platforms such as Google My Business, FourSquare and public records besides business transactions, the company measures the risk associated with lending money to a particular customer. Got too many negative reviews about your small business on Yelp?
On Deck is not yet another run-of-the-mill business money lender. This is a New York based company that uses big data analysis to analyze the digital footprint of companies that it wants to lend money to. Using data from platforms such as Google My Business, FourSquare and public records besides business transactions, the company measures the risk associated with lending money to a particular customer. Got too many negative reviews about your small business on Yelp? Chances are that you may not get the loan after all.
On Deck is part of a small but actively emerging group of businesses that make use of big data analytics to assess risks. A recent research study conducted by Infosys Labs found that the accuracy of risk assessment using traditional parameters like demographic data and credit history increases when this data is augmented with social data – namely, their social behavior and actions. The study, for instance, found that the chance of a prospective customer defaulting increased when they were ‘friends’ with people who have defaulted.
Interestingly, the United States, which is home to hundreds of big data start-ups, is not at the forefront of using big data for risk assessment. A number of internet-based financial institutions like Aliloan and P2P lending have mushroomed in China that are giving the banks in the country a run for their money. These institutions analyze the customers’ search terms, their location information, ecommerce spending trends along with other public information to make an accurate assessment of risks. The growth of these institutions has forced the Chinese banks too to adopt big data technology in a big way to prepare risk assessment reports.
While there is no argument against the use of big data to make accurate assessment of risks, not everyone may be convinced about the efficacy of augmenting this using public platforms and social data. Studies, as those published by Infosys, present a true picture of how things stand today. However, if the use of social data to ascertain risks become mainstream, it is not difficult to fathom how this may be gamed by customers desperate for a loan. Similarly, it is extremely easy to influence the reviews on public platforms like FourSquare and My Business. Using that as a strong parameter to make risk assessments may not work forever.
Also, not all lending risks may be assessed through big data analysis of demographic data. For instance, there are institutions like LowVARates that specialize in home loans to veterans and the military fraternity. Given the higher incidence of physical and stress related trauma among this demography, a conventional big data analytics may throw up higher risks. However, the US government has been actively pushing towards making it easier for the veterans to be able to take home loans.
So what’s the verdict here? Big data is indeed here to stay as far as risk assessment goes. However, the source of this data needs to be chosen quite judiciously. Public records, including social data and internet transactions can be easily manipulated and using this as a source to analyze risks may not be a viable model for the future. However, when it comes to using this data to make demographic analysis and identifying transaction patterns, there is no question of how big data can be useful. What are your views on this?