In much the same way the internet transformed the banking industry through the introduction of online banking, big data stands to revolutionize how loans are handled. Think about the headaches you have to go through when getting a loan for a car, home, or new business. There’s bound to be piles of paperwork you need to fill out, some of which may require a law degree to fully understand. That’s not to mention the discussions you’ll need to have with loan officers and the many visits you’ll need to pay to the bank. Much of that is changing now as big data gets used for approving people for loans. It’s a new way to evaluate risk that helps give people with no credit the chance to get those loans that can change their lives.
When speaking of online lending using big data, it’s important to point out that one of the major benefits not only means getting approved for a loan when you otherwise might not. With big data, loans can get approved much more quickly, bypassing the hours usually needed in the more traditional way. It’s a quick process that in some instances can actually lead to lower interest rates when compared to market averages. Needless to say, it’s an appealing option that many will be drawn to, especially younger generations used to conducting their business in digital form.
Banks and lending institutions are not charities, though. When dealing with loans, the name of the game is risk. These organizations want to evaluate how likely you are to repay the money they give you, plus interest. That’s where big data plays its biggest role. Whereas most banks and credit unions will look at your credit score to determine how risky lending money to someone is, many startup lending companies use different — and some would say unorthodox — methods. With a combination of big data and machine learning capabilities, they can figure out the likelihood you’ll repay a loan through some unlikely factors that you may not have thought of.
Have you ever given much thought to the time of day you ask for a loan? What about how many emails you send out every day? Did you know that your Facebook friends could determine how likely you are to repay a loan? Some of these items may seem unimportant, but through big data analytics, experts have found that they provide signs of whether lending money to a person or business is risky. As more institutions embrace big data and Hadoop on cloud, they’re finding that these seemingly innocuous elements may be more accurate than the usual credit score in determining repayment reliability. If it takes you a while to input an email address, for example, big data has shown that may be indicative that you’re using a new email for the express purpose of applying for the loan (which is usually not a good sign). And they’ve also found that Apple users are less risky to lend to. Make of that what you will.
Based off of these and other factors, people are able to be approved for online loans, even if they don’t have a credit score. From this perspective, it’s easy to see how big data can be seen as a blessing for those who would be shut out of the process under normal circumstances. As helpful as these types of loans can be, it’s important to note that for now, most of them are designed for the short-term, and even though interest rates can be lower, some of those same factors may lead to loans with higher rates. It’s all done in a case by case basis, but when these alternative lenders approve up to 60 percent of loans for small business when the average is 20 percent for other organizations, the option can’t be dismissed.
The use of big data in this way, however, is not without controversy. Being denied alone because your friends on Facebook with the wrong people strikes many as absurd. Not to mention that this delves into personal details more than usual, which may feel like an intrusion on privacy. Despite these concerns, big data is clearly the future for loans. Big banks are no stranger to big data analytics, so it’s likely only a matter of time before they adopt similar processes.
Rick Delgado is a technology commentator and freelance writer.
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