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SmartData Collective > Big Data > Workforce Data > Big Data and Lending: A Match Made in Heaven?
Big DataData ManagementWorkforce Data

Big Data and Lending: A Match Made in Heaven?

Larry Alton
Larry Alton
5 Min Read
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Is there anything that big data hasn’t touched? It’s nearly impossible to find some aspect of business that remains unaffected by the mind-boggling rise of big data and the technologies that come along with it. And in the next five or ten years, you can expect to see the lending industry changed for the better. The Relationship Between Big Data and Lending Every so often, you can look around and find an industry or niche that clearly stands to benefit from big data more than most. Lending – arguably this country’s largest niche in a burgeoning finance industry – is one such area. In fact, when you look at the current relationship between big data and lending, as well as future predictions, it’s hard to even imagine how this industry got along without it. In order to understand the full impact big data will have on the lending industry, it’s helpful to check out some different concepts and trends. Here are a few of the top benefits stemming from this profitable relationship. 1. Quicker Approvals For years, one of the biggest pain points for lenders and borrowers has been lengthy approval processes. Whereas it used to take days or weeks for an approval to be processed, it can now happen on the spot. “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,” data and tech expert Rick Delgado explains. “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.” We’re already seeing this play out in isolated situations. Rocket Mortgage is a QuickenLoans product that has enjoyed enormous popularity over the past couple of years. While the proprietary formula used to come up with loan approvals isn’t known to the public, big data is clearly involved. 2. Down With the Credit Report Have you ever thought about how ridiculous it is that a credit score determines someone’s ability to get a loan? In order to have a high credit score, you have to be a major borrower. Sure, it indicates that you pay off your debts on time, but it also shows that you’ve historically purchased things with money you don’t have. For example, someone who has $350,000 in outstanding debt, three credit cards, and makes $55,000 a year will most likely have a better credit score than someone who has no outstanding debt, one credit card that’s rarely (if ever) used, and an income of $150,000 a year. It’s totally backwards. With big data, lenders may finally be able to move away from this broken format. No longer do borrowers need to have a credit score over 600 just to avoid paying a high interest rate. Lenders will look at things like income, savings, signs of financial responsibility, and other elements that indicate someone is making smart choices with their money. 3. Less Paperwork The amount of paper that goes into a loan application and approval is astounding. If you own a house, you know just how fat that stack of paper they hand you at the closing table can be. Even a car loan packet can be 50-plus pages long. Big data stands to revolutionize this as well. By using big data tools, lenders can bypass traditional lending processes and handle everything in a digital format. This reduces dependence on paper and further speeds up the process. What Does the Future Hold? The future of lending is brighter because of big data. Processes will be easier, faster, and more cost-effective for both the lender and the borrower. We’re only just now getting a taste of what’s to come. Get ready!

TAGGED:big datafintechlending industry
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ByLarry Alton
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Larry is an independent business consultant specializing in tech, social media trends, business, and entrepreneurship. Follow him on Twitter and LinkedIn.

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