How Big Data Is Revolutionizing the Credit Scoring Industry
Assessing the loan-worthiness of an applicant is no easy task. Credit scores, a three digit metric that offers lenders a way to benchmark your worthiness against the average applicant, is often seen as a flawed system that punishes the “guilty” with a sentence that is much larger than the “crime”.
Assessing the loan-worthiness of an applicant is no easy task. Credit scores, a three digit metric that offers lenders a way to benchmark your worthiness against the average applicant, is often seen as a flawed system that punishes the “guilty” with a sentence that is much larger than the “crime”. Regardless of what the common perception of a credit score is, the bottom line is that your credit score matters and has a huge impact on how easily you can get loans and what rates you are charged for it.
But over the past few years, a number of new start-ups have cropped up that have redefined the way credit scoring is done. Neo Finance, for example, is a Palo Alto based lender for auto loan borrowers. Instead of using the conventional FICO scores to assess the credit worthiness of the borrower, Neo Finance looks at the applicant’s job history and the quality of their connections on LinkedIn to assess their loan worthiness.
While the viability of such social credit scoring mechanisms is to be assessed over the long term, the bigger impact to the industry is being dealt through sophisticated big data assessment systems. Unlike the FICO score that primarily uses an applicant’s transaction history to assess their loan worthiness, these new start-ups make use of a much larger data pool. FICO scores are flawed in that even if an applicant has enough credit worthiness, their score could be impacted by minor oversight on their part like failure to notice a payment deadline. Also, due to the confidential nature of these reports, most applicants have no way to know or contest these figures, unless they pay money to obtain the report.
Take the example of ZestFinance. This company makes use of all kinds of data to assess the loan worthiness of a customer. The company is co-founded by Google’s former Chief Information Officer and considers all data about a customer as credit data. The company uses technology that analyzes thousands of variables including factors like the number of times a debtor has moved house, how well they use capitalization on a web form, etc. to build a profile of an applicant that assesses the risk in a much more efficient way than a FICO score does.
It will be a while before such disruptive new technologies make their way to mainstream lending via banking institutions. At present, most companies owning such alternate credit worthiness measurement systems have their own lending infrastructure. The next decade should see such technologies find greater adoption among banks.
According to a report on CreditRepair.com, one of the leading credit repair companies in the United States, 37% of Americans have a mortgage on their house with another 29% paying a car loan with over 69% paying above the minimum for their credit card debt or loans. Given this scenario, there is a huge untapped market for people shunned by the traditional banking sector to be able to benefit from alternate lending assessment systems. The next decade will tell us how credit scoring evolves and the impact big data will have on the lives of people in need of financial help.
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