If you are involved in clandestine gambling activities or drunk driving, watch out – you might be hampering your chances of getting a loan from new-age online lenders like InstaPaisa, GoPaySense, Faircent, CashCare and Vote4Cash! Lenders have been using an algorithm to track your social media profile for assessing your creditworthiness.
A report in the Economic Times on Monday says: "Online credit marketplaces like CreditMantri and BankBazaar.com that have found a clientele in the 25-35 age group do their due diligence on borrowers using not just payslips and bank statements but also unorthodox metrics like phone location data, SMS alerts and social media behaviour.”
How does it work?
The social lenders run their algorithms through tonnes of data in just a few minutes to assign someone a personality score. This score determines the rate at which the person would get a loan. The rate so determined could range from, say, 9% to 30% -- against the standard industry norm of 13-17%.
Besides, mobile phone data also give lenders and other financial service providers useful information about potential borrowers. From someone’s smartphone usage – how much the person spends on airtime, how he or she uses mobile wallet, etc – algorithms can determine both creditworthiness and need for credit.
A report in the Economic Times on Monday says: "Online credit marketplaces like CreditMantri and BankBazaar.com that have found a clientele in the 25-35 age group do their due diligence on borrowers using not just payslips and bank statements but also unorthodox metrics like phone location data, SMS alerts and social media behaviour.”
How does it work?
The social lenders run their algorithms through tonnes of data in just a few minutes to assign someone a personality score. This score determines the rate at which the person would get a loan. The rate so determined could range from, say, 9% to 30% -- against the standard industry norm of 13-17%.
Besides, mobile phone data also give lenders and other financial service providers useful information about potential borrowers. From someone’s smartphone usage – how much the person spends on airtime, how he or she uses mobile wallet, etc – algorithms can determine both creditworthiness and need for credit.
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