LoanAdda's proprietary algorithm disburses highest online Rent Securitisation Loan of Rs. 150 cr


New Delhi [India]
Financial technology company LoanAdda on Monday announced disbursing its highest and first-ever online Rent Securitisation Loan worth Rs. 150 crore to Okaya Energy Ltd, through Bajaj Finance Ltd.
Underwritten by LoanAdda's proprietary algorithm, LoanSwift, this is the biggest loan amount disbursed by the platform as well as the first-ever completely online disbursal of such a high value loan by any online lending company in India.
The achievement has fast-tracked LoanAdda's growth trajectory, strengthening its position as one of the fastest growing fintech companies in the country.
LoanSwift, its proprietary technology platform enables quick underwriting decisions using machine and deep learning models by evaluating the applicant's data across nearly 2,000 data points.
As soon as a loan application is received, its system accesses data from numerous sources to provide a more accurate understanding of all potential borrowers. All of this happens within minutes and a decision is arrived at almost simultaneously, enabling a fully automated and seamless experience for the customer.
"Our transparent and user-friendly platform gives us a technological edge over the competition. By using more data and analysing customer default probability, the credit scoring system is able to predict behavior, thereby helping lenders come to a more conclusive decision based on data," said co-founder and CEO LoanAdda, Anshuman Mishra.

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LoanAdda, however, uses Machine Learning to process each customer's application as a vector of factors. It then maps the corresponding factors to enhance the chances of lending to the customer.
This has been operationalized in their app which analyses new to credit customers or complex transaction like Loan against rent securitization. The platform analyzes thousands of nontraditional and traditional variables such as how a customer fills out a form, how much time they spend on a site, and more to accurately score borrowers, along with vast amounts of in-house data, such as customer interaction data, payments profile, and purchase transactions.

Disclaimer: No Business Standard Journalist was involved in creation of this content

First Published: Sep 18 2017 | 7:22 PM IST

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