A few years ago, Raju, a small tea shop owner based out of Delhi wanted to avail a housing loan. To his disappointment, he found out that being a business owner in the unorganized segment he was not credit worthy for the mainstream bank loans. Through an acquaintance of his, he ended up applying for his loan on loanadda.com, a platform designed to help the under served in meeting their financial requirements. LoanAdda thorugh its propreitary AI enabled algorithm (LoanSwift) based underwriting was able to analyze vast amounts of non-traditional credit data to enable his loan approval. LoanSwift is the only machine learning platform developed specifically for credit under writing, consisting of capabilities such as data aggregation, which identifies, cleans and aggregates data from thousands of sources, regardless of format and modeling tools which help train, ensemble and productionalize machine learning models that address credit risk analysis.

Technology Platform for the Needy
“LoanSwift”,its proprietary technology platform enables it to make quick underwriting decisions using machine and deep learning models across customer life
cycle, from acquisition to

customer engagement to credit under writing and risk modeling. As soon as a customer application is received, its system is capable of pulling diverse data from numerous sources like cash flows, credit scores, tax payments, ratings, margins, other financial data etc. All this happens within minutes and a decision is arrived at almost simultaneously making it a completely automated, digital experience for the customer.

Loan adda uses Machine Learning or Artificial Intelligence in which a vector of factors are mapped to the corresponding factors to enhance the accuracy or probability of an event, there by enhancing consumer experience and reducing costs

Loanadda uses Machine Learning or Artificial Intelligence in which a vector of factors are mapped to the corresponding factors to enhance the accuracy or probability of an event, thereby enhancing consumer experience and reducing costs where in technology lays emphasis on streamlining the customer experience.

Talking about LoanSwift, Anshuman says, “Traditional underwriting works for evaluating borrowers with a considerable credit history, but when there is limited or no data, there is no possibility of ascertaining the difference between a credit worthy and a high-risk borrower. Machine learning fills those gaps by analyzing a considerably broader

set of data.”

Targeting the under banked primarily, these are customers who have no or limited credit history and numerous inaccuracies, some thing which is not enough to access credit worthiness. As a result, they are denied credit because they cannot be underwritten by traditional systems. The platform analyzes thousands of nontraditional and traditional variables to accurately score borrowers, including thin-file and no-file borrowers. It can analyze vast amounts of in-house data, such as customer interaction data, payments profile, and purchase transactions. LoanSwift can also add traditional credit information and nontraditional credit variables, such as how a customer fills out a form, how much time they spend on a site, and more.”

The Journey to Success
Powered by a team of top notch banking expertise Loanadda disburses loans on its own app which is the highest ranked app in Working Capital Loans and Loan against property. LoanAdda’s focus on profitability makes it have a hybrid model of Lending cum aggregation with Banks & NBFCs. The company has been a catering to a wide range of segments from under banked to high end ticket loans like the first ever online Rent Securitization Loan of Rs.100 cr from Bajaj Finance along with disbursing the first online NRI Home Loan for Nigeria, Africa location, from Tata Capital Home Loans. Aiming to do a business of 1000 cr in the FY 2017-18, Anshuman concludes,“Our future plans revolve around big data analytics and develop a dynamic scorecard based credit risk model that automatically decides the loan score with improvised ways at lesser costs”.