Credit where Credit is due: Using data science to lend to customers without a credit history
Traditional loans are based on banking history leaving a large segment of people ineligible. These however, represent a highly untapped segment representing large purchasing potential. How do you deem if someone is trustworthy when you have no information to base your decision on? This session will detail methods of evaluating people and extending loans irrespective through leveraging technology and data in today’s digital world
1) Potential for lending in developing countries such as India
-Current Scenario -Untapped Potential that exists 2) How do you make an informed decision with no traditional sources of data
-What are the traditional sources of data -What are the alternate sources -How can Big Data and Data Science help? 3) Algorithms to deem credit-worthiness
-Credit-scoring architecture -ML techniques employed for different data sources and types -Feedback loops to come full circle
Driven by identifying patterns, deriving insights and problem solving, data science and Vanitha is a natural fit. She has been instrumental in Oxigen’s evolution to a data-driven organization backed by a decade of experience in gleaning actionable insight and scalable data science solutions. She and her team enable key business decisions using ML, Big Data technology and sometimes just plain grit. When not working she is analysing random trivia usually over chai. She can be reached at email@example.com
Analyzing numbers, trends and visualisation is like breathing to Neelu, be it in industry specific domain analytics or identifying insights from polling numbers each election season as a hobby. As a core member of Oxigen’s Data Science Team, Neelu has built a company culture of utilizing data-driven insights for key business decisions. She has more than 7 years of experience in the industry across domains and can be reached at firstname.lastname@example.org.