Leaders: Sandeep MS, Shan Pan
Globally 2.5 billion individuals do not have a financial identity (e.g. Credit Score) and a vast majority of these people are either the poor (less than $8 a day) or the lower-middle class ($8 - $20 a day). In the absence of financial identity, banks and other formal financial institutions are hesitant to lend to them without a viable method to underwrite credit. While the microfinance movement in the last decade has effectively addressed the problem of financial exclusion among the poor, it is the lower-middle class who still suffer due to the lack of decent option for short-term credit.
However, very recently, some revolutionary financial institutions have started to create a new type of Credit Score for customers who are left out by Credit Bureau using alternative data such as employer data, social media data, mobile data, etc. Essentially, these organizations combine technology and data-science, and leverage Machine Learning and Artificial Intelligence-powered algorithms to crunch vast quantities of non-traditional data to create Alternative Credit Score.
Participating Organization: Finwego
Developed in Harvard Innovation Lab, Finwego is a cutting-edge Saas based HR & financial access portal that helps Small and Medium Businesses (SMBs) to manage their human resource effectively, and at the same time provide convenient and affordable short-term credit to their employees.
In India alone, there are 100M lower middle-class customers who work for Small and Medium Businesses (SMBs) don’t have easy access to short-term credit despite a stable job and a regular salary. Bank find it difficult and expensive to credit profile these customers as they work for unverifiable and unlisted SMBs.
While Finwego’s SMB partners use Finwego portal to manage their human resource functions such as attendance management, leave management and payroll processing, Finwego uses the data (with employee consent) to underwrite credit. For e.g. Finwego uses length of employment to predict stability, Time In/Time Out attendance data to predict punctuality, leave pattern to predict consistency, salary increment and bonus payout to predict reliability and dedication, etc.
This Sandbox works with Finwego to further fine-tune their credit underwriting algorithm by establishing deeper relationships between alternative data and credit discipline. Further, the Sandbox will help Finwego to identify additional alternative data points that can further improve the algorithm’s ability to predict the borrower’s future behavior with respect to repayment of the loan. Using user-centric research and design-thinking methodologies, the Sandbox will also support Finwego to identify features and functionalities to enhance the UI/UX of Finwego SaaS portal.