Gap between the top 1% wealthy and the rest, racial wealth gap, gender wealth gap and inequities that we see around us and its impact on access to opportunities. Often cited challenges were lack of information about marginalized groups and therefore an inability to design products and services to serve them well. Although that may have been true in the past to some extent, that is not entirely the case. Some of the deep-rooted biases that cause exclusion continue to thrive today because of entrenched habits and operational practices that favor convenience and operational cost over wider reach and rational decision making based on data and science. This is not only excluding hard working people who deserve better but also is a burden on economic productivity which impacts us all. Through this initiative called Data for Inclusion, my goal is to foster a dialogue that can serve the needs of our communities, especially those that have been underserved and unserved.

My theory of change is challenging assumptions to break down barriers. And by doing so facilitate development of innovative products and new businesses that will drive financial inclusion.

Currently, I am the Head of Product at Uplinq, a fintech start-up that is dramatically changing the landscape of funding to small businesses by helping banks and lenders say ‘yes’ more often. Lack of access to financing is one of the biggest reasons for business failure.

There are similar challenges around exclusion faced by different groups and segments of people. Given that one of the key objectives of technology developments and AI should be fostering inclusion for financial betterment. There needs to be a concerted effort towards including data that has the potential to break down long held assumptions especially about marginalized groups such as people of color, women, minorities, protected classes and immigrants. This is the primary driver behind the Data for Inclusion initiative.