<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=306561&amp;fmt=gif">
Skip to content

How ZestMoney is Using Machine Learning to Reach New Customers in India

ZestMoney Chief Data Officer Natalia Lyarskaya explains how cutting-edge technology is helping consumers in India access credit where it was previously unavailable

The appetite for credit is growing in India. However, compared to developed credit markets like the US, India is underserved. There are only three credit cards per 100 people in India, compared with 32 per 100 in the US.

This may be starting to change. In this week’s episode of the Business of Data Podcast, ZestMoney Chief Data Officer Natalia Lyarskaya explains how ZestMoney is using AI and machine learning technology to create a transparent and trustworthy credit solution where traditional banks have been unwilling or unable to do so.

“There is a kind of chicken and egg problem where someone needs access to the credit products but has never been in the financial sector before and other banks, traditional banks, cannot evaluate their creditworthiness,” Lyarskaya says.

She continues: “We believe that using data and technology, we can build this affordable, transparent, financial product for the Indian people that can be used by everyone and can increase also the trustworthy population in this new credit segment.”

Evaluating Customers Using Data, Machine Learning and AI

ZestMoney has created a 100% digital user experience that uses an array of data coupled with machine learning and AI technologies to evaluate new credit lines in a matter of milliseconds.

“Based on the AB testing that we've done we have collected quite a good amount of data,” Lyarskaya says. “[We built] some predictive models that allow us to differentiate between different groups of users, so we can propose different journeys and different options for users to apply for our product.”

While the technology behind ZestMoney’s model evaluates new credit applications and makes the final decision on credit approval, it also guides the user on a personalized journey assessing and modifying questions during the application process based on personal and historical data.

“This [model] is basically behind every decision that we take along the journey,” she explains. “Like, what kind of questions we want to ask a user, or do we want to ask this question in one way or the other?”.

She concludes: “There is a model that stands behind that tells us what exactly we need to do and who is the user that we see in front of us. So that is all based, not just on our assumptions, but on what the data has been telling us.”

Key Takeaways

  • Machine learning and AI are helping financial firms reach new credit markets in India. Where traditional banks have been slow to react, tech upstarts have been able to capitalize.
  • Balance privacy and personalization for a better user experience. Understanding how much data an individual feels comfortable sharing is an important first step to creating outstanding user experiences.
  • AI and machine learning solutions enable better products but do not create them. Human critical thinking is needed to make sure a system works. AI and machine learning make sure that the system is efficient.