Editor in Chief Sarah Wheeler sat down with Manish Garg, senior vice president of product and technology at EarnUp, an autonomous financial wellness platform, to talk about how his company is using gen AI to deliver a personalized experience for customers at scale. Garg has a deep background in building enterprise software and has spent the last decade working with fintechs in the mortgage lending space.
This conversation has been edited for length and clarity.
Sarah Wheeler: What differentiates your tech?
Manish Garg: We focused on borrower, financial health, compliance and risk data protection as the guiding principles while building our tech stack. We always work from the desired outcome backward, focusing on consumer financial health. We bake in aspects where we serve our tech to servicers or credit unions or banks to help them reduce default risks by doing a lot of data analysis behind the scenes to help them identify how they can reduce risk of non-payments, reduce any default risk, and keep their books healthy but also keep the consumers in a healthy place. So we have a lot of tech in place for predictive analytics.
SW: How are you leveraging AI?
For a very long time, we were mostly doing traditional AI, which is building forecasting models, predictive analytics and being able to classify risk into different categories and providing all of that back to the enterprises. In the last 18 months or so, things have changed dramatically.
We were in a fortunate place to have some visibility to that very early on. And we started investing in that right at the beginning and we’ve now built core capabilities in our platform to be able to do things that we’ve only talked about in the industry for years. It’s like pipe dreams finally coming true — being able to generate compelling, hyper-personalized content for consumers to help the loan officers, back office, underwriter or processor to do their jobs in even more efficient ways. These are capabilities that we all hoped someday would be real, but it seemed like science fiction, and suddenly it’s not. Suddenly, it’s here.
SW: How have AI capabilities changed in just the last six months?
MG: We’ve had AI around for a while. Most of the people on the tech side understood it and appreciated it, and the data scientists, but for a lot of business users, the value was not clear. But for the first time, it’s something everyone can touch and feel. So that is something that is fundamentally shifted and why there is so much adoption and why there is so much optimism around that. The second part of it is doing things which seem almost magical or very, very difficult to do — that has become very easy to do because of large language models (LLMs) and AI.
For example, creating hyper-personalized content for a consumer. We do a lot of that with our customers where we are able to ingest a lot of personal finance information about consumers,