Prodigal and the consumer finance hyper-personalization frontier

In the world of consumer finance, providing a tailored, empathetic experience can make all the difference for borrowers navigating difficult financial situations. 

At Prodigal, the AI and machine learning team is pioneering innovative approaches to hyper-personalization - leveraging advanced algorithms to optimize every customer interaction.

Talking to Dr. Harsh Pareek about AI and consumer finance personalization

"Top of mind for us is hyper-personalization," says Harsh Pareek, Prodigal's Machine Learning Lead. "How can we make experiences for borrowers that respond uniquely to their user journey?"

Harsh explains that the widespread use of basic templates and pre-written messages lacks the nuance to truly connect with customers' individual contexts and needs. "Our goal isn't just to have text that looks novel or follows a particular style," he states. "We're looking for messages that actually increase conversions and drive action."

Powering hyper-personalization with the "Two Towers" model

To achieve true 1:1 personalization, Prodigal is adapting the powerful "two tower" model that has transformed recommendation systems across industries like entertainment, social media, and e-commerce.

"This is a machine learning model that's also sometimes called a factorization machine," describes Harsh. The model involves two neural networks - a "user tower" that captures all available data about an individual, and a "content tower" representing the characteristics of a specific message, product, or recommendation.

"By running the model, we get the user's affinity for this kind of message or content," says Harsh. "We can generate many different message options that could appeal to different people, and then pick the one that will truly resonate with each individual customer's situation."

Bringing cutting-edge personalization to collections

While major tech players like Netflix, Amazon, and Meta have paved the way for hyper-personalized user experiences, Harsh notes this "hasn't really made it to consumer finance yet - and that's what's top of mind for us here at Prodigal."

In lending and collections, micro-targeting every communication is critical for building understanding and trust. As Harsh explains, "If someone defaults on a debt, they typically default on all debts at once. Our goal in sending emails is to stay top-of-mind, so when they're able to pay, we may be the first they reach out to based on previous empathetic or discounted settlement offers."

By analyzing users' digital journeys and borrowing histories, Prodigal's AI can prescribe the ideal outreach depending on the situation. "If someone has been delinquent for a while but just opened the payment portal, an appropriate message could be 'Are you confused about your options? We have agents ready to help,'" offers Harsh. "For others, urgency or empathy may resonate better."

The future of hyper-personalized consumer finance

While navigating compliance complexities, Harsh sees hyper-personalization as "really humanizing" and enhancing consumer relationships. "We're thinking about how to use the entire user journey to influence what messages we send people."

With our AI's assistance in optimizing every interaction, the future is about treating each consumer finance customer as a true individual and providing an experience as personalized as a 1:1 conversation.