TrueAccord’s Chief Product Officer, Laura Marino, is a civil engineer turned product management executive.
As mentioned in a blog post by TrueAccord, Laura has a unique viewpoint or perspective on the evolution of machine learning in software across a variety of industry segments.
Laura recently shared her insights on machine learning at TrueAccord and in collections, in general.
She noted that at TrueAccord, they know or realize that consumers prefer digital channels and self-service.
They also know that simply offering the digital channels is “not enough.” To truly start engaging with consumers, we need to “help them throughout the journey.” This is “where machine learning comes in,” Laura explained.
What is machine learning?
Machine learning is an application of artificial intelligence (AI) that offers systems the “ability to automatically learn and improve from experience without being explicitly programmed.”
In the context of collections, and “specifically in the context of our consumer-centric approach to collections, machine learning is a wonderful tool to personalize the experience for each consumer, effectively engage with each of them, and ultimately help resolve their debt,” Laura added.
She pointed out that there has been a lot of hype around machine learning, but often firms that claim to do ML are “really using fixed rules or heuristics (if a consumer does X, then do Y) without including any of the automatic learning and improvement. Or they may be using ML for a very specific, very limited scope – like automating some consumer support responses.”
Laura further noted that the reason that leveraging ML is so challenging for something “as complex as collections and recovery is that it requires a lot of expertise in data science and behavioral science, it requires a lot of user research, and it requires a lot of data.”
She pointed out that this is “not something that a company can decide to start doing overnight as an add-on.”
Applying machine learning to debt collection
TrueAccord is leveraging machine learning and behavioral science “throughout the entire journey, from initial engagement all the way to resolution,” Laura revealed.
She also shared:
“We were built specifically around the hypothesis that focusing on machine learning-driven, digital-first experiences was the way to transform debt collections. We have been doing this since 2013, and we have orders of magnitude more data than anyone else. Just to give you an idea: we send millions of emails per day, and hundreds of thousands of text messages per week and our ML engine learns from every open, every click, every action on our website, and every interaction with our call center agents. Because of all of this, we have something that is very hard for anyone to imitate.”
She added:
“Unlike traditional collections, we do not use demographic data like age, zip code, or creditworthiness to personalize the experience. Instead, we use engagement data about how the consumer responds at every step in the process.”
Laura also noted that they have handled debts for more than 24 million consumers and they have collected data about each individual interaction with those consumers. That wealth of data, along with their user research is “behind the ability of Heartbeat (our fully automated and reactive decision engine) to personalize the experience for each consumer,” Laura revealed.
She added that they’ve seen this data-driven machine learning customer-centric approach “lead to increased customer satisfaction, better repayment rates, and lower complaint rates.”
For more details on this update, check here.