There are countless reasons to go digital in collections in 2020. Many of those reasons have to do with the end user: the borrower. A growing proportion of consumers and borrowers are tech savvy Millennials who expect SMS reminders and sleek web portals. They don’t like to be contacted on the phone and have a reasonable expectation that new numbers calling them are scammers.
As collection strategies move to digital communications, lenders can present tailored, personalized communications. True machine learning provides the opportunity to mine data and decipher trends hidden within your consumer behavior. Recognizing these trends guides the creation of intuitive self-service customer experiences that will cultivate customer loyalty and satisfaction and ultimately improve collection rates.
But borrowers are not the only ones who benefit with today’s machine learning in debt collections. Used appropriately, machine learning can guide new levels of regulatory compliance and audit-proof your operations.
Humans aren’t perfect. That much is obvious. The vast majority of compliance problems in debt collection come from manual processing problems—agents saying things they shouldn’t, accidentally calling too many times—or from poor automation. There is a lot of room to improve and tweak the system using augmented decision-making from machine learning.
Machine learning is at the heart of Katabat’s strategy engine. But unlike many of our competitors who advocate turning over decision making entirely to AI, Katabat’s machine learning leverages the power of both human and artificial intelligence to create intuitive and flexible workflows. Everything Katabat does is founded in the expertise of our operational managers. They draw on their extensive knowledge of collections regulations and best practices to draft proven and actionable strategies for SMS, email, and phone that are a gold standard. This strategy and template repository is available to our clients so they can choose communications that meet their compliance requirements.
With automated outreach in place, the machine learning does its job of chewing through massive quantities of data about the borrowers and debts. It predicts optimum outcomes and selects the best block of actions from among the human-written strategies to deliver a superior, personalized customer experience.
Machine learning is not going to solve everything for you. And it shouldn’t. The technology is still relatively new, and while computers can now play chess or Go, they can’t come up with great advice all on their own. (Witness the really terrible results of efforts to have chatbots give medical advice.) Nor can they generate emails and letters that comply with all the relevant regulations. Many experts agree that the best use case for machine learning is to augment human agents’ performance and vice versa. The ML and human partnership is where value is skyrocketing right now, but we’re the only ones who are really talking about this in collections.
In artificial intelligence, people talk about “black boxes” when there is a serious lack of transparency. With the way some systems are built, it’s hard to gain understanding of why the AI came up with the answer it did. In some cases this is by design, when the programmers don’t want to share proprietary secrets. In other cases, it’s because transparency simply wasn’t a priority. Both of these situations can lead to the widely reported cases of biased AI you may have heard about.
At Katabat, we have made transparency a priority. We know it is essential to our customers. Not only do they have to comply with FDCPA and TCPA, they need to be able to demonstrate that compliance throughout their operations if there is an audit. We meet that need by providing a detailed feature analysis. The report shows exactly what criteria the ML used to make a recommendation and which data had the biggest impact on the choice. The only place I want to hear about black boxes is in aircraft safety systems.
Help Your Agents Help Your Customers Pay
Machine learning amplifies the impact of your best agents. It informs their actions using predictions based on more data than they could hope to assess, and supports them with Katabat’s library of expert strategies. Katabat solutions bring together the best of both human and artificial intelligence so you can reach your customers exactly how, when, and where they are most likely to respond. Satisfied customers are more likely to not only complete the payment process but to pay more than the average. In fact, our data shows a 38% increase in payments with our machine learning at work.
Ye cofounded Katabat in 2006 and continues to enjoy creating technology solutions to solve business problems. Ye’s deep experience in artificial intelligence, banking and internet technologies have and continue to shape Katabat’s product development and evolution. Prior to Katabat, Ye worked for Bridgeforce and Ensuredmail, Inc. Ye received a BS and Master’s degree in Electrical and Electronics Engineering from Sichuan University. He also has his Master’s and Ph.D. in Computer Science from the University of Delaware. Ye is an avid technologist and has published multiple conference and journal articles in the fields of computer vision, pattern recognition, and artificial intelligence.
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