Katabat hosted industry experts for an interactive discussion and luncheon last Tuesday in London. There were record hot temperatures in London last week. Coincidentally, on the agenda was one of the hottest topics in our industry—Machine Learning in Collections.
Technology continues to disrupt the ARM industry, so it is an exciting time to be part of this movement. A decade ago it was considered cutting edge for a company or software to leverage marketing principles within the collections process. Today the line between consumer-to-consumer communication and business-to-consumer communication has become fully blurred.
Consumers wantus to leverage all of the “digital” channels they’ve been using for years. That means things like email, SMS, two-way SMS, FB Messenger, WhatsApp, and any other tools they are using to communicate. Leading organisations are leveraging the omnichannel trend. We need to embrace not only these principles with open arms, but also the advanced technologies that are making them more efficient than ever. This is no easy task, but it’s our job as consumer advocates to figure out how to make this happen.
As Chris Warburton from Arum shared with us, machine learning capabilities continue to evolve, right along with consumer demand for more intelligent engagement. Instead of monitoring performance over several months and then building, testing, and deploying new engagement strategies, we can leverage machine learning to execute the same process in a matter of seconds.
At Katabat we’ve developed a machine learning capability that satisfies consumers’ needs using cutting edge technology. Now, for the first time in the receivables management industry, there is a product that allows financial institutions to immediately deploy digital engagement programs andapply machine learning to reach and maintain peak efficiency faster than ever before.
We thank all of the attendees for their subject matter expertise, active participation, and valuable feedback.
To learn how Katabat can expedite digital engagement and machine learning within your organisation, contact us at email@example.com.