Retail Collections

RMEx is an excellent choice for retail collections.

While the concepts of Artificial Intelligence have been understood for several years, its application to solve common business problems has been relatively limited. We have been able to use the concepts of artificial intelligence and added them to a debt collection system.

RMEx was conceived in the beginning of 1990. The collection industry was in desperate need of new ways to be more productive in the core operations within the collection cycle. We recognized the trend towards lower fees, higher costs and more demanding clients. This meant that in the absence of a miracle, we would have to look to the solution that was rescuing the major auto-makers and IBM from their financial woes – technology. We had identified several areas that needed attention in order to offer us the chance for a quantum leap in productivity. These areas were –

  • Eliminating the over-working of accounts
  • Ensuring that collectable accounts were sufficiently worked
  • Automating the management of the dialer
  • Finding a method of evaluating individual productivity in a pooled dialer environment (dialers will accept very large volumes of phone numbers, dial the numbers and only connect successful calls to an operator)
  • Automating the “management” of every account. We had assumed that a human was needed to evaluate and determine the fate of every account we had to collect. This was impossible to do, because humans are incapable of accurately analyzing large numbers of possibilities and conditions, and then making educated decisions on thousands of accounts, every single day.
  • We described the basic problem within the industry as follows – Our recovery rate is about 20%. If we put the same effort into the 80% that we do not recover and the 20% that we do collect, is not correct that most of our expenses are being wasted? The first generation of collection software was designed to replace the card system. We needed a new generation of software to make us profitable in the new collection environment.
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