Automation can roughly be divided into operations automation – handling communications, payments, complaint registration and other day-to-day tasks automatically – and decision-making automation – making choices about which customers to call, what payment plans to offer, etc.
- Operations automation is easy to automate, with the notable exception of situations where a human-in-the-loop is a key feature, whether due to customer service concerns (e.g. the perceived quality of service of talking to an actual human) or due to the unpredictability of exceptional circumstances (e.g. breadth and unpredictable nature of complaints). It is hard to see how these obstacles can be overcome by currently foreseeable technology, but these processes are likely to be disrupted in the near future by real-time assistive technologies – e.g. voice monitoring to provide suggestions on tone and approach, intelligent anticipatory data retrieval to more effectively leverage knowledge bases, etc.
- Decision-making automation has traditionally been the hardest to automate, but is also the one where modern analytics (including machine learning) have had the most impact. The collections industry is rather conservative in adopting such technologies but I do expect empirical treatment workflow design to be a thing of the past by the middle of the next decade – whether through a combination of process-based and predictive modelling, or through a full-on data-driven approach like reinforcement learning.
by Panayis Fourniotis Pavlatos,
Director of Intelligent Decisions at QUALCO