- Do you use Rich Media Messaging in your debt collection strategy?
MK: We use this as our first key part of the strategy. So yes, we utilise every day.
GM: At present we don’t currently use Rich messaging as part of the collection’s journey, but I do believe the opportunity exists to do this in future and to create more interactive messaging that allows customers to do a variety of things, e.g. something as simple as viewing a bill instantaneously even if you are not registered with an online account is an opportunity to improve right first-time outcomes.
- Do you think technology can be used in customer segmentation?
MK: AI and ML certainly have segmentation in their reach I would state especially if this is linked through to many data lakes.
GM: Technology is used to segment our customers today, Natural Language call steering and use of bots in chat channels are just examples of ways in which we do this now. In collections we’ve introduced online triaging of customers to better identify and understand circumstances before we engage in conversation – this type of approach can help with handling/treatment based on vulnerability, affordability and contact preferences. There is still a significant opportunity to build on data-based segmentation with these types of overlays and the advent of Online Banking could help this process become more iterative and accurate as time goes on.
- Do you think you can use technology to measure customer vulnerability and what are the best ways of measuring customer outcome?
MK: Open banking already has examples of where this is the case. Credit Agencies are already marketing to consumers to share their information with open banking to support any hardship and vulnerability cases.
GM: See above on vulnerability. On the best ways of measuring customer outcome, I’m not sure of the specific nature of the question, but in Collections, % of costs on secured payment terms, ‘stickability’ of outcomes and e.g. 14/28/35 day cure rates are all good indicators of customer engagement and adherence
- What impact do you think Artificial Intelligence and Machine Learning could have on digital collections in the future?
MK: AI and ML can have a tremendous impact in several ways. It can link through to data lakes and target the correctly with the right amount of intensity the debtors likely to pay and those that are having difficulties. It can support existing collection agents with a never forget mentality, ensuring all parts of the collection journey are covered. The list is endless of the advantages AI and ML can bring. Tools like DOMO get this right to the desktop and so fast. Every character is indexed, so a real google engine on the data. Link this further with natural language processing and the self-serve dream becomes a reality.
GM: I think there’s more scope to incorporate new AI technologies into collections strategies, Open Banking is a prime example of this, but the challenge will be deploying automated algorithms at scale and still being able to tend to affordability and vulnerability obligations, particularly in Utilities and Fin Services. Debt Management systems that can ingest data effectively would seem to be the way forward here. Rigid CRM systems present real challenges (cost and logistics) in absorbing and utilising data to drive decision making on (digital) contact channels, so for me, there is an opportunity to develop component-based predictive analytics tools capable of using new data sources effectively to drive strategic planning.