Imagine a scenario where Keith (customer) and Jason (debt collector) are having a conversation:
Keith – Well yes, I know I have a balance past due, but I hope you understand this is not a good time. I am at the office right now.
Jason – Oh! I am sorry to catch you at the wrong moment; can I call back in the evening?
Keith – Well I moonlight as a Radio jokey. I can talk between either my day break 12-1 PM or 6:30 PM – 7:00 PM.
Jason – Very well, let me make a note of this……
While this looked like a perfectly executed collection call, there is a fundamental flaw in this scenario. The problem is that these notes may not be referred ever again and hence a crucial piece of information about the borrower’s communication preference would unfortunately be lost forever!
In today’s world where terabytes are no longer considered absurdly large and gigahertz is easily available on a laptop’s processors, for the lenders, not being able to use every bit of available information might mean losing out to the competition. Customer experience in debt collection is no longer just a good to have.
While the banking industry has seen a dramatic shift in the past decade or so – with online banking and app-based services that enable customers to make transactions anytime, anywhere and across devices, the debt collection process has somehow lagged behind in this customer-centric approach. Most outsourced agencies still have traditional call-center setups, which use rudimentary risk segmentation method without any regards to behavioural aspect of the customer. The lack of empathy in the collection agents’ tone coupled with untimely follow up calls may not only cause grave inconvenience to the customer but may also diminish chances of amicable collections altogether and any possibility of resurrecting customer’s loyalty towards the bank.
What banks need today is a customer centric approach to debt collection. And this is where Artificial Intelligence comes in by enabling debt collectors to effectively plan and execute a well-crafted customer segmentation strategy by utilizing data. The availability of huge volumes of historical transactions of customer data can be utilized effectively to analyze and predict patterns and create accurate risk segmentations by taking into consideration customer behaviors and outcomes.
In this blog I would like to propose a ‘3 RIGHTS’ strategy that uses AI to create customer friendly collection strategies.
A considerable shift is the need of the hour to look at debt collection from a customer-centric perspective to bring about a change that will not only improve collections but also ensure an improved customer experience. Many banks today, are shifting to technology to ensure a customer-first approach to debt collection, and Artificial Intelligence (AI) is leading the way in ensuring effective steps to make this process efficient and profitable while ensuring higher levels of customer’s satisfaction.
Existing processes can be improved to deliver better value with the application of AI by analyzing data and making recommendations based on usage patterns. Objection handling and language used by successful agents can be analyzed to provide insights and best practices to improve performance and productivity of other agents.
Businesses thrive when customers see value in their services. A great experience with a brand is what helps customers stick. The banking industry has been actively progressing with a customer-first approach and reaping the benefits that come along. It’s time debt collection adopts this approach too, to deliver an experience that is in the interest of customers while constantly improving collection in a structured manner.
EdgeVerve’s CollectEdge is a data driven intelligence application powered by advanced machine learning that helps reduce delinquency rates, boost recoveries and improve operational efficiencies, all-the-while delivering a great customer experience.