The collections process is a vital component of any lender’s business model, but it’s often an area that doesn’t receive the attention or investment it needs. Collections teams are always under pressure to do more with less—to recoup as much as possible from delinquent accounts, while keeping operational costs as low as possible.
This is especially difficult because collections have always been a people-intensive part of the business. The traditional approach involves a sliding scale of treatments for delinquent accounts, beginning with gentle reminders for overdue payments and escalating to more robust demands. Call centre teams aim to contact as many accountholders as they can, as soon as possible after they fall into arrears, because early intervention has the best chance of resulting in payment.
COVID-19 throws a curveball
The problem is that in the COVID-19 era, we’ve seen unprecedented and unpredictable economic disruption. As a result, many thousands of customers who banks believed to be low risk are now struggling to pay their mortgages and bills. Over the next few years, we’re going to see collections teams come under significant strain as they attempt to triage this huge new wave of delinquent accounts.
At the same time, most organisations are recognising the importance of customer-centricity as a competitive advantage. Customer-centricity is a way of doing business that fosters a positive customer experience at every stage of the customer journey, encouraging repeat business and creating greater customer loyalty in the process. Debt management is part of the customer journey and collections strategies must balance customer experience whilst preventing losses.
In this series of articles, I’m going to explore how collections teams can master this balancing act by adopting new technologies. The key point is that while it’s going to be necessary to transform the way collections are managed, this transformation doesn’t need to happen all at once. There’s plenty of low-hanging fruit that can make a big difference to collections efficiency, and these changes are relatively easy to integrate into existing collections processes without much disruption.
Problems with simplistic segmentation
Today, let’s look at the first and easiest of these quick wins: segmentation. Most collections teams already do some kind of segmentation of the accounts they’re tasked with collecting. Typically, they sort these customers into broad buckets of low, medium and high risk, using indicators such as the remaining balance of the loan, the customer’s risk profile, and the number of days delinquent to create an approximate risk score.
The trouble is that these methods are relatively crude. They lump together debtors whose financial situations are wildly different and treat them all the same way. This lack of personalization is problematic: if you fail to take individual circumstances into account, it becomes impossible to make smart decisions about which customers to focus on, how and when to contact them, and what the optimal terms for a settlement are likely to be.
A smarter approach to segmentation
The solution is to adopt more sophisticated methods of segmentation by creating predictive models that combine data from the collections system with other sources of information from across the business. Transactional data such as credit card spending can be used to identify changes in spending habits; digital behavioural data can reveal whether customers are exploring additional credit solutions such as overdrafts and loans; and channel interaction data can tell you which channels each customer prefers to use.
For example, if a customer is very active in your online banking system or mobile app, but almost never calls your call centre, it might suggest that digital channels are a better way to contact them about overdue repayments than chasing by phone. This is not only likely to be more effective—it’s also cheaper, because it frees your collections agents to focus on calling other accounts.
How SAS can help
At SAS, our Collections Optimization solution uses advanced analytics and machine learning to transform collections with a customer-centric approach to increasing revenues and reducing losses.
Consolidating data from relevant sources across your business and creating a consistent 360-degree view of each customer gives a much broader understanding of the individual customer and the strategies that can solve their debt-related problems. This approach helps identify pre-delinquent and financially vulnerable customers, so you can be more proactive in helping them to avoid falling into the collections process in the first place.
AI-based segment discovery algorithms sort customers into small, focused groups, taking into account their individual circumstances and optimising customer contact strategies based on channel preference and probability of payment and predicted yield.
First steps towards transforming collections
Better segmentation can make a huge difference to your ability to collect more payments faster while delivering the right customer experience—and you can implement better segmentation without needing to make significant changes to the rest of your systems and processes. If you’d like to learn more about how to get started, why not check out our ebook THE AI ENABLED COLLECTIONS MODEL