The science fiction writer William Gibson once observed “the future is already here — it’s just not very evenly distributed.”
This sentiment almost perfectly describes the current state of digital transformation within banking. Some operational areas, like account opening and fraud management, have seen significant changes as banks have worked to redesign legacy processes around new digital capabilities.
Debt collection — by contrast — feels like it has been standing still.
It’s not difficult to understand why. As a collections executive at a large U.S. lender told Aite Group in a
And yet, that same research tells us that banks’ historic unwillingness to invest in collections is starting to waver. Forty-two percent of consumer lending executives from top U.S. banks reported that their IT budget for collections increased from last year. Perhaps more meaningfully, survey respondents report that the allocation of those collections budgets is shifting, with 56% saying that they are planning to increase investments in analytics and data science over the next two years.
There are two primary drivers for this shift towards greater (and more sophisticated) investments in collections.
First,
Credit cards are a good example. Rising acquisition and reward expenses coupled with slowing revenue growth make it unlikely that issuers will be able to afford significant operational investments in receivables management if they wait until the credit cycle turns.
Second — and of more strategic importance — the concept of collections within the broader context of banks’ customer relationships needs to change. Banks are increasingly emphasizing the importance of financial health and are slowly reorienting their products, services and processes to help
Today, far too many lenders draw a bright line between customer management (servicing and growing relationships with customers who are current on their payments) and collections (reclaiming money from delinquent customers). This division — observable in everything from org charts to siloed decisioning systems to the very language we use to describe these processes — perpetuates a more important philosophical divide.
A customer is in good standing and is deserving of support as long as they are making payments on time — even if they are making financially unhealthy choices, like paying just the minimum on a revolving balance. However, as soon as they cross that bright line into delinquency, they become a problem that needs to be efficiently (and sometimes ruthlessly) solved.
If that sounds like a vast oversimplification, it is, but it’s also an operational reality that is continually reinforced by the arbitrary divisions that banks draw (and regulators sometimes strengthen) between these functional areas.
The truth is that the possibility of a customer falling behind on their payments is more than a credit risk problem for banks — and it should be treated that way. Delinquency harms customers’ financial health. It delays them in reaching their long-term financial goals. It’s an outcome that is, with proactive monitoring and preemptive guidance, often avoidable.
And that is exactly what banks’ digital capabilities can help with. Artificial intelligence and machine learning can efficiently churn through huge amounts of customer data looking for signals that might indicate the need for a minor “
The opportunity for customers and collections departments to benefit from their banks’ digital transformation initiatives is incredibly significant. It’s time to make the future a bit better distributed.
