Imagine you’re a credit card collections professional, and you’re viewing your list of delinquent customers. In something of a collections quiz, here’s what you see:

What can be initially gleaned here?

• These are all first time delinquent customers
• They all are in the first bucket of delinquency
• Their dollar balances are in a similar range

These are not made-up figures. They represent actual customers of a credit card client. These particular customers completed BackOnTrack^®. Because of this, we have more information about them than would a typical collector. In this post, we look at what a collections group might do differently with better information.

One size fits all in early stage collections

How differentiated is your collections approach for customers in the first stage of delinquency? In conversations with lenders, it’s common to hear there’s a one size fits all approach. The customer is in a given delinquency bucket, and there is a single playbook. Primarily, a series of repeat communications to make a payment.

One lender utilizes proprietary risk scores for customers, including the 1-30 DPD bucket. The frequency of recorded message outreach varies depending on the level of risk the customer presents. Riskier customers receive recorded messages earlier and more frequently. There is no differentiation in treatment options.

As we’ve said before, first stage delinquency is the Grand Central Terminal of collections. Thousands stream in, and no station manager can tell where they’re all heading initially.  Considering the volume and the lack of information, it’s not surprising collections shops apply a common set of treatments to all.

Differentiating treatments based on customer profiles

The consulting firm McKinsey has given this some thought. In The analytics-enabled collections model, McKinsey proposes one framework for using analytics to apply different treatments:

Notice the approach here. As the risk of the customer increases, so too does intensity of efforts. Junior, green collections staff handle the lowest risk customers. Those who have a higher likelihood of returning to accounts in good standing.  As the risk increases, so too do the level of skill for the collections staff, and presumably the range of options that can be applied to the situation (e.g. settlement at the extreme end of the risk spectrum).

The trick of course is: how do know the level of risk?

Start by asking the customer

Let’s return to the list of customers at the start of this post. They don’t present easily determined risk profiles. What to do? How about this: ask them why they’re late.

We do this as part of the BackOnTrack experience. It’s proven to be very good at predicting payment behavior several months later. It also allows a faster resolution of the delinquency that better matches the customer’s circumstances.

Here is what three of those customers above, in their actual own words, wrote about why they were late. One of the three ended up being a chargeoff several months later. Can you guess which one?

In this case, it’s (C). The clue was the mention of work hours as the cause of the missed payment. Our analysis shows that work-related reasons for delinquency are the toughest financial situations for customers to manage.

This is an example of a simple question that immediately sheds light on the customer’s situation, and repayment risk. In our experience, many of the BackOnTrack questions illuminate the credit risk of an early delinquency customer.

Armed with this additional information, a creditor could move much earlier to help a struggling customer, or to secure a higher recovery as appropriate. Think in terms of McKinsey’s model above. If you knew a customer presented a higher risk, move quickly to your more experienced, talented collectors. If you knew they were low risk, keep ’em on the back burner, and happy.

The good news with all this? It works in a digital environment. You need not have a high touch, lavishly staffed collections group to learn more about the customer’s situation.

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