Two aspects of the coronavirus recession that rightfully have received the most attention are (i) the hardship suffered by consumers, and (ii) the expected large credit losses financial institutions will suffer. There is another aspect to consider: the impact on collections operations. A view into the situation from the Wall Street Journal:
Borrowers seeking debt relief are encountering jammed phone lines, overflowing inboxes and stretched-thin customer-service departments.
To address the coming surge in delinquent accounts, BackOnTrack has three tools to help collections groups flatten the curve.
Capture early warning signs at scale
As delinquent customers complete BackOnTrack, they are asked questions that illuminate their current and future situation. This “CRM for collections customers” is a source of insight normally not captured early on, or captured expensively via live call agents.
One example of such intelligence is a question that asks the customer how they would raise $2,000 in the next month if needed.
That question is a measure of financial fragility, and correlates with subsequent payment behavior by the borrower. Knowing the answer provides an early warning to collections groups about what is happening with their customers. This helps planning appropriate interventions before the delinquency worsens.
Reduced roll rates in the most intensive delinquency buckets
As described previously in Improve collections roll rates with these three behavioral principles, BackOnTrack reduces the roll rates of delinquent customers. But more importantly is where the improvement happens.
As the diagram below illustrates, collections operational intensity increases as delinquencies lengthen.
88% of the improvement in roll rates from BackOnTrack occurs in the worst three buckets, 120+ days. These are the most intensive buckets. Reducing the number of customers hitting those buckets is valuable relief for collections groups facing an incoming delinquency surge.
Predict the likelihood of rolling
During the BackOnTrack experience, data relevant to the customer’s risk of rolling forward is captured. Various data elements are fed into an algorithm that predicts the risk the customer will fall into a later delinquency bucket 4-11 months later.
The chart below shows the significant segmentation that algorithm provides:
The risk model successfully identified the third of customers with the highest propensity to roll at 31.0% in this population. With this information, collections groups can put in place measures to reduce their risk and help at-risk customers before losing their connection with them.
Click here to find out more about how BackOnTrack’s tools will help with the incoming collections surge.