BackOnTrack’s core value for financial institutions is a valuable reduction in roll rates.  There are separate benefits as well. This post describes how BackOnTrack allows creditors to predict the likelihood that an early stage delinquent customer rolls forward or cures.

BackOnTrack: Data Collection Platform

As the consumer engages with BackOnTrack, data is collected. The data reveals the numerous aspects of  delinquent customers and their situation:

• Cause of delinquency
• Numeracy
• Interest in changing due date
• Interest in payment reminders
• Interest in autopay
• Mindset about current financial situation
• Sense of agency over one’s financial affairs
• Financial fragility
• Pace of completion
• Personal benefit of getting account current

From this data, we’ve developed a statistical model that predicts the likelihood an early-stage (1-29 DPD) customer will either roll forward or cure. The data in this model is unique to BackOnTrack, and does not overlap information derived from other sources.

Analysis Design

The source of data for the model discussed herein comes from a Top Ten credit card issuer deploying BackOnTrack. The period covered is 4-11 months after collection of the BackOnTrack data, as shown in this diagram:

Performance in October examined each account’s status: cured (current), stabilized (back in the 1-29 days past due bucket), rolled forward (30+ days past due).

Predictions of Rolling Forward or Curing

Using data collected from BackOnTrack, a statistical model was developed. The model is built on 9,500 credit card accounts that were 1-29 DPD. The model’s efficacy is tested on 4,051 separate accounts. The segmentation results for the test accounts are below.

The model was optimized for the dependent variable “rolled forward”, and it provides a strong segmentation of accounts that are high risk vs. those that are low risk. The latter two risk buckets – 4, 5 – have significant numbers of the accounts that rolled.

Conversely, the lowest risk accounts –  1, 2 – contain few of the accounts that rolled, but instead have a majority of accounts that cured.

With these risk scores, financial institutions can apply risk mitigation measure to the riskiest accounts: quicker triggers if the account becomes delinquent again, prioritize enrollment in autopay, manage dollar exposure, tighten account authorizations, etc.

And the lowest risk accounts can be: deprioritized for work effort, assigned looser authorizations, given longer leash to repair a delinquency, etc.

Click here to find out more about how BackOnTrack reduces roll rates and helps you segment who will roll and who will cure.

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