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A better path to account prioritization

Prioritizing accounts always matters. But when volumes are high and your resources are limited, it matters even more.

That’s exactly where we are right now - you’ve got more accounts, but not more resources to match them.

You can’t afford to chase accounts that can’t afford to pay. And the prioritization models that we’ve been using leave a lot to be desired.

Why good enough isn’t good enough anymore

When your resources are limited, account prioritization becomes even more important. Every account you pursue has to be an account that will pay, or it’s wasted time.

But the most common models we’ve been using to prioritize accounts all have major drawbacks.

  • Prioritizing based on account age assumes more recent accounts are more likely to pay, but that’s not a guarantee.
  • Prioritizing based on account balance aims high so your return is larger than your investment, but the biggest balances might be big for a reason, meaning spending time on those accounts backfires.
  • Prioritizing based on scoring models incorporates factors that should be helpful, like account history and bureau data, but are often incomplete and out of date.

Credit scores aren’t enough

It’s worth underlining that last part - credit scores, often considered a gold standard prioritization ingredient, can take weeks or months to update.

But what if your customer got in a car accident last week? Or got a promotion yesterday? Both of those situations will greatly impact their ability to pay, but a credit score won’t tell you either of them.

Where to get better information

While account and payment histories, credit scores, and bureau data are all useful parts of a solid account prioritization model, they are just that - parts.

To build a better model you need information that is:

  1. Current
  2. Directly from your customers

Think of the things customers say to you every day:

“I just got laid off, but I have two job interviews next week.”

“Can I change my due date? I only get paid once a month.”

“We’re moving to a bigger place.”

“I got promoted to manager.”

Every one of those things has what credit scores don’t have - detailed, up-to-date information straight from the person who knows their finances best.

How to add your current customer information to your prioritization models

Manually evaluating each customer conversation or interaction for this kind of information isn’t feasible - you’re already stretched thin.

And forget about being able to apply that information to create a genuine model that you can apply across accounts.

But if you could, it would transform your prioritization models for the better, right?

That’s why Prodigal took the opportunity to build AI customized for consumer finance businesses just like yours, including the ability to extract information like this and add it to your models to improve your account prioritization.

We call it an intent-to-pay score.

No more guessing. No more relying on outdated or incomplete information.

  • We start with our AI Intent Engine, which we’ve trained on more than 300 million consumer finance interactions (and counting!).
  • That expertise is put to work as our AI analyzes your customer interactions across every channel - voice, text, email, and chat.
  • Then we add your other customer information, such as data in your CRM.
  • To enhance these ingredients, we blend the insights from your customer information with demographic and credit bureau data.
  • Putting these things together paints a current, comprehensive picture of each customer, factoring in their financial history, creditworthiness, and personal circumstances.

What you get

The outcome? A customized intent-to-pay score for your customers, indicating how likely they are to pay right now.

You prioritize your accounts based on that score (or add it to your model), and start focusing on accounts with the highest likelihood of payment. 

And it pays off…literally.


Ready to learn more?