A Game-Changing Account-Decisioning Tool for the Collections Industry

One of the biggest challenges facing the collections industry is weighing the financial risks of litigating a case. We all know that mistakes can be costly. It’s not uncommon for collections professionals to expend significant time, energy and unnecessary cost chasing down cases with little to no chance of paying back the investment.

Until recently, guesswork has been the norm when it comes to deciding which accounts are worth pursuing in court. Powered by decades of Unifund’s industry leadership and data acumen, Recovery Decision Science created two, revolutionary account-decisioning models: Paymetrix AD and AD+.

Paymetrix AD and AD+ analytical models help creditors identify and prioritizes legal collections decisions, allowing them to collect:

The most profitable account in the right channels.
In the right order.
All while optimizing costs and minimizing waste.

RAINFALL: THE SCIENCE BEHIND PAYMTRIX AD

Paymetrix is built on RDS’s proprietary “rainfall” data analysis model.

Much like a TV meteorologist predicts both the probability, and amount of precipitation we can expect from a weather system, the RDS rainfall model addresses two essential variables to guide account decisions:

RDS considers numerous variables to analyze and project an individual consumer’s probability of paying toward an outstanding debt. Currently, no other industry resource offers this level of depth and accuracy.
Once the probability to pay is determined, RDS uses a second set of variables to determine how much that consumer will be able to pay toward a debt balance.

The variables used in this second step calculate the probability-adjusted, net present value for each individual account.

Using all of the available metrics, plus an analysis of the potential litigation costs, the Paymetrix AD model then produces a profitability index, which guides a company’s strategy with regard to prioritizing accounts to pursue.

PAYMETRIX AD AND AD+ IN ACTION-A REAL WORLD CASE STUDY

To put AD to the test, we worked with a sample of 50,000 previously-litigated accounts from a major national bank.

But first, let’s review the difference between the two models, AD and AD+:

Paymetrix AD is an “explainable” model that relies on linear and logistic regression. Some financial institutions prefer the explainable model to satisfy their particular regulatory requirements.
Paymetrix AD+ is a “black box” model that utilizes advanced, machine-learning techniques. As such, its outputs cannot be explained with the clarity we can achieve through the basic AD model. Because the black-box model delivers superior results (see below), RDS uses AD+ as its default account-decisioning model unless otherwise instructed by our clients.

In building the case study, we wanted to establish a benchmark for comparison to AD and AD+. To that end, we looked at the expected results of a random sample where litigating 30% of the total accounts would yield 30% of the total profits.

Below is a matrix summarizing the results of the test:

Here’s what we see from the matrix:

The GREY dotted line is the benchmark of a random sample of accounts.
The BLUE dotted line shows that Paymetrix AD yields 40% of profit via 30% of the accounts, a 33% improvement versus the random sample.
The GREEN line illustrates the impact on profit by using machine-learning power of Paymetrix AD+. As you can see, AD+ obtained 58% of the profits from just 30% of the accounts, which translates to a 93% improvement vis-à-vis the random sample.

Whether Paymetrix AD or the advanced, machine-learning metrics of AD+, the collections industry now has access to an account-decisioning tool that is able to accurately pinpoint the right accounts to litigate, in the right order.

Let’s summarize the benefits of account-decisioning through the Paymetrix models: