Artificial intelligence (AI) already plays a substantial role in our daily lives. Anyone who leverages the “recommended for you” engine of Siri or Alexa understands this. In addition to everyday tasks and conveniences, AI is being implemented across numerous industries to address large, complex issues. These industries include cybersecurity, healthcare, and even the collections industry. But just how can collections agencies take advantage of the power of AI? Let’s take a look.

There for the taking

The total amount of consumer debt in the United States tallied more than $17 trillion in 2023. When you consider that the average success rate for debt collection agencies in the U.S. is between 20-30%, that’s a sizable segment of available collections dollars that go uncollected.

A host of factors account for this lack of collections success, including:

• Inaccurate contact information: Collectors may struggle to reach debtors if their contact information is outdated or incorrect.
• Lack of proper documentation: Inadequate or missing documentation can make it difficult to prove the debt’s validity, leading to disputes and non-payment.
• Inefficient collection processes: Poorly managed collection processes, including lack of follow-up and inadequate staff training, can result in lower success rates.
• Improper or non-existent account segmentation: Failure to understand how “cohorts” of like accounts can intelligently inform collection strategies and campaigns leads to suboptimal macro approaches to collections.

Many collections agencies have turned to robotic process automation (RPA) and AI to help in these areas. Automated workflow processes grounded in RPA can eliminate human errors. And AI can be used as a safeguard to ensure that necessary information is not only present, but correct. Of course, AI is generally more efficient in addressing issues that are not neatly linear. These include researching and gathering required information without the need for a network of human-designed if/then statements that tend to inform foundational RPA. And that is truly the benefit of AI. It can handle an enormous volume of tasks more quickly than any individual or even an entire team could hope to do.

Volume and velocity

No matter the application – be it in the collections industry, the healthcare space, or elsewhere, the benefit of AI fundamentally comes down to the idea of simultaneously solving for both volume and velocity.

But what exactly does that mean?

Let’s take each one separately and then bring them back together using a real-life example with which most people are familiar – Netflix.

One of the primary ways Netflix leverages AI is to generate subscriber recommendations for shows, movies, and other offerings based on your given profile. The primary aim of these recommendations is rather simple. It is t0 keep you on the platform, consuming content. As such, it is in Netflix’s best interest to make “good” recommendations. Otherwise, its quarter of a billion subscribers might take their viewing and subscription dollars elsewhere.

Of course, everyone understands that – at the current stage of AI maturity – the best recommendations for what you would most enjoy would likely come from a trusted friend or even a movie buff.  With enough time, that individual could sit down with you, ask a host of questions about your viewing habits, show you clips from different movies, and have a discussion around what you find entertaining. Then, after a few hours, this person would  return with an incredibly effective series of recommendations.

This may be a good idea in terms of the best possible outcome. However, two fundamental problems exist:

• No one wants to wait that long for recommendations or repeat this process every time they learn something new about their viewing preferences, no matter how good the output.
• No one individual trying to put together a list of recommendations could effectively scale even a small group, to say nothing about hundreds of millions of people all regularly modifying behavior and preferences.

That’s where AI comes in!

In a snap

In the time it took to read the previous paragraph, AI models, such as those Netflix employs for this task, are capable of reviewing a member’s profile and user data, and comparing it against millions of data points and permutations. The model can parse any connections across the data and make what amounts to thoughtful, intelligent recommendations at scale. This is, in short, how AI addresses the problem of volume.

Often accompanying volume in this day of lightning-speed data creation is the notion of velocity. Large data sets – at least those large data sets that underly a problem to be solved – are rarely static. New data points flood into the environment in what amounts to literal real time. At that velocity, it stands to reason that the connections and model conclusions that were valid only days, hours, or even minutes ago, may no longer carry the same validity. Fortunately, AI can handle that volume in a way no human being possibly could – making updated connections in milliseconds and effectively “editing” or adjusting conclusions as warranted at a tremendous scale.

In the time it takes to snap your fingers, any capable AI engine can evaluate hundreds of thousands of data attributes in addition to the billions upon billions of permutations required to attain the desired conclusion. In the case of Netflix, that equates to a list of recommendations that keeps the viewer on its platform.

Not just for entertainment

Any matter where AI is deployed in an attempt to address this can essentially be reduced to solving the twin problems of volume and velocity. This applies in the collections industry as well. The idea of increasing collections and revenue is absolutely at the core of that volume and velocity problem.

Collections agencies possess an enormous amount of data about their consumers. This data represents a largely untapped goldmine, mostly as a result of – you guessed it – the massive volume and velocity of the data, making it difficult to assess beyond the most rudimentary of conclusions. Fortunately, AI is capable of agnostically exploring that data, looking at millions of different permutations, and testing near-countless hypotheses to uncover connections that, in turn, help craft more effective collections strategies.

Of course, a well-seasoned individual collector can also reach accurate conclusions about this, but it would take weeks or months before deriving anything of value from a human being’s assessment. And it isn’t likely that any collections agency has the time or staffing necessary to enable this. The advantage of AI here is the sheer speed at which it moves. Given how quickly an effective AI engine can assess exhaustive iterations without the penalty of lost time that a human would incur for chasing a dead end, the engine can afford to “investigate” connections without bias for what may or may not yield results. In short, no stone is left unturned. If, to use a deliberately absurd example, consumers with the middle name Clarence have a staggering likelihood of paying in full, the AI will make this identification and suggest collection strategies that capitalize on this information.

In practical terms

The specific areas where AI can benefit collections agencies include:

• Propensity to pay​: Assigning a propensity or probability to pay based upon more nuanced data than credit scores alone.​
• Best time to call​: Taking the “guesswork” out of when to call a debtor by assigning probabilities to desired outcomes.
• Preferred communication channel: Leveraging external data to calculate a preferred means of communication.
• Campaign cohorts​: Grouping and aggregating debtors by like characteristics – rapidly and at scale.

An absolute necessity

A collections agency has a goldmine of meaningful insights in its data, which it has largely left untapped. And while a highly trained member of your staff could certainly comb through that data and arrive at some of those valuable insights, there is no world in which they could do so with the speed and accuracy of AI.

In today’s ultra-competitive market, AI is no longer a “nice to have.” It has quickly become table stakes, and the effective use of AI is becoming critical for collections agencies to increase collections without increasing costs.