You may have already downloaded our free eBook, Automating Debt Collection 101. This is an excerpt.

Flipping the traditional butts-on-seats model on its head and teaching a machine how to do a human’s job is not an easy process. We’re talking about domain-expert based automation. This is a grueling, operational process of understanding why some people pay and others won’t, and translating it into algorithms that grow with the data they accumulate. To realize the benefits of automation, you’ll need to pay attention to three elements:

Data Infrastructure

The key in this process is defining our key performance indicators. One can’t start this task if data are unavailable, corrupt or fragmented. Most collection teams use a tapestry of systems – for scrubbing bankruptcies, for calling, a mail processing system, a payment processing interface and so on. That leads to a fragmented data store, which makes it impossible to know which actions were taken on a debt and attribute success to any of them. You can’t improve what you can’t measure.

Your first step is creating a unified data store for all your data.

Feedback Loop

Extracting knowledge from domain experts can be frustrating. Often they decide intuitively and cannot explain their reasoning. It takes training, ongoing conversation, and an iterative process to structure their knowledge. The feedback loop includes three steps:

1. Interviewing your experts: presenting several cases that were successfully converted and those that weren’t, and asking what they have in common.
2. Implementation: the resulting model is validated against data trends.
3. Deployment: the model is deployed to your system, and agents can comment on its performance in real time and compare it to the way they would act under similar conditions.

Creating a feedback loop between your agents and data scientists is incredibly important. Without it, your data scientists are guessing, and your agents work without guidance, their knowledge untapped.

Increasing Relevance

The human brain is an incredible machine, and it offers intuitive connections that computers can’t make. Whenever faced with new information, even the slightest addition, the brain recalculates its route and makes new assumption about the person they are interacting with. A machine can’t replicate the brain’s ability but it can mimic it – with some help.

Use your experts’ understanding of a customer’s response to inform the way you send your initial communication, as well as using responses you get from them to inform your next communication. While deploying follow up flows based on browsing patterns, we realized some flows converted up to 7 times better than a regular message.

Find pockets of customers who don’t get personalized treatments and create those responses.

Bottom Line

Consumers are increasingly reliant on credit to fund their consumption – whether short or long term. This leads to defaults, and to debt collection being a part of any business’ tool box. As you grow, using automation or an automated solution like TrueAccord is the right way to minimize your costs while increasing your performance, scalability and customer satisfaction.

Interested to learn more? Pick up our free eBook: Automating Debt Collection 101

Debt collection and account receivable departments often start with one person contacting late customers and evolve from there. Even third party collection agencies grow this way as they get more business. As a result, most collection departments are comprised of large teams of operators trying to negotiate with customers. Data science teams that are tasked with improving performance and profitability usually approach the task in one of two ways: process automation or agent-independent decision automation.

Process Automation is the effort to automate manual tasks done by collection agents, replacing them with an automated process or a self-service portal. This may mean skip tracing, logging payments, or queuing up phone numbers to call. The data science team acquires data sources or builds a process that replaces manual work with automated one, reducing the amount of time an agent spends per case. It’s about optimizing agent time on the phone, making sure that every action an agent takes is a high yield one, while busy work is replaced by some level of automation.

Decision automation means trying to teach a machine how to make the same quality of decision an agent makes in the collection process. For example: how to talk to debtors, what to tell them, how to respond to their issues. Because most agents have a hard time explaining in detail why they made one decision and not the other (they “just know”), often data science teams treat agents as an unreliable source of information. The team determines what they are trying to optimize – for example, right-party contact or the number of calls ending with a payment. They then build models that optimize these metrics, but without asking agents for feedback – only looking at long-term liquidation results.

While both approaches are important and are often used at TrueAccord as well, there’s a third one that often gets overlooked because data scientists and agents don’t interact often: Agent Dependent Decision Automation, or Expert Based Automation.

Interested to learn more? Pick up our free eBook: Automating Debt Collection 101

When we started working on our patented collection engine, Heartbeat, the industry told us: you’ll fail. Computers can’t collect. Humans do. The best you can do with automated communications is to drive inbound calls, so human collectors can “seal the deal”. Fast forward 18 months since our launch, and Heartbeat beats call-center based agencies in a growing number of segments.  It turns out that computers collect debt pretty well. How come?

Debt collection is a numbers’ game. Consumers are ready and able to pay at different times, react to different stimuli, and need varying levels of support in the process. Teaching a machine to respond to these needs was historically more expensive than hiring humans, but as technology improves and compliance requirements grow, this is changing rapidly.

Humans are great at acting on intuition and responding to a changing situation. We act well based on partial information, guesses, slight changes in tone of voice and intonation. Good sales people do so without thinking. Humans are great at identifying and understanding corner cases and responding to complex inquiries. Machines can’t learn these things unless explicitly taught, and many of these skills are nuanced and complicated. Machines are “robotic”, for better and worse, and can’t have empathy.

Humans do have downsides, too. We are susceptible to biases. We make decisions based on the few past examples we remember and ones that fit what we believe. Collectors fixate on high balance accounts, worry about missing their goals, fight with their significant other and lose focus. Machines do not. Machines don’t forget a thing, and they always take as much data as available into consideration. Machines don’t talk back or get angry.

Historical attempts failed because they either tried to replace humans with even lower-paid humans, or tried to automate and get rid of humans altogether. We realized that a hybrid approach was the best one: machines make accurate decisions based on historical data when available, and learn from humans when not. Humans understand corner cases. We had to create a combination of a strong engine, and a team of experts to continuously improve it.

How does that work? When Hearbeat doesn’t “know’ what to do with a customer, it defers to our team of experts in San Francisco. They resolve the issue for the customer, and also give enough input so Heartbeat will know how to deal with the same situation in the future. The combination allows us to hit incredible productivity rates, while beating other “robotic” and passive “payment gateway” solutions.

Can machines collect? They can, and apparently many who are in debt prefer their targeted approach. When you think about the user experience, the ease of use and the automation, it’s actually not that surprising.

Tasked with creating a collection strategy? Fighting to reinvent your collection funnel or turn around a failing operation? It’s time to harness technology to get ahead. 21st century technology has arrived, and digital collections are here for you.

This 28 page book includes:

• Collection strategy 101: for the uninitiated – learn the basics
• The pitfalls in common collection strategies – how legacy forces constraints
• Using technology to get superior results – how machine learning and digital collections can help you blog through your goals

Learn from our experience working with dozens of companies from various segments and different stages, and with hundreds of thousands of customers in debt. Download the free eBook here.

Already interested in digital collection and how they can enhance your strategy? Worried about upcoming changes in debt collection rules and how you’d adapt to them? You’ve come to the right place. Check out our website and talk to our experts about what we could do for you.