Companies have many choices as to how they manage their delinquent and charged off debt portfolios. Options range from having an internal collections team, working with third parties, selling their debt to debt buyers to engaging the court system. Every company has its own requirements, so collection strategists must leverage the available options to varying degrees to optimize its resources, protect the brand name, and recover as much as possible while remaining compliant.

TrueAccord achieves extraordinary collection results because it’s designed to fit into any debt collection model. Here’s how we optimize each stage of the collections process.  

Pre-charge Off vs. Post-charge Off

Creditors service their debt in both the pre and post-charge off stage. Some creditors may choose to sell their debt post-charge off, but more sophisticated creditors will often have a strategy with multiple phases.

Pre-charge off focuses on remediation and putting the customer back on track. The collection is on the part of the loan that is late, which is often not the whole amount. Interest and fees may be accruing while most of the collection work is done as first party, under the creditor’s name. Post-charge off is the opposite; most collection is done as a third party and no interest and fees accrue. At this point, many creditors believe the customer relationship has been lost .

First-party vs. Third-party Collections

First party refers to using the creditor’s brand, while third party means using the agency’s. The former is common in pre-charge off collections and the latter in post-charge off.  However, savvy collection strategies make use of both first and third-party collections in both stages.

Historically, pre-charge off is considered to be “soft” collections while post-charge off are recoveries or “hard” collections. As banks and issuers see more repeat customers, they are moving away from “hard” collections, because better customer relationships means more repeat customers. Several issuers use “back on track” plans to give customers in debt an on-ramp to financial services through more secured/specialty programs or products.

Legal and Pre-legal Channels

Depending on the characteristics of the debt, creditors can take legal action by hiring a lawyer to review the accounts and determine which ones to pursue and collect. However, many creditors first want to see how the collections route will succeed before resorting to legal action. This collection period before utilizing legal demands is often referred to as a “pre-legal” strategy.

The TrueAccord Advantage

Our goal with every client is to help build a strategic vision and chart the most effective path to great returns. TrueAccord fits into multiple stages of a sophisticated collection strategy: first party pre-charge off, first or third party post-charge off, pre-legal strategy, and even as a champion challenger to debt sales.

We help determine which accounts to pursue, for how long, and what the next steps should be after accounts have been handled by our system. TrueAccord has a vast library of content and algorithms based on years of real-life collections data that fit into these various strategies to improve returns and increase customer engagement.

For large clients with diverse collection requirements, we leverage multiple strategies based on their existing approach towards their current use of pre- and post-charge off, debt sales, legal, and placement duration. TrueAccord offers a combination of solutions to make the most out of our strategy. Our debt collection automation technology, Heartbeat, can handle the account as a first party pre-charge off, then become a third party post-charge off followed by a call-center agency, or a pre-legal strategy followed by a law firm.

TrueAccord works with every client to optimize collections performance for the long run. We can help you build an intelligent, sustainable and successful collections strategy. For more information, check out our podcast page on SoundCloud.

We like to tell our new clients that working with TrueAccord to manage debt collection will be an entirely different experience than they’re used to. Traditional collection agencies are usually fast out of the gate, calling the highest value debtors and pushing for short-term plans or liquidation settlements. The problem with this approach, apart from the lack of learning mechanism, is it sacrifices long term gains from consistent payers for short term settlements. It may move quickly but it also fades quickly and puts a damper on long-term results.

TrueAccord takes a more steady and consistent approach, both in learning and offering payment arrangements that customers stick to. Our decision-making engine, Heartbeat, is based on machine learning and it is the driving force that helps us find connections between different types of debt along common similarities. It learns as it interacts with consumers, and ends up beating our call center-based competition. It does take some time for the machine to learn and tune itself; it starts behind at first but rapidly picks up steam as the decision engine learns more and more and we begin to make adjustments based on the inputs that are unique for each client and portfolio.

Our piloting process is built to help the machine to learn and fine tune its strategy. It is the first step in determining the right debt collection approach for a type of debt As we gain more experience and work with millions of consumers, we leverage our experience working with major banks, issuers and lenders to give our subsequent clients the best results early and consistently based on those accumulated lessons.

Here’s how we build the perfect pilot process.

1. Gather Client Data

Our new client first gives us the base collections information and content, including the number of accounts, the number of placements and the duration of each. While some clients have giant placements of 10,000-100,000 accounts per month, others have smaller sizes of 1,000-5,000. Ideally, we look for at least three placements of 3-6 consecutive months, with at least  5,000-10,000 accounts per placement. This ensures we’re analyzing enough information to ramp up the algorithm, perfect the experimentation model and enhance performance of future debts.

TrueAccord uses a dynamic, self-service model for collections, which means we try to get the customer to pay on their own without any direct contact from an agent. Only two percent of our customers ever speak to an agent on the phone or via email, and more than 90 percent of customers with balances of over $300 choose payment plans. Since we focus on getting more customers on payment plans with lower breakage rates, the liquidation curve appears slow at first because of the time it takes to optimize customer-plan fit, but it catches up and exceeds the competition’s curve in roughly 45-60 days .

2. Build the Ideal Initial Model

When we onboard a new client, we look for specific policies and procedures they have, disclosures, content and communications to come up with the right recipe for our pilot, including duration of payment plans and settlement authority. That’s where Heartbeat’s decision-making engine comes into play. Heartbeat is always evolving based on all the data it receives.

If the client has a product that we haven’t worked with before, we gather anecdotal information and do a manual review to see what new features we can add to Heartbeat to optimize collections. We validate the anecdotal information with data by monitoring direct consumer engagement rates. For example, if more than two percent of consumers are still reaching out to our engagement team that’s an indicator that we need to improve the flow to service them manually. We build a feedback loop into the process to continually evaluate if we have the right configuration settings and determine how to modify our strategy.

3. Experiment, Learn and Optimize

Once the pilot is up and running we continue to execute on a suite of performance enhancing experiments. Our goal is to gain data that comes from real-life testing of each strategy and content. Over time, Heartbeat learns automatically how each scenario will impact liquidation, giving us valuable insight on how the product and process are working for each customer, not just on hard outcomes such as liquidation or conversions.

Here’s a quick example of how we would adjust the pilot based on higher-than-normal engagement rates. On new types of debt, we might not be using email content that is easily understood by each audience. The consumer might not even know why they are in debt in the first place, or to whom they owe the debt (especially with debt buyers who collect on debt that may be old, and using a name that the customer doesn’t immediately recognize), and that’s why they reach out to the engagement team. When we detect a spike in contact rate and run through a manual review, we discover how to refine the messaging and target each consumer better to lower the direct engagement rate. Over time, Heartbeat learns to automatically classify each engagement so there’s no need for a person to take the time and do it.

After testing, the final step is to roll the learnings back into the machine learning models. In the end, it’s all about creating a cadence of placing accounts, monitoring performance, building consistency into the model and maximizing liquidation.

TrueAccord’s highly successful debt collection model all begins with a well-oiled piloting process designed to kick-start the machine learning engine, enhance the consumer collection experience and optimize long-term liquidation rates. To hear more, please tune into our podcast.

The genesis of Heartbeat—the machine learning engine that makes TrueAccord debt collection a reality—is a story that demonstrates our commitment to consumers who hope to take control of their financial future. Heartbeat is how we create a more humane and thoughtful collection experience.

So What Is Heartbeat?

Heartbeat is a fully automated and reactive decision engine that uses a combination of machine learning and data-driven heuristics to determine the optimal way of interacting with each individual debtor. It tells us when we should contact them, how often, through which channel, with what content, and what specific types of offers we should provide. Most importantly, it is the engine that replaces an agent’s phone-based collection activities with a data-driven strategy, ultimately making the same decisions, but automatically, more quickly and with a bigger heart.  

How Was It Built?

We built Heartbeat with three key tenets in mind. The first is compliance: to create a pre-approved boundary for what should and shouldn’t be said to each debtor. The second is performance: how we leverage data-driven heuristics to test our assumptions and continually improve the performance of our debt collection system. And the third is the customer experience: how consumers engage with the product at every phase and how we ensure we’re seeing positive reactions. Put these all together and they constitute the core foundation of Heartbeat.

We Start with Data, Then We Test

The process starts with data, and lots of it. We’ve collected years of historical collections data to help determine the optimal way to communicate with consumers and generate the best collections model. We then set up an experimentation engine to test and refine the process for continuous improvement.

For the testing to be relevant, we ask a few key questions: Is this a problem that we can define well enough to solve and take action? Do we have enough data from different segments of our population to solve the problem for all of our customers, not just some? And does the result add value to the process? Otherwise, it’s not worth putting the time into it.

Once we decide to move forward, we establish a hypothesis (e.g. paydays are the best days to set up recurring payments) based on the intuition of our domain experts who know what an ideal customer journey should be like. We test our hypothesis with an A/B experiment to see if it performs better than our current status quo. The data we collect from these experiments shows us what tactics work best. To ensure we’re optimizing for various audiences,  we re-target the test to new segments until we have enough data to apply the new treatment to the broad audience.

Then Machine Learning Takes Over

The biggest challenge is that data and heuristics are not enough to offer highly personalized treatments at scale. At some point we have to transition, by taking the learning outcomes based on all of our initial data and programming them into a machine learning model. The goal here is to replace human heuristics with an automated decision-making model that continues to learn from multiple samples at scale. A human agent is prone to biases, such as using non-compliant language in their calls when pressed to make their monthly numbers or using the wrong tone based on a previous conversation that may have impacted their mood. A machine learning model doesn’t fall prey to these biases.

The more data we collect, the better the system gets and the more accurately it represents edge cases and special needs. Now with more than 2.5 million consumers and tens of million of interactions, we’re seeing great results and constant improvement. The larger sample sizes also allow us to reach a statistically significant result faster in large experiments, often in only 30 to 60 days.

What’s Next for Heartbeat?

Right now, only two percent of our customers still need to interact with one of our agents. That’s already a pretty impressive number, but we still want to reduce it even further.

We constantly scale the technology behind Heartbeat and improve its intelligent self-service capabilities that feature our three key tenets: better compliance (Heartbeat can navigate the legal restrictions with less risk than a person), better collections performance (50-500% better than our competition), and a better customer experience where consumers are empowered to manage their debt in a way that puts control back in their hands and treats them the way they want to be treated.  

At TrueAccord, we’ve always been committed to providing the best customer experience for a behaviorally complex debt collection process, and Heartbeat is true to its name in working to that objective.

If you’re interested in learning more, check out this interview with one of Heartbeat’s creator’s Sophie Benbenek!

Moving collection communications online means moving away from phone calls. Writing to consumers at scale draws a lot of scrutiny because of regulatory requirements and user experience considerations. Hear our Managing Paralegal and Director of PMO, Antonia Wong, discuss this with our Head of Design, Shannon Brown.

Hear our Director of Operations, Lauren Sawicki, talk about the differences between running operations for a major bank versus TrueAccord. While both are collections related, the differences can sometimes be staggering.

TrueAccord beats traditional agency performance, and does so by using a machine learning based, digital first system. Since our system learns from individual consumer behavior, it requires specific pilot design to provide the right amount of data for the algorithms to tune themselves. In this episode, our Head of Client Services and Head of Data Science discuss the optimal pilot structure to make the best use of our platform.

Our CCO and In House Counsel discuss 2017 litigation and complaints trends for the industry in general and TrueAccord specifically. We discuss the reasons why TrueAccord’s legal risk and exposure are so much smaller than the industry’s average.

Bonus addition: TrueAccord’s CCO, Tim Collins, reviewing trends in WebRecon’s January 2018 litigation and complaints trends report.