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!

We wanted 866-611-2731 to be recognizable. TrueAccord was built as a consumer facing brand from day one. We have one number, 866-611-2731, that we use for outbound and inbound calls. Our name is distinguishable, not a three letter acronym. We have Google reviews and an online presence. We wanted consumers to easily find, research, and comment on our presence. We want to make a difference. You can't help consumers if they don't know who you are Being in debt is scary, confusing, and generally not a great experience. When consumers are bombarded by calls from unknown numbers or worse, callers who pretend to be from their area code, their trust in phone calls erodes. Less trust leads to fewer contact rates, and disengaged consumers. Running away from your debt is a bad idea if the alternative is working with a customized, personalized, and digital first experience that actually helps you pay down what you owe. We wanted people to know who's calling. The thing is, debt collection can be a stepping stone. When turned into a cooperative and personalized experience, it can be a first step to getting back on your feet. People get into debt for many, diverse, largely unexpected reasons: divorce, job change, healthcare issues for them or a loved one. By making debt collection accessible, TrueAccord aims to be a part of your growth journey, not just focus on helping you pay a single debt. You'll find customized payment options, an easy mobile experience, and a helpful customer service team (when you call our number, 866-611-2731). Having a recognized number helps us call *less* When consumers don't pick up, the most common strategy is to call again. Agencies may call a number 5 times per day. At TrueAccord, we don't think this is a good experience. When we call a consumer, even once, our recognized phone number allows them to find us online and be convinced that they want to talk. From there, going to our website or finding one of our emails in their inbox is a breeze. Self service is welcoming and easy. No more aggressive repeated phone calls when it's least convenient. Being customer-facing and helpful is our #1 goal. If you see 866-611-2731 in your caller ID, know that we'd love to help Call us or click a link. Great experience in debt collection isn't a myth anymore. That's why we started TrueAccord, and why we want you to have an easy time finding us and talking to us.

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.

New to collections? Looking to understand the moving parts? Or maybe you're an experienced strategist looking to understand how to best use TrueAccord? Hear our Head of Business Development, Jason Hass, and Head of Client Services, Pej Azarm, talk about these important topics.

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.

TrueAccord's machine learning based system handles millions of consumer interactions a month and is growing fast. In this podcast, hear our Head of Engineering Mike Higuera talk about scaling challenges, prioritizing work on bugs vs. features, and other pressing topics he's had to deal with while building our system.

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.

TrueAccord's system is machine learning based, but every new product type requires a little bit of tuning to beat the competition. Hear our CSO and VP of Finance in this short podcast about the Conversion Team and what it does to make sure TrueAccord stays ahead of competition.  

“I think no matter what direction you look at it from, debt collection in the United States is just broken. Because it takes consumers who want to pay, who could pay and turns them into customers that can’t,” noted TrueAccord CEO and Co-Founder Ohad Samet. As an innovator looking to fix the broken system by using data as a substitute for “draconian collection methods,” Samet’s position on this issue is expected. But it’s a position he shares with an unexpected regulatory source: CFPB Acting Director Mick Mulvaney. Read more in this link from PYMNTS.