In 2019, Todd Johnsen, Snap Finance’s Senior Manager of Collections Vendors, was charged with doing something that had never been done at Snap before: developing a third-party collections program. According to Johnsen, “At that time, the only recovery program for charge-off accounts was a call-and-collect settlement at tax season. I knew we could do better, but we’d have to start from scratch.” Johnsen also found a large amount of backlog accounts that had never been worked by a collections agency, as well as the need for a forward flow third-party recovery program.
Johnsen and team surveyed their options: they looked at both traditional agencies (predominantly making outbound calls) and digital-first collections solution providers, like TrueAccord. Johnsen was particularly interested in how digital-first providers like TrueAccord used machine learning to optimize their relationships with consumers via digital channels like email, SMS, and push notifications.
“My thought process was — we work with subprime consumers who may have bad associations with debt collection,” said Johnsen. “This audience may have already had experiences with incessant collection phone calls, and they are used to avoiding them. I wanted to find an agency that was doing things differently. I knew that TrueAccord was using technology and digital channels in a way that other providers weren’t.”
While Johnsen was curious about working with a digital-first agency like TrueAccord, he wasn’t ready to go all-in immediately. The Snap Finance team decided to engage both a traditional agency and TrueAccord and compare the results. In evaluating the competing partners, key considerations included liquidation rate performance, security and compliance, and optimization efficiency. The result? TrueAccord delivered better results across all measured parameters.
“The reality of the results really knocked me out,” said Johnsen. “What we saw was almost 25-35% better performance on the accounts that we placed with TrueAccord, compared to the accounts we placed with traditional agencies. It was a real eye-opener. In fact, TrueAccord is number one in every tier I have them in. We’ve seen nothing but huge benefits as a result of that individual, digital-first interaction that TrueAccord tailors to each consumer.”
To learn more about TrueAccord’s work with Snap Finance, read the full case study.
Generally, when talking about artificial intelligence (AI) in regards to medical collections, we hear about how it has automated the once-painstaking process of medical coding for billing. But why stop there? With all of its capabilities, AI has much more impressive and patient-facing applications when used to improve customer experience, especially in the healthcare industry which is increasingly digital-first and self-serve. In this post, we’ll explore how AI and machine learning can supercharge the healthcare revenue cycle by catering to consumer preferences, turning billing and collections into a seamless, efficient experience for both patients and providers.
But first: why is it necessary—and even urgent—to improve healthcare revenue management? The answer is patient expectations. Patients now expect the same type of personalized, easy-to-use experience they’ve grown accustomed to receiving from other industries, including banking, airline and retail industries. Patients are now “digital-first” and look for an end-to-end experience that allows them to handle medical-related issues on their own, often from their mobile devices. Patients can already schedule appointments, request prescription refills, receive test results, and even contact their healthcare provider directly through digital platforms. The application of digitization through AI and machine learning to other touchpoints in the patient journey, all the way through billing and collections, can improve customer experience and thereby their overall interactions and relationships with their healthcare providers.
First, digitization powered by AI and machine learning can replace manual and paper processes to speed up the recovery timeline. A 2020 report by InstaMed, a J.P. Morgan company, found that patient collections take more than a month for 63% of healthcare providers. This figure isn’t surprising when 81% of providers still leverage paper and manual processes for collections, while 75% of consumers want to receive eStatements for medical bills. The traditional method of collections does not align with consumer preferences, with more than half (54%) of consumers surveyed saying they prefer electronic communications (emails, text messages, in-app messages and live chats) for medical bills. And a majority of consumers (65%) preferred paying those medical bills digitally as well – whether online through their doctor’s or health plan’s website, their bank’s bill-pay portal or mobile apps – instead of manually. Using AI and machine learning to match the consumer’s communication and payment preferences can drastically improve the time needed to engage and collect from patients.
Second, AI-powered systems can personalize the billing and collections process and offer intuitive payment solutions for patients to achieve the best possible recovery rates. According to the InstaMed report, collecting patient financial responsibility in a timely manner was especially challenging for large patient balances, with 49% of surveyed providers reporting that they cannot collect bills of more than $400 in 30 days. Especially with multiple billers on different payment cycles, it can be difficult for a patient to set up a payment plan with terms they can successfully meet. AI can improve this experience by identifying the most efficient time, place and manner to communicate with a patient about their financial responsibility and go a step further in presenting personalized, affordable payment options.
Third, AI can be used to interface directly with clients where they are and minimize the need for waiting on hold for the next available representative, creating a more seamless, humane process and a better customer experience. AI-enabled chatbots can answer basic questions, while automation can help provide information on why claims were denied and other status updates. Empathetic customer service is important in the healthcare industry and customized customer self-service can reduce frustration for the patient and the number of service agents needed for the provider.
At TrueAccord, we use AI and machine learning to build digital debt collection solutions for billers that put customers first. By implementing behavioral analytics to predict consumer communication preferences and machine learning to create smart, intuitive processes that increase likelihood of patient repayment, TrueAccord products stay a step ahead to ensure a successful revenue cycle where both patients and providers win. To safeguard personal patient information, TrueAccord’s policies and procedures are designed to comply with all HIPAA-related requirements (Health Insurance Portability and Accountability Act), including documenting the use of protected health information (PHI) and the physical, technical, and administrative safeguards implemented to protect PHI. Learn more about how we use AI and machine learning to provide a personalized collections experience at scale here.
Banks are accelerating their adoption of new digital debt collection tools in anticipation of a “tidal wave of consumer debt issues” when government stimulus programs end and financial institutions stop offering forbearance and loan deferral options.
That’s the premise of a new article in American Banker highlighting a variety of technology-powered strategies banks are using to make debt resolution more automated, conversational, and empathetic. These approaches range from the convenient (more flexible self-service payment options) to the high-tech (robotic process automation).
The American Banker article highlights promising signs of progress, particularly for industry players that have not always been known for digital adoption. KeyBank, for example, is in the process of rolling out a self-service digital payment portal designed to offer banking customers privacy and flexibility in resolving payments. And Alabama-based Regions is implementing digital messaging and intelligent interactive voice response (IVR).
At the same time, the article shines a light on the massive challenges facing any financial institution looking to implement intelligent digital debt collection at scale. Here are three common hurdles on the path to digital debt collection maturity – and why they matter:
Challenge #1: “One-size-fits-all” approaches
The challenge: In its overview of Regions, the article makes reference to a single conciliatory messaging tone used in all outreach to delinquent customers.
Why it matters: Consumers differ vastly in their preferences and responsiveness to digital touchpoints. For example, one consumer might respond to a friendly message delivered by SMS, while another might respond best to a straightforward message delivered by email. As a result, a one-size fits all approach falls short of realizing the potential – in both performance uplift and customer experience – of true one-to-one personalization.
The TrueAccord approach:HeartBeat, TrueAccord’s patented machine learning platform, mines through tens of millions of data points to optimize digital outreach on the individual level within a programmed set of compliance rules – and continues learning the more data it analyzes.
Challenge #2: Narrow, channel-specific use of machine learning
The challenge: Another challenge that banks face in scaling their use of intelligence – including artificial intelligence (AI) – is the limited deployment of algorithms and optimization within a single kind of channel, such as in a call center environment. The article profiles a collections and business process outsourcing company, for example, that developed an AI-based virtual assistant that can handle most inbound phone calls.
Why it matters: Machine learning and artificial intelligence (AI) are powerful tools for restoring intimacy and relevance to customer relationships at scale. At their most useful, these tools should be deployed to personalize the customer’s full experience with a bank – not just the limited interaction on one channel.
TrueAccord’s Approach: HeartBeat captures a continuous data feedback loop and optimizes for each customer touchpoint across a variety of digital channels, ensuring that each customer is being reached on the channel that is most relevant for her.
Challenge #3: Building a truly comprehensive and flexible self-serve portal
The challenge: Constructing a digital portal that drives consumer adoption and usage takes major work. To truly match the convenience of online banking, digital tools must also allow consumers to adjust the length and installment amount on a payment plan, defer a payment, dispute all or a portion of their debt, apply for a hardship pause on their debt, and much more.
Why it matters: Research suggests that customers want to be able to self-serve. But doing so requires the full, flexible range of interaction options that would be available to them through traditional analog channels.
TrueAccord’s Approach: Through a robust and flexible digital platform, TrueAccord offers a best-in-class self-serve experience: over 95% of users resolve their accounts without ever directly communicating with an agent.
Ultimately, digital debt collection technologies offer banks the ability to build lasting relationships with their customers. As Kimberly Snipes, consumer chief information officer at KeyBank puts it in the American Banker article: “We want our customers to say, I hate that I had that situation, but I felt like my bank was working with me, not against me.”
Being aware of the challenges on the path to digital debt collection – and having a plan in place to address them proactively – can help financial institutions ensure that they’re set up for long-term success.
TrueAccord is reinventing the relationship between creditors and lenders with a machine learning-driven, digital approach to debt collection. Our technology personalizes outreach to each customer across digital channels, continuously optimizing for performance while delivering a customer experience that builds long-term brand loyalty. Schedule a demo today to learn more.
Building scalable technology requires constant evaluation and improvement. Experimenting is defined by trying new things and creating effective changes that help teams to make informed decisions around product development. Trying new things creates momentum, and organizations that are driven by experimentation turn that momentum into growth.
Machine learning and artificial intelligence support large-scale, concurrent experimentation that helps these technologies to improve upon themselves. With the right tools in place, you can test a variety of scenarios simultaneously.
For example, we use our systems to track changes in the collection process and better understand how our digital collections efforts can be improved. Since digital-first channels offer thorough tracking and analysis, including real-time tracking on our website, we can learn in short cycles and continuously improve our product.
This kind of frequent experimentation helps to avoid making product development decisions based on untested hunches. Instead, you can test your instincts, measure them carefully, and invest energy where it matters.
Machine learning drives the experimentation engine
Aggregating historical data and processing it using machine learning algorithms and artificial intelligence helps you to understand their effectiveness. Regardless of how intelligent your learning algorithms may be, waiting to test and expand your knowledge base before marching blindly ahead can make or break the success of your product.
To launch an experiment, we follow these steps:
Start with a hypothesis that you want to test
Assign a dedicated team to manage the experiment
Monitor the performance of the test as it is guided by machine learning
B2B companies can benefit from partnering directly with clients to customize experiments for their unique product lines in order to make experimentation-based optimization an ongoing process for both new and existing business. Keep in mind that the goal of product optimization is not always jumping to the finish line.
Understanding how your product works ultimately offers you and your customers more value, but it’s easy to become distracted by positive outcomes. Effective, scalable products require intentional design; if you’ve accomplished a goal, but the path there was accidental, taking a few steps back to review that progress and test it can help you to get a clearer picture and grow the way you want.
Below are two sample experiments we conducted to optimize our machine learning algorithms.
Experiment #1: Aligning Payments to Income
The number one reason payment plans fail is consumers don’t have enough money on their card or in their bank account.
If you align debt payments with paydays, consumers are more likely to have funds available, and payment plan breakage is reduced.
We tested three scenarios: a control, one where we defaulted to payments on Fridays, and one where consumers used a date-picker to align with their payments with their payday. After testing and analysis, we determined that the date-picker approach was the most effective as measured by decreased payment plan breakage without negatively impacting conversion rates.
By understanding which payment plan system was the most effective, we were able to provide our AI content that offered these plans as options to more consumers and integrate the knowledge back into our systems and track those improvements at a larger scale!
Experiment #2: Longer payment plans can re-engage consumers
Customers dropped off their payment plans and stopped replying to our communications.
Customers can be enticed to sign up for a new plan if offered longer payment plan terms.
We identified a select group of non-responsive consumers that had broken from their payment plans and sent them additional text messages and emails. These additional messages offered longer payment plan terms than the plans they broke off from.
Ultimately, we found that offering longer payment plans, even with reference to the consumer’s specific life situations didn’t lead to an increase in sign-ups. The offers that we sent had high open and click rates but did not convert. This indicated that we were on the right track but needed to iterate and come up with another hypothesis to test.
This experiment was especially important because it illustrates that not every hypothesis is proven to be correct, and that’s okay! Experimentation processes take time, and the more information you can gather, the better your results will be in the future.
We’re able to simultaneously update our product and continue experimenting, thanks to algorithms called contextual or multi-armed bandits. Here’s what you need to know about these algorithms and how they help!
Building the newest, most innovative products feels exciting, but building without carefully determined direction can be reckless and dangerous. By regularly evaluating the effectiveness of machine learning algorithms, you can make conscious updates that lead to scalable change, and experimentation paves the way for consistent product improvement.
Machine learning algorithms are playing a key role in the collections industry’s technological growth. Companies are working to integrate artificial intelligence and machine learning into their strategies in response to changing regulations and evolving consumer preferences. These processes can look dramatically different from business to business!
Some technologies are being applied to optimize traditional call and collect strategies while others are building digital-first outreach platforms. Understanding how these algorithms are working for the industry can provide insight into the future of collections.
Business intelligence and analytics
Business intelligence platforms are the foundation for the future of collections. They not only help companies understand how to best reach their existing accounts using traditional collections strategies but also integrate into other digital tools to create powerful automated systems.
These algorithms process large sets of data such as call times, call effectiveness, the value of certain accounts, collections rates, and many other variables. By analyzing this information, teams can optimize their outreach strategies by focusing on accounts that are more likely to be collected on, understand what times of day or channels work the best, and even determine what language to use in conversation with specific subsets of accounts.
Portfolio evaluation and exchange
By adding a clear scoring system to business analytics tools, teams can share their portfolios in an online marketplace with other creditors and debt buyers in order to buy, sell, and even outsource debts as needed.
While debt marketplaces are not new, real-time scoring updates and activity insights provide a dynamic, cloud-based view into a fluctuating market.
Human-like contact center agents
As companies evaluate their data and optimize their outreach, they can also integrate digital agents to interact with consumers over the phone. Artificial intelligence software can be used to create human-like voices and personalized experiences for consumers.
These platforms can operate at scale more easily than sprawling call centers but still rely on a traditional call and collect model that consumers are shying away from. As consumer preferences shift toward digital channels, more machine learning tools can help to optimize for an omnichannel experience.
Digital collections platforms
Digital collections software is able to optimize performance data and leverage it using a diverse, multi-channel communication approach. Phone calls may be included as part of a larger strategy, but these platforms are primarily built around modern consumer channels including email, SMS, push notifications, and direct drop voicemails.
Contextual bandit algorithms take channel selection to a level beyond traditional A/B testing. Even if 10% of your consumers prefer one message type to another, it’s important to understand all of your audience’s preferences.
Digital channels integrate seamlessly with decision making algorithms and can optimize communications in ways that call systems cannot. For example, digital channels like email can reach consumers outside of hours typically limited by the TCPA.
25% of TrueAccord’s consumers access their accounts outside of the 9am to 9pm when traditional agencies cannot legally reach them.
Digital debt collection agencies
Each of these implementations of machine learning help to build a more personalized, more focused, and more forward thinking debt collecting experience for both consumers and creditors. One consistent factor that does limit their effectiveness is the need to build them into existing systems or alter processes at scale.
A collection agency that bears the consumer in mind and has a machine learning-driven, digital-first strategy removes this hurdle and enables a full-service, easy to use experience for both companies and consumers. With these technologies built into a team rather than a product or service, digital debt collection agencies can provide the services outlined above alongside a dedicated infrastructure and a team of technology experts.
Choosing the right tools and support for your company’s collection efforts is more important now than ever before, and understanding the options that are available can help you to future-proof your strategy before it’s too late.
Still have questions? Our team is happy to help make sense of what a digital-first collections agency can do. Set up some time to chat!
The term “multi-armed bandit” in machine learning comes from a problem in the world of probability theory. In a multi-armed bandit problem, you have a limited amount of resources to spend and must maximize your gains. You can divide those resources across multiple pathways or channels, you do not know the outcome of each path, but you may learn more about which is performing better over time.
The name is drawn from the one-armed bandit—slot machines—and comes from the idea that a gambler will attempt to maximize their gains by either trying different slot machines or staying where they are.
How do multi-armed bandits fit into machine learning?
Applying this hypothetical problem to a machine-learning model involves using an algorithm to process performance data over time and optimize for better gains as it learns what is successful and what is not.
A commonly used model that follows this type of structure is an A/B/n test or split test where a single variable is isolated and directly compared. While A/B testing can be used for any number of experiments and tests, in a consumer-facing world, it is frequently used to determine the impact and effectiveness of a message.
You can test elements like the content of a message, the timing of its delivery, and any number of other elements in competition with an alternative, measure them, and compare the results. These tests are designed to determine the optimal version of a message, but once that perfect message is crafted and set, you’re stuck with your “perfect” message until you decide to test again.
Anyone that works directly with customers or clients knows that there is no such thing as a perfect, one-size-fits-all solution. Message A, when pitted against Message B may perform better overall, but there is someone in your audience that may still prefer Message B.
Testing different facets of your communication in context with specific subsets of your audience can lead to higher engagement and more dynamic outreach. Figure 1 below outlines how a multi-armed bandit approach can optimize for the right content at the right time for the right audience rather than committing to a single option.
Rather than entirely discarding Message A, the bandit algorithm recognizes that roughly 10% of people still prefer it to other options. Using this more fluid model is also more efficient because you don’t have to wait for a clear winner to emerge, and as you gather more relevant data, they become more potent.
Following from Figure 1, digital collections strategies can determine which messaging is right for which consumer. Sorting this data in context can mean distinguishing groups based on the size or the age of the debt and determining which message is the most appropriate. In a fully connected omni-channel strategy, the bandit can take a step back and determine which channel is the most effective for each account and then determine messaging.
These decisions take time and thousands upon thousands of data points to get “right,” but the wonder of a contextual multi-armed bandit algorithm is that it doesn’t stop learning after making the right choice. It makes the right choice, at the right time, for the right people, and you can reach your consumers the way they want to be reached.
Machine learning is a powerful tool that many companies can use to their advantage. The ability to have algorithms make decisions based on large scale sets of data enables teams to build efficient, scalable tools. Some of these algorithms require frequent monitoring and management from data scientists in order to get up to speed and continue learning. Others are able to operate and learn on their own in order to generate new information to act on!
Supervised and unsupervised machine learning algorithms both have their time and place. Let’s discuss a few examples, the difference between the two, and how they can be used together to create a powerful, AI-driven strategy for your company!
Supervised Machine Learning
Supervised learning algorithms are trained over time based on foundational data. This data will provide certain features as data points that will teach the algorithm how to generate the correct predictions. Figure 1, below, provides an example of a binary classifier and a set of data about cats and dogs that will teach the algorithm how to identify one or the other!
These models function best in situations in which there is an expected, intentionally designed output. In the example above, the expected output is that the algorithm can properly separate cats from dogs. In digital debt collection, it may be separating accounts that will be easy to collect on from ones that are more difficult.
Classification vs. Regression
The models above are both examples of a supervised learning model that is seeking classification, but supervised learning can also be used to build regression models. The key difference between the two is that in a regression model the output is a numerical value rather than categorical.
A regression-based model may use input features such as income and whether or not they have children to accurately predict a person’s age. When using a regression based model in combination with consumer data, you can even segment demographics for communication and marketing.
For full transparency we want to state that TrueAccord does not use its customer demographic data for these purposes. This is strictly an example.
With proper supervision, these models will become more accurate over time, and the data scientists building them can adjust them as business needs change. Whether you are gathering data using a regressor or a classifier, it is dependent upon the data scientists to build the most effective inputs in order to get the “correct” output.
Unsupervised Machine Learning
While supervised models require careful curation in building proper features that will lead to the “correct” output, unsupervised models can take large sets of unlabeled data and identify patterns without aid. The output variables (e.g. dog or cat) are never specified because it is now the algorithm’s job to process and sort the data based on similarities that it can identify. Using this method, you can learn things about your data that you didn’t even know!
Clustering vs. Association
Just as supervised models have primary methods for training their output data as either classification or regression models, unsupervised models can be trained using clusters or associations. Clustering algorithms gather data into groups based on like-features that exist in the data set.
If you have thousands upon thousands of customer accounts in your system, a clustering algorithm can learn using the customer data and form them into distinct (but unlabeled) groups. Once it has assigned these clusters, data scientists can review the output data and make inferences such as:
This cluster is all of the accounts that have not yet established a payment plan
This cluster is all of the users that started signing up for a payment plan but didn’t finish the process
This new data set then provides the foundation for a new outreach strategy!
Association algorithms are the other end of unsupervised learning algorithms. Associations take the idea of grouping random data points one step further and can make inferences based on the data available. Continuing on from our account creation example, an association-based model can identify two data points and draw conclusions based on the patterns it finds. One such pattern may be:
A person that signed up for an account the first time they opened an email is more likely to pay off their balance.
The algorithm recognizes that multiple steps in a customer’s journey creates another data point. Because association algorithms are still unsupervised, a team of scientists will be responsible for labeling the output data, but the algorithm can outline previously unnoticed patterns.
The power of teamwork
By leveraging both supervised and unsupervised machine-learning algorithms, you can make decisions based on previously unfathomable scales of data. While they cannot necessarily be used to substitute one another, they can be used to create a perpetually improving cycle. Using unsupervised models to extract meaningful information from large data sets and building new supervised models to further hone your data creates more opportunities than ever before.
In a technology driven world, effectively gathering and acting on data-driven decisions is essential for success. A growing market of analytical tools combined with an exponentially expanding pool of accessible data means that companies can make more precise decisions than ever before. The realm of machine learning makes accessing and processing that data even easier.
Machine learning is a field of computer science and statistics focused on giving computers the ability to make decisions that they haven’t been explicitly programmed to make. By leveraging data to enable computer systems to make decisions, some of the biggest companies in the world are able to provide better experiences for their users.
The future of debt collection communication is digital, and what better to aid in digital efforts than powerful, adaptable computer models? Here’s what you need to know about machine learning and how it can change your (and your consumer’s) debt collection experience.
How can a machine learn?
Just as a person can learn by consuming more information on a subject, machine learning algorithms are able to learn by aggregating large data sets and identifying patterns, but they still require help getting started! When building a machine learning system, engineers and data scientists collaborate to establish parameters that help the model define data in a set that it can use to extrapolate from. Here’s an example:
A simple, supervised machine learning model known as a binary classifier can serve as a foundation for more complex decision making. Imagine a program that is designed to distinguish cats from dogs. The data scientists building the system know the difference between the two and can pick a few features that are likely to identify one or the other and break the qualitative information into quantitative values that the model can recognize.
Figure 1 (below) depicts how physical features of cats and dogs can be broken down into numbers or binary (Y/N) responses to help the computer model understand what features likely indicate a cat and which features likely indicate a dog!
Once the model has been trained using this data, it can learn what features are most likely correlate to a cat or a dog without the team telling it what to do! (See Figure 2, below).
The binary classifier described here is a supervised learning algorithm, meaning that it still requires designers to engineer its features in order to get it up and running.
Going beyond our cat and dog model, unsupervised machine learning models can aggregate data like this in order to make further predictions and decisions without human involvement!
Applying machine learning to debt collection
So your machine learning algorithm can now fairly reliably recognize the difference between a cat and a dog, but how can this process help in debt collection? When algorithms can slowly learn to distinguish results or users and place them into groups, they can learn to do things like:
Understand what kinds of messaging people respond to
Recognize what kinds of payment offers seem to be accepted
Define different types of consumers
With enough data to analyze and enough features extracted from that data, machine learning algorithms can help you to optimize collections processes. Rather than telling us “this is a cat” or “that is a dog,” a similar system could be used to make observations like:
“This type of account will be especially difficult to collect on”
“That consumer may like to receive fewer emails”
Or even something as specific as “this content might be more engaging for this consumer”
This information can help to inform new collections strategies, dictate the use of different communication channels, or provide further insights into effectively segmenting a customer base.
By crunching enormous amounts of information, an unsupervised machine learning model may be able to recognize patterns in groups that have similar preferences or needs and relay relevant communications to them based on that information!
If you’re interested in learning more about how machine learning can be harnessed to communicate with customers, check out this interview with two of TrueAccord’s data team!
Experimenting to learn more
One way to continue improving a machine learning model’s decision making ability is to provide it with more data and features to learn from. Perfecting a model requires a very scientific (and iterative) approach:
Start with a hypothesis that you want to test
Monitor what decisions it is making based on the data available
Introduce new information
Review how the system operates and what decisions it makes with the newly presented data
By experimenting with various tools and approaches, a debt collection-focused machine-learning model can work in conjunction with data teams to rapidly evolve and improve collections efficiencies at different stages of the collections process.
Machines learn and collections grow
As it becomes more and more difficult to contact consumers in debt, integrating digital collections solutions into a collection strategy is becoming invaluable. Digital debt collection offers more opportunities for in-depth analysis, and by introducing machine-learning to that evaluation process, you can build systems that can support their own growth and improvement!
The more data you have, the better you can collect, and the more you collect, the more data you have. The self-sustaining nature of machine learning is revolutionizing approaches to collections, but it isn’t as easy as it sounds. Building and continuing to maintain complex systems requires a talented team and a stable infrastructure that can support these processes at scale.
Those that can properly build and manage these systems will be the driving forces in the future of the collections industry, so find your partner and learn what you can. Maybe these machines can teach the industry a thing or two.
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.
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.