Category: Product and Technology

Q&A: Code-Based Compliance for Collections

By on September 27th, 2022 in Compliance, Industry Insights, Industry Interviews, Product and Technology

Just as technology has evolved leaps and bounds, so have consumer communication preferences, especially when it comes to debt collection. The Consumer Financial Protection Bureau (CFPB) recognized in Regulation F—rules updating the Fair Debt Collection Practices Act (FDCPA)—that consumers in debt want to communicate with debt collectors through digital channels, like email and SMS.

Under the FDCPA, Regulation F, and other state laws, these digital channels have the same compliance requirements as calls, such as no harassment or abuse, no false or misleading representations, and no unfair practices. Even though these additional channels have the similar compliance requirements, businesses must still manage these requirements across all channels and have the capacity to update requirements as new laws are passed, new cases come out, and new guidance is released from regulators causing a need to change in a compliance practice. How can businesses ensure compliance through the evolving regulatory landscape?

Code-based compliance is a critical component for the debt collection industry.

We interviewed five key stakeholders in this process to get different perspectives on what code-based compliance is and how it benefits businesses, lenders, consumers, and auditors. Read below for insights from: Eric Nevels, Director Operational Excellence; Hal Eisen, VP Engineering; Kelly Knepper-Stephens, Chief Compliance Officer and General Counsel; Michael Lemoine, Director Client Success; and Milo Onken, Director Quality Assurance.

What is Code-Based Compliance?

Eric Nevels: When an algorithm is used to help make decisions on consumer communications in debt collection, a code-based compliance system would be coded into that algorithm or work side-by-side with the algorithm to ensure that all digital communications fall within federal and state laws and regulations.

Michael Lemoine: Here’s an analogy to help explain code-based compliance: You lace up your new running shoes. You scoured all the online reviews and this pair provides the best ankle support. You ate a light but fuel packed breakfast, no mid run slump for you. You eyed the weather app on your phone, all clear and perfect temp. Hydrated, check. Headphones, check. Mood, great! You’ve got this, everything is under control and accounted for. Off…you… go!

Even if you’re not a big runner this sounds like a safe and productive way to start a day. But what if instead of checking for rain and eating a little oatmeal to make sure you had a good jog, you had to manually complete a full body diagnostic and perform microsecond electrical and chemical adjustments to your body just so you didn’t become disabled or even die while getting a little exercise? Not so safe and productive now. Is the risk of immediate death worth the effort and small reward of a single run?

Every second your body automatically, without thought or effort, reads your current condition and reviews thousands of risks and initiates controls, responses, and actions to keep you alive—called the autonomic nervous system. Code-based compliance is the autonomic nervous system of an organization’s risk and control program. Now, it’s not as dramatic as life and death, but code-based compliance can supercharge any compliance management system because once the code has been programmed and deployed the system always follows the programmed rules leading to consistency and accuracy.

How is Code-Based Compliance Different From More Traditional Approaches to Compliance?

Eric Nevels: In the absence of code, human beings would need to check against the various restrictions on communications. Anytime humans are involved, even with rules and procedures in place, it is possible for errors to occur. With a code-based system, it is impossible for that action to take place.

Kelly Knepper-Stephens: Certainly it’s better than manual compliance because with manual compliance you have an opportunity for human error. But it doesn’t mean that code-based compliance is “code it and forget it.” Your coders need a process to quality check the code. And your compliance team or a front line control team needs to monitor to make sure the coded compliance rules are working as you intended them to work.

How Does This Approach Benefit Collection Compliance Strategies?

Hal Eisen: Code-based compliance is great because it never gets tired or distracted and is not subject to any of the other human frailties. Done correctly, it can be efficiently applied to a wide range of software products without needing additional investment. Most compliance rules were written for the benefit of consumers. The better we comply, the safer consumers are. Consumers should have accurate disclosures, fewer annoying interactions and feel better about the whole experience.

Eric Nevels: Lowers operational risk and ensures compliance with regulations. Additionally, it is much easier to update the code when regulations are changed. It helps ensure that they are being treated within the bounds of the law, which is their benefit.

Milo Onken: The code-based approach ensures accuracy and tangible evidence for compliance audits. Collaboration with different internal teams and Legal ensures we check, implement, and follow industry compliance directives.

A Code-Driven Future for Debt Collection

Code-based compliance offers predictable and consistent collections methods when coupled with digital platforms. New technology can be mistaken as a risky investment, but digital debt collection systems offer more compliance security and more transparency—for consumers and creditors. Digital collection solutions not only evolve to meet consumer needs, but they can also continually adapt to changing regulations and quickly meet compliance requirements.

Beyond code-based compliance, what are compliance issues unique to collections that need to be front of mind when sending digital communications to effectively engage your customers?

Join us Thursday September 29th at 1pm ET for our interactive webinar, The Future of Collections & Compliance, hosted by TrueAccord Associate General Counsel Lauren Valenzuela and Director User Experience Shannon Brown.

Reserve your space now for an interactive discussion on:

  • Cutting edge digital collection compliance
  • The role of the legal team in creating a digital collection strategy
  • How compliance drives collection revenue
  • The future of digital compliance

Register now for the upcoming webinar»»

*This blog is not legal advice. Legal advice must be tailored to the particular facts and circumstances of each unique matter.

What Makes an Effective Compliance Strategy for Collections?

By on September 16th, 2022 in Compliance, Industry Insights, Product and Technology

Creating an effective compliance strategy is a crucial component of a business’s chance of success. Debt collection is highly regulated and must adhere to different regulations and laws like the FDCPA, Regulation F, and unique state laws—including regulations that may not be specifically focused on debt collection but still apply to the practice. Noncompliance with laws and regulations that govern or even parallel an industry can result in unhappy customers, litigation, reputational risks and/or enforcement actions.

Using a high-level overview of what an effective compliance strategy can look like, this article will help outline how to create a compliance management system to help your business mitigate risk and keep your customers happy.

What are the key elements to create a compliance strategy for collections?

Some of the key elements to an effective collections compliance strategy may seem like no-brainers but can be more complex than you realize. Being aware of what laws and regulations apply to your specific business, industry, state, and even local jurisdictions is a critical element. Equally, internal audits to make sure your business’ processes are working as intended is a great way to get a temperature check on your compliance’s health. Internal audits should be conducted on a routine basis.

Additionally, due diligence should be conducted on any third-party servicers you may work with for debt collection and recovery purposes: make sure they are legitimate, law-abiding, consumer-respecting businesses. For example, a great way to verify you’re working with a reputable debt collector is by searching the Receivables Management Association (RMAi) database. If a company is RMAi certified, that means they have passed and/or comply with the organization’s rigorous background checks, industry standards and best practices guidelines.

Beyond what can feel like the no-brainers of compliance strategy, another key element is having a Compliance Management System.

What is a Compliance Management System and what does it cover?

From a high-level view, a compliance management system (CMS) is how a company sets, monitors, and oversees its compliance responsibilities. The CFPB describes a CMS as how an institution:

  • Establishes its compliance responsibilities
  • Communicates those responsibilities to employees
  • Ensures that responsibilities for meeting legal requirements and internal policies and procedures are incorporated into business processes
  • Reviews operations to ensure responsibilities are carried out and legal requirements are met
  • Takes corrective action and updates tools, systems, and materials as necessary

What are the components of a Compliance Management System?

  • Board Management and Oversight
    • Allocate the right resources to compliance and risk management
    • Regular Board of Directors reporting
  • Policies and Procedures
    • Documented and updated at least annually by the business owner
    • Detect and minimize potential for consumer harm
    • Reviewed by Audit and Compliance to ensure followed and meeting requirements
  • Risk Assessment – Controls & Corrective Action
    • Documented and evaluated regularly by the business owner
    • Reviewed by Audit & Compliance to ensure mitigating risks and control gaps
    • Deficiencies remediated by business owner through corrective action plans
  • Training
    • Consistent with policies and procedures
    • Ready before a change or roll-out
  • Consumer Complaint Response
    • Recorded and categorized – used to improve processes
    • Investigated, prompt responses provided, corrective action
  • Monitoring & Audit
    • Aligned with risks
    • Independent – reporting shared with top management

Why is a Compliance Management System important?

A compliance management system is important because it’s the checks and balances of the business you’re operating. One of the most important parts of a CMS are the policies and procedures—these help to manage risk by setting a framework and infrastructure to proactively and reactively respond to incidents, issues, and change, such as:

  • Changing product and service offerings
  • New legislation, regulation, interpretations, court decisions, etc. that address developments in the marketplace and are relevant to the product and service offerings of the organization
  • Unexpected incidents (data breach, global pandemic, etc.)

How can you ensure your compliance strategy is effective?

A compliance strategy is not “set it and forget it”—the strategy needs to be tied to the evolving consumer preferences and corresponding new compliance requirements to be effective. This helps businesses be proactive versus reactive. Ensuring checks and balances are in place helps establish proactive stance in case normal policy fails, gaps are discovered, or other unforeseen issues arise.

What can you do to ensure compliance strategy is effective for the future?

Want to learn more about the different facets of what makes a compliance strategy effective in collections? Join us Thursday September 29th at 1pm ET for our interactive webinar, The Future of Collections & Compliance, hosted by TrueAccord Associate General Counsel Lauren Valenzuela and Director User Experience Shannon Brown.

Reserve your space now for an interactive discussion on:

  • Cutting edge digital collection compliance
  • The role of the legal team in creating a digital collection strategy
  • How compliance drives collection revenue
  • The future of digital compliance

Register now for the upcoming webinar»»

*Leana serves as TrueAccord’s Paralegal Operations Analyst II. This blog is not legal advice. Legal advice must be tailored to the particular facts and circumstances of each unique matter.

How Buy Now, Pay Later is Transforming Online Shopping With Gen Z

By on August 24th, 2022 in Industry Insights, Product and Technology
How Buy Now Pay Later is Transforming Online Shopping With Gen Z

Buy Now, Pay Later (BNPL) plans have taken over as a popular financing option for consumers, partly due to an increase in online shopping demands during the pandemic. In 2021, Americans spent more than $20 billion through BNPL services, taking up a bigger part of the $870 billion-a-year online shopping market. From laptops and airline flights to clothing and furniture, BNPLs make it simple to pay for almost anything in small installments. Since the start of the pandemic, millions of international consumers, especially Gen Z (10-25 years old), have gravitated toward using this service. According to a study by Forbes, BNPL use among Gen Z has grown 600% since 2019. The rise of interest in BNPL is also likely influenced by increased financial uncertainty, high-interest rates and a downward trend in credit card approval. As consumers show preference for digital financial services, BNPL continues to grow and become available at more retailers. 

Why are BNPLs Popular with Gen Z?

Services like Afterpay, Klarna, Affirm and others have gained a lot of popularity in recent years, especially among younger generations who may struggle with cash flow. With BNPL, the first payment is due at the time of purchase, with subsequent interest-free payments usually due within a few weeks or months. 

More and more, BNPL providers are reaching these younger audiences through influencers and brands on TikTok, and the variety of goods and services you can purchase with the service continues to expand. Some popular buy now, pay later items include clothing, concert tickets, cosmetics, electronics, furniture, groceries, hotels and flights.

But, like credit cards, missing payments can result in late fees and other penalties. With Gen Z, there’s already a pattern of missing payments. A survey conducted by Piplsay showed that 43% of Gen Zers missed at least one BNPL payment in 2021. 

Gen Z Favors BNPL More Than Other Generations

Debt types and payment preferences constantly change along with technology. The traditional credit card debt is being replaced by BNPL, specifically when we look at Gen Z. For one, it’s easier to be approved for a BNPL application since the process only requires a soft credit check, unlike a hard credit check that most credit card issuers require. When looking for an alternative to high-interest credit cards, BNPL installment payment plans are a popular option. BNPL consumers know upfront what will be expected of them, and the possibility for large debt build-up is replaced with a finite number of payment installments. This transparency and manageability make it easier to understand. And it’s one that has the potential to continue to evolve for the better by providing consumers with more inclusive credit and payments options.

When it comes to both luxury and essential purchases, younger consumers are more likely to take advantage of BNPL to afford them. A survey from TrustPilot found that 45% of consumers between the ages of 18 and 34 were likely to use such services for basic essentials while 54% would use them for luxury items. For those aged between 34 and 54, these results were 33% and 38% respectively. And for people aged 55 and up, the results were 16% and 24%. 

Since it’s quite easy to sign up for one or more BNPL loans, the likelihood of losing track of payments or overspending is real, especially for Gen Z. According to a report from J.D. Power, about one-third of younger consumers said they spent more than their budget allows with BNPL. And since different retailers offer financing through various BNPL services, it can also be a challenge to track multiple accounts at once. This isn’t surprising as some of the younger generations do not have the financial literacy or experience that older generations have and they’re more likely to face consequences and penalties like missing a payment.

Meet Gen Z Where They Are to Effectively Recover More

The good news is that the outlook for Gen Z BNPL customers that end up with accounts in collection is different than for those who default on credit card debt. On average, BNPL debts see higher and faster repayment rates than similar-sized credit card debts. Higher engagement leads to better repayment rates. According to TrueAccord data, the percent of BNPL customers who make a payment is more than double the like-size credit card accounts at 30 days post placement and 50% higher at 90 days. 

As a debt collection platform that engages digital-native consumers where they are and with a priority on customer experience, many leading BNPL providers partner with TrueAccord to address both early delinquencies and charged-off accounts. After these BNPL customers repay their loans and have a positive experience, they’re able and likely to use the service again, and this time with some experience about how it works. By using this information, TrueAccord can help find the most optimal ways to reach the younger audience and help them pay off their debt from BNPL. 

Want to learn more about how to engage with consumers of any generation in whatever stage of collection they might be in? Schedule a consultation to see what TrueAccord’s digital solutions can do for your debt recovery strategy. 

Further Reading: 

TrueAccord Digitally Serves 20 Million Consumers on Path to Financial Health

By on July 12th, 2022 in Company News, Customer Experience, Machine Learning, Product and Technology

With more than 20 million consumer accounts serviced through intelligent, digital-first collections products, results show better repayment and happier customers than “call to collect” agencies

LENEXA, Kan., July 12, 2022 — TrueAccord Corp, a debt collection company using machine learning-powered digital recovery solutions, today announced that it has served more than 20 million customers in debt with a digital-first experience. TrueAccord’s customer-centric approach and commitment to creating a positive consumer experience is reflected in its 4.7 Google customer satisfaction rating, customer feedback, and an A+ rating with the Better Business Bureau.

TrueAccord’s collection solutions harness machine learning and digital-first communications to deliver a personalized, consumer-friendly experience for those in debt. As is the nature of machine learning, the system dynamically analyzes and refines the approach used for each customer based on their interactions combined with years of previous engagement data in order to deliver the most effective communication treatment. The patented system, HeartBeat, which is now 20 million customer engagement interactions strong since its 2013 inception, continues to optimize with each new customer interaction.

“Machine learning is only as good as its data sources, and with more than 20 million accounts’ worth of engagement data that informs the HeartBeat system, we’re confident that the experiences being delivered are as streamlined and as aligned to consumer preferences as possible,” said Mark Ravanesi, CEO of TrueAccord Corp. “As a mission-driven company, we prioritize creating better experiences for consumers in debt, and based on our high customer satisfaction and repayment rates, it looks like we’re making significant progress.”

Powered by TrueAccord’s industry-leading tech stack, the product suite includes Retain, a client-branded early-stage consumer engagement platform for managing pre-charge off debt, and Recover, a full-service debt collection solution. Key benefits of both products include a simple, intuitive and effortless-to-use digital platform leading to great user experience, constant A/B testing and optimization to reduce friction and boost conversion rate, infinite scalability, and second-to-none channel deliverability. 

While holding customer experience as a priority, TrueAccord products continue to prove more effective than competitors, as evidenced by client case studies showing 25-35% better performance on accounts using Recover when compared to those placed with traditional agencies, and recovering $17 million in delinquent bills with a 44% paid in full rate using Retain.

To learn more about TrueAccord and its digital-first recovery solutions, visit www.TrueAccord.com and follow on Twitter and LinkedIn.

About TrueAccord

TrueAccord is the intelligent, digital-first collection and recovery company that leaders across industries trust to drive breakthrough results while delivering a superior consumer experience. TrueAccord pioneered the industry’s only adaptive intelligence: a patented machine learning engine, powered by engagement data from over 20 million consumer journeys, that dynamically personalizes every facet of the consumer experience – from channel to message to plan type and more – in real-time. Combined with code-based compliance and a self-serve digital experience, TrueAccord delivers liquidation and recovery rates 50-80% higher than industry benchmarks. The TrueAccord product suite includes Retain, an early-stage recovery solution, and Recover, a full-service debt collection platform.

Elevate Your Collection Strategy with Machine Learning and HeartBeat

By on July 7th, 2022 in Industry Insights, Machine Learning, Product and Technology

Debt recovery and collection look quite different in 2022 than it did ten, five, even just a year ago: new channels to reach consumers, larger data sets to analyze, complex regulations that can vary state by state, and so much more.

So when it comes to deciding the best way to engage consumers and effectively recover debt, has your strategy evolved to keep up? Machine learning, artificial intelligence, data science—these terms are thrown around a lot, and for good reason.

But how does it tactically improve the experience for both lenders and members?

Decoding Machine Learning for Debt Collection and Recovery

To help decipher real differences between a machine learning strategy versus the traditional call-and-collect, we have designed a highly visual guide to cut through the jargon and help you understand the basics of machine learning in collections. Decoding Machine Learning for Debt Recovery and Collection provides straightforward definitions, clear diagrams, and bottom line benefits make this eBook your at-a-glance guide to machine learning in debt collection.

Download your complimentary copy of the new eBook Decoding Machine Learning for Debt Recovery and Collection here»

From delivering a better experience in-line with what consumers expect from businesses to streamline communications, machine learning has gone from a “nice to have” to a “must have” for collection efforts.

Upgrade Debt Recovery & Collection With HeartBeat

Although this type of technology is a step in the right direction, it’s only one step forward—your debt collection strategy can go even further with TrueAccord’s patented decision engine, HeartBeat.

Integrating machine learning into your practice is certainly important—but how does this technology know what the best choice is to engage all of your delinquent accounts now and in the future?

Say hello to HeartBeat, our intelligent decision engine, and say goodbye to missed debt recovery opportunities left on the table by basic machine learning models. See exactly how HeartBeat upgrades your collection strategy in our new eBook, Upgrade Debt Recovery & Collection With HeartBeat—the more in-depth companion piece to our visual guide to machine learning (detailed above).

While HeartBeat utilizes machine learning in its decision-making process, it is not limited to it. This decision engine is continuously evaluated for performance, and adjusted to align with the current economic situation, changes in consumer behavior, and updates to compliance rules.

If you are switching from a more traditional outbound approach then a basic machine learning model can provide a short-term lift in recovery rates, but will hit a dead end when it comes to optimizing, adapting, and improving over time. HeartBeat is set up for the long game and recovers more because of it.

Download your complimentary copy of the new eBook Upgrade Debt Recovery & Collection With HeartBeat to learn how to start recovering more»

Elevate Engagement, Recover More

Together, these two companion eBooks, Decoding Machine Learning for Debt Recovery & Collection and Upgrade Debt Recovery & Collection With HeartBeat, serve to be the ideal introduction into machine learning in debt collection and then a deeper look beyond the basics to see what even more advanced technology can do for your recovery operation.

Discover how an intelligent, digital-first collection strategy drives overall improved performance, better member experience, and the more effective recovery of delinquent funds—without implementing more manual processes or adding headcount to your team.

Schedule a consultation today with one of our experts today to learn more about how you can elevate your debt collection practice today.

Bridging the Gap Between Machine Learning and Human Behavior with HeartBeat

By on June 27th, 2022 in Industry Insights, Machine Learning, Product and Technology

When it comes to engaging consumers in debt collection, behavioral science helps us to understand and respond to an individual’s situation, motivations, and contact preferences.

For example, we know that consumers don’t like being called by debt collectors. With that knowledge, behavioral science then helps us determine the optimal way to meet consumers where they are, contact them when they want by using their preferred channels, and lastly sending a message that resonates with them enough to engage.

It starts with a lot of engagement data. At TrueAccord, we’ve been collecting data about how consumers engage with us for nearly 10 years to determine the optimal ways to engage with consumers, and then use that data to generate a special collections experience just for them – from best time, best channel, and best language to engage that individual consumer.

Take content for example, each person is unique and different people respond differently to different communications. They are driven to action by different words and are convinced for different reasons. In writing content, it’s our goal to write content that responds to these individual needs.

There are 2 things we consider when writing content for collection communications: content type and content dimensions.

  • Content type is what we send based on a user’s actions. As an example, following up on a page view—if a consumer is viewing a payment plan, disputing a debt, or thinking about unsubscribing but drops off the page, we will try sending a follow up email or SMS with other plan options, information about the account, a description of how to unsubscribe or dispute for them to view. We will continue to try different content types until we find the right one that engages the consumer.
  • Content dimensions are more established behavioral science frameworks used to ensure that our communications vary in style and tone so that we are speaking to consumers in a unique way that will motivate them to engage. Everyone responds to different motivations so we use a variety of different frameworks until we find the one that connects with the individual

Each piece of content is tagged depending on the content type and dimensions so it can be easily used by HeartBeat, our powerful and intelligent patented-machine learning engine. Good content will lend itself well to automated, data-driven prioritization done by HeartBeat to present customers with the best possible content item at each given time.

How can technology personalize the debt collection experience?

By using technology and behavioral science to determine the best way to communicate with consumers, we are able to personalize each user’s unique experience. Our patented machine learning engine mentioned above, HeartBeat, allows us to do just that. HeartBeat collects engagement data and then, after analyzing multiple solutions, suggests the best possible treatment depending on the individual and their engagement. HeartBeat also uses a real-time feedback loop so the technology can adapt to a consumer’s engagement right as it happens.

Instead of relying on data like age and location, HeartBeat uses engagement data to personalize the communication process. The engagement data is collected every time a consumer engages in a certain way, whether it’s clicking on an email or SMS, visiting a webpage, and/or viewing payment plan options. Our system learns what motivates the consumer and responds with content or payment options that will resonate with them.

For example, if the consumer clicks on an email that uses likable content mentioning “short term cash flow,” our system may determine to send a friendly follow up email letting them know that they can set up a payment plan that starts on a later date when they’ll have the ability to make a payment. We know what motivates an individual may change from day to day depending on their circumstances, so we treat them based on their active engagement and behavior with our system rather than construct a specific profile for each consumer and treat them based on that basic account profile.

By combining behavioral science and machine learning, the best-possible payment options are offered to customers based on their debt situation, previous communication, and engagement data. Whether their actions show that they would benefit from a long-term payment plan, or if it shows that they’d prefer to pay in full, HeartBeat will suggest the best option for that customer. The power of using behavioral science and machine learning is anticipating the needs and preferences of our customers and using that to help them as seamlessly as possible.

Overall, there is no one way of communicating that will work for everyone across all situations, and tailoring communication and collection strategy to align with consumer preferences is better for both the consumer, lenders, and our business. That’s why building the bridge between machine learning and human behavior is essential.

Discover how HeartBeat can help humanize your collection process in our new in-depth eBook, Upgrade Debt Recovery & Collection With HeartBeat, available for download here»

Learn more about behavioral science from TrueAccord’s User Experience Director, Shannon Brown in a new interview in Collector Magazine here»

Technology in Collection: 21 Must-Know Buzzwords

By on May 24th, 2022 in Industry Insights, Machine Learning, Product and Technology

Your Guide to Key Terms for Today’s Debt Recovery Strategy

Reaching consumers today requires a more sophisticated process than simply dialing the phone or sending a generic email, especially when it comes to debt recovery and collection. But reviewing potential strategies can often leave you lost in a sea full of acronyms and buzzwords. Between terms like AI, machine learning, and data science, it can be difficult to keep up with the different definitions—and understand how they impact your business and bottom line.

To help keep this word salad straight, we’ve compiled a glossary of helpful terms, definitions, and examples to help differentiate them:

  • Accounts per employee (APE), account to collector ratio (ACR): The number of delinquent accounts that can be serviced by an individual recovery agent – often used to measure cost effectiveness.
  • Artificial Intelligence (AI): AI is a blanket term describing a range of computer science capabilities designed to perform tasks typically associated with human beings. Machine learning (ML) is a subset of AI. Through AI, processes like debt collection can become more efficient by developing better outreach and deployment strategies.
  • Big Data: This term means larger, more complex data sets . Big data can save collectors a lot of time by using many variables for analytics-based customer segmentation, insert, insert..
  • Coverage: The percentage of users for whom organizations have digital contact information, such as email addresses or phone numbers.
  • Customer Retention Rate: Measures the total number of customers that a company keeps over time. It’s usually a percentage of a company’s current customers and their loyalty over that time frame.
  • Data Science: ‍A cross-discipline combination of computer science, statistics, modeling, and AI that focuses on utilizing as much as it can from data-rich environments. Data science (which includes machine learning and AI) requires massive amounts of data from various sources (customer features such as debt information or engagement activity) in order to build the models to make intelligent business decisions.
  • Deep Learning (DL): A subset of machine learning. Deep learning controls many AI applications and services and improves automation, performing analytical tasks with human intervention.
  • Delinquency rate: The total dollars that are in delinquency (starting as soon as a borrower misses as a payment on a loan) as a percentage of total outstanding loans.
  • Deliverability: The percentage of digital messages that are actually reaching consumers (e.g., as opposed to ending up in email spam filters).
  • Digital engagement metrics: A range of KPIs that capture how effectively digital channels are reaching and engaging consumers.
  • Digital opt-in: The percentage of users who have indicated their preference to receive digital communications in a particular channel.
  • Efficiency: Measures a company’s ability to use its resources efficiently. These metrics or ratios are at times viewed as measures of management effectiveness.
  • Machine Learning (ML): Technology that uses algorithmic modeling techniques to observe patterns and trends, reassessing the best approach to achieve a goal, and adapting behavior accordingly. It continuously, automatically learns and improves at a massive scale as more data is observed. With the help of machine learning, companies can make sense of all their data and take on new approaches to debt collection processes from better customer experience to more efficient delinquent fund recovery.
  • Net loss rate: The total percentage of loan dollars that get charged off (written off as a loss).
  • Open rate, clickthrough rate: The percentage of users who are actually opening and clicking digital communications.
  • Predictive Analytics: Predicting outcomes is one specific application of machine learning. It allows companies to predict which accounts are more likely to pay sooner and allows them to better plan operations accordingly.
  • Promise to pay kept rate: The percentage of delinquent accounts that maintain a stated commitment to pay.
  • Promise to pay rate: The percentage of delinquent accounts that make a verbal or digital commitment to pay.
  • Right party contact rate: The rate at which a collections team is able to establish contact with the consumer associated with a delinquent account.
  • Roll rate: The percentage of delinquent dollars that “roll” from one delinquency bucket (e.g., 60 days past due) to the next (e.g., 90 days past due) over a given timeframe.
  • SMS: An acronym that stands for “Short Message Service” referring to text messages on cellular devices.

For more information on how to get started integrating innovative technologies into your debt recovery strategy, schedule a consultation today.

Sila Offers Customers Digital Debt Collection Services through New TrueAccord Partnership

By on February 7th, 2022 in Company News, Product and Technology
TrueAccord Blog

PORTLAND, Ore., (February 7, 2022) – Sila Inc., a fintech software platform that provides payment infrastructure as a service, today announced that it partnered with TrueAccord, the leading debt collection company offering intelligent, digital-first collection and recovery solutions, to make it easier for Sila’s customers to use TrueAccord’s products and services. How to deal with delinquent and defaulted accounts is a key element that fintechs need to have in place as part of their overall management of funds. Using a patented machine learning engine and engagement data from millions of customers, TrueAccord delivers a personalized, self-serve experience that drives consumer engagement and industry-leading results. Meeting consumer preference for digital-first services and to cut through the noise and empower customer self-service and inbound communication, TrueAccord uses a range of channels including email, SMS, voicemail drop, and more.

Since its inception, Sila has been laser-focused on providing industry-leading API solutions. As importantly, Sila has been steadily growing its partner network to augment its offering by anticipating additional functionality that Sila customers will need to successfully build their businesses. With the recent addition of TrueAccord, Sila is on path to have agreements with over 40 specialist service providers signed by the end of this quarter.

“Sila is proud to welcome TrueAccord as a partner. We know that our customers will benefit from this key addition to our partner network and from a closer relationship between our two organizations,” said Shamir Karkal, CEO and co-founder, Sila Inc. “Like many of our fintech customers, TrueAccord was founded by an individual who had a sub-optimal experience with a traditional financial institution and decided to do something about it. That’s a mindset that is very close to our own because we started Sila around the idea to provide payment services that allow entrepreneurs to build the new financial world they have in mind.”

“We have worked with more than 16 million consumers on their journey to pay off their debts, and we use that data and feedback to understand how and when to best engage consumers to facilitate repayment. By allowing consumers to create flexible payment plans and by offering modern, digital-first communication channels, we are changing the landscape of debt collection from hostile and harassing to empathetic and helpful,” said Mark Ravanesi, CEO of TrueAccord Corp. “We are looking forward to bringing to bear our significant expertise for the benefit of Sila’s customers and consumers.”

About Sila

Sila is a fintech software platform that provides payment infrastructure as a service, a business-critical element for all companies that need to integrate with the US banking system and blockchain quickly, securely, and in compliance with applicable US regulation. Sila offers Banking, Digital Wallet & ACH Payments APIs for Software Teams. The firm was recognized as a ‘2021 best place to work in financial technology’. Sila is headquartered in Portland, Oregon. For more information go to www.silamoney.com

About TrueAccord

TrueAccord is the intelligent, digital-first collection and recovery company that leaders across industries trust to drive breakthrough results while delivering a superior consumer experience. TrueAccord pioneered the industry’s only adaptive intelligence: a patented machine learning engine, powered by engagement data from over 16 million consumer journeys, that dynamically personalizes every facet of the consumer experience – from channel to message to plan type and more – in real-time. Combined with code-based compliance and a self-serve digital experience, TrueAccord delivers liquidation and recovery rates 50-80% higher than industry benchmarks. The TrueAccord product suite includes Retain, an early-stage recovery solution, and Recover, a full-service debt collection platform.To learn more, go to http://www.trueaccord.com.

How TrueAccord Embraces Machine Learning to Create Positive Consumer Experiences in Debt Collections

By on December 23rd, 2021 in Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

By Laura Marino

TrueAccord’s Chief Product Officer, Laura Marino, was recently featured in the New Standard in Debt Collection panel as part of the Beyond Digital: The Next Era in Collections summit. As a civil engineer turned product management executive, Laura has a unique viewpoint on the evolution of machine learning in software across a variety of industries. In this blog post, Laura shares her perspective on machine learning at TrueAccord and in collections, in general.

At TrueAccord, we know that consumers prefer digital channels and self-service. We also know that just providing the digital channels is not enough. To truly engage with consumers we need to help them throughout the journey. This is where machine learning comes in.

What is machine learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In the context of collections, and specifically in the context of our consumer-centric approach to collections, machine learning is a wonderful tool to personalize the experience for each consumer, effectively engage with each of them, and ultimately help resolve their debt.

There has been so much hype around machine learning, but often companies that claim to do ML are really using fixed rules or heuristics (if a consumer does X, then do Y) without including any of the automatic learning and improvement. Or they may be using ML for a very specific, very limited scope – like automating some consumer support responses. The reason that leveraging ML is so difficult for something as complex as collections and recovery is that it requires a lot of expertise in data science and behavioral science, it requires a lot of user research, and it requires a lot of data.  This is not something that a company can decide to start doing overnight as an add-on.

How does TrueAccord apply machine learning to debt collection?

TrueAccord is leveraging machine learning and behavioral science throughout the entire journey, from initial engagement all the way to resolution. We were built specifically around the hypothesis that focusing on machine learning-driven, digital-first experiences was the way to transform debt collections. We have been doing this since 2013, and we have orders of magnitude more data than anyone else. Just to give you an idea: we send millions of emails per day, and hundreds of thousands of text messages per week and our ML engine learns from every open, every click, every action on our website, and every interaction with our call center agents. Because of all of this, we have something that is very hard for anyone to imitate.

Unlike traditional collections, we do not use demographic data like age, zip code, or creditworthiness to personalize the experience. Instead, we use engagement data about how the consumer responds at every step in the process.  

We have handled debts for over 24 million consumers and we have collected data about each individual interaction with those consumers. That wealth of data, combined with our ongoing user research is behind the ability of Heartbeat (our fully automated and reactive decision engine) to personalize the experience for each consumer.  We’ve seen this data-driven machine learning customer-centric approach lead to increased customer satisfaction, better repayment rates, and lower complaint rates.

Machine learning is used to personalize and optimize every step of the customer journey. The first thing we need to do is to effectively engage with the consumer.  For that we have several models: 

  • Cadence optimizer: determines the right cadence to communicate with each consumer about their debt. Specifically, it determines which day to send the next communication. We don’t have a fixed rule that says “send an email every x days.” Our decision engine decides it dynamically based on the type of debt, the consumer behavior, and where they are in the process. 
  • Send time optimizer: determines when during that day, communication should go out. A working mother who is busy with her kids in the morning and in the evening is more likely to check her messages in the middle of the day during her lunch break. A construction worker has a very early start to their day, may prefer to check messages at the end of the day.  We want our consumers to receive our communications during their preferred times so that they are at the top of their inbox and not buried under 50 other emails. Reaching people at the right time of day has a big impact. Due to our send time optimizer, we saw a 23% increase in liquidation for certain types of debts. 
  • Email content rater: we also want to make sure that the tone of our communication is one that will best resonate with a specific consumer. For each piece of content we send out, our content team has created multiple versions with different voices, ranging from very empathetic to more ‘to the point’ because different people respond to different styles. Heartbeat chooses which one to send based on what it has learned from the behavior of each consumer. 

After engaging the consumer with the right cadence, timing, and content we want to make sure that they commit to a payment plan and stick to it until their debt is resolved. For that, we have machine learning models that determine the best combination of discount and length of payment plans to offer to each consumer. The options that the consumer sees when they get to the payment plan page are tailored to them based on what Heartbeat believes will work best. The consumers can build their own plan but, if we can proactively offer options that work, we make it easier.

We also have a ‘payment plan breakage model’ that helps us identify consumers who are at risk of not making a payment so that we can proactively reach out to them and give them options. With this we were able to increase the resolution rate among customers at risk by 35%.

What do customers think about TrueAccord’s model?

We have a lot of very positive feedback from our consumers which I attribute very much to our machine learning capabilities. It is one of the things that I think is so exciting for everybody who works at TrueAccord. We consistently get messages saying, “Thank you for making it so easy. Thank you for allowing me to do it via digital channels without having to talk to anybody.” And then when people call with questions, our call center knows that they’re there to help. People definitely respond very positively to the approach we’re taking to collections.

This content originally appeared as part of the Beyond Digital: The Next Era in Collections summit. Watch the entire summit here. 

Building a Digital-First, Third-Party Collections Solution with Snap Finance

By on August 13th, 2021 in Industry Insights, Machine Learning, Product and Technology

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.