Code Driven Compliance is the Future for Debt Collection

By on July 17th, 2017 in Compliance, Industry Insights, Machine Learning

Debt collection is a highly litigated activity. Compliance personnel and systems budgets are crowding out other investments. It’s appropriate: debt collectors and creditors are often hit by class action lawsuits and government action, leading to huge fines and settlements. Reducing risk is their primary priority. When examined closely, though, the traditional debt collection model attracts numerous compliance issues. The legacy approach is being replaced by  machine learning and digital first systems. These code-controlled systems offer predictable, scalable, and auditable operations that, coupled with best in class user experience, significantly reduce the risk of litigation and regulatory action.

The traditional model invites regulatory scrutiny and lawsuits

Collectors often cite compliance concerns as impediment to adopting new technologies. Lawyers are concerned about TCPA exposure from text messaging, consent requirements for emails, and FDCPA violations when using social media. These concerns are unfounded: text messages can be safely delivered if consent and revocation are properly documented, the CFPB saw no need for consent to email (as reflected by a growing body of opinions, as well as its 2016 rule outline), and social media can be used with restraint. While dragging their feet on evaluating new technologies, compliance departments embrace and perpetuate much bigger risks: the prevalent use of human labor, over reliance on phone calls, and the outdated, fragmented interfaces used by collectors.

Humans are the weakest link in the compliance chain

Traditional wisdom says that only people collect from people. That claim is demonstrably false. People are subject to biases and acting emotionally when interacting with debtors – which is why machine learning based systems collect better than humans. People may be tired, angry, or distracted. They can be baited into violating the FDCPA by a ill-meaning debtor. The prevalent commission-based compensation model, a broken and outdated model for collections, puts them in odds with debtors whenever they interact. Human beings just cannot do error-free work, no matter how trained or experienced they are.

Keeping appropriate staffing levels is another challenge for collection teams. Large market participants report 75-100% annual turnover rates (per the CFPB’s operational survey), requiring constant hiring of collection staff. Training and overseeing these new people is a daunting task, especially with the ever changing case law and legislative landscape in the collection space. Providing an efficient and fully compliant collection experience while relying on new and untrained collectors is almost impossible.

Phone calls are a dying communication method

Consumer preference is shifting away from phone calls, but phone call compliance would have been difficult even if that wasn’t the case. Calls are a compliance liability due to their frequency, their real-time nature, and the overall regulatory sentiment towards them.

Collection calls must be frequent to reach consumers. On days when an agent works an account, they may attempt to contact the consumer 4-6 times, often as frequently as 10 times per day. Consumers aren’t picking up the phone, so agents need to make more call attempts to try and reach them.   While most states, and the FDCPA, don’t limit call frequency, high frequency of calls often leads to complaints and lawsuits alleging harassment. Collector take this huge risk because calling is the only tool they understand.

Collection calls are also real-time. No matter how elaborate call scripts are and how experienced collectors may be, it is impossible to completely control the development of any individual call. Voice analytics software is limited, unable to identify most baiting and escalation issues. Real time monitoring of all calls by supervisors is financially implausible. Collection agencies are forced to settle for the best training possible, clear escalation paths for collectors whose calls go badly, and hoping for the best. Realistically, when making a large volume of calls, every day will have some potential violation.

Finally, regulation has been working against phone calls for the past few years. The FCC’s ruling limiting the use of ATDS has been devastating, and expecting it to be completely undone by the new commissioner is a pipe dream – government is not debt collectors’ friend. States like West Virginia and Massachusetts have enacted call frequency limitations, and the CFPB’s new rule outline includes a 6-times-per-week limit on call attempts. All signs point to a future where phone calls cannot plausibly be the main channel for collecting debt with any semblance of compliance.

Code driven compliance is here, and it’s a big step forward

Code driven compliance gives us complete control on what actions can be triggered by our system. It’s one of the components in Heartbeat, our machine learning-based, digital first collection platform. Heartbeat is a leap forward in debt collection, and its compliance advantages are many: from better user experience to perfect auditability.

Best in class user experience in debt collection is a compliance advantage

Many if not most of debt collection lawsuits hang on a technicality. A word is arguably missing or written in a debatable way. It’s unclear whether 8 calls or 9 calls constitute harassment. Often, consumers don’t resort to lawyers because they know for a fact they have been wronged – it is often not clear that they have been – but because their experience with the collector has been bad enough to push them to seek defence or retribution. Great user experience is therefore not only a way to improve the creditor’s brand perception and returns, but also a way to reduce the rate of complaints and lawsuits. TrueAccord’s Heartbeat system attempts to contact consumers an average of 3 times per week, compared to 4-6 times a day for traditional agencies. That, paired with best in class web and mobile experience and a helpful customer service department, significantly reduces consumers’ desire to sue for, or complain about, ambiguous technicalities.

Consumers get a consolidated account page showing all their options

Since more than 90% of Heartbeat’s interactions with the consumer do not involve a human collector, human beings are only needed for a fraction of the work. TrueAccord is able to hire skilled workers and pay them a living wage, with no commission component. Knowing that they will earn a good salary working for a technology startup reduces any incentive our team members would have had to fight with or harass consumers. That, in turn, contributes to great user experience and reduces compliance risk.

Pre-approved content and an integrated system eliminate human error

Human error is the biggest challenge for compliance departments. Collectors today need to navigate multiple systems to call, negotiate with, and collect payments from consumers. Updating the results of a call is often a complex process, requiring yet another system. Many requests to unsubscribe numbers, cease and desist communications, or simply to provide debt verification are lost and lead to complaints. This fragmented process is extremely tedious and time consuming, and inherently flawed. Letting collectors write their own emails and text messages is too much risk – something that will surely lead to violations on a daily basis.

TrueAccord’s content approval console

Heartbeat takes a code controlled approach to communications. Every outgoing communication is pre-written, then reviewed and pre-approved by TrueAccord’s legal team. Every email, text, web page and letter have to pass TrueAccord’s content guidelines driven by law, policy and procedures, including required disclosures and forbidding certain words and phrases in subject lines, or in the body of communications. Our clients’ legal and content team are also involved in commenting on our procedures as well as specific content items, to make sure we fit each company’s risk tolerance. Heartbeat will only send text messages to numbers that it knows it has express consent to text, and that have gone through an ownership check within a defined time period. Even when collectors respond to inbound consumer emails, they use pre-written replies that then direct Heartbeat how to proceed in serving the consumer. The decision to proactively communicate is strictly based on Heartbeat logic, not on collector whims; collectors cannot decide to contact consumers whenever they see fit.

After contacting consumers, the system monitors their response. Consumers can easily opt out of communications, by replying to a text message or by clicking a link in every email that lets them easily unsubscribe from future email communications. Every email and every payment page contain a link that lets consumers ask for debt verification via a few simple online steps instead of a cumbersome and mail-based process. Every interaction is designed to give consumers an opportunity to ask for more information or limit communications to their preferred channel. Though easy to dismiss as an invitation for abuse, these options increase consumer engagement and result in overall better collections – while significantly reducing complaints about continued communications and missing documentation. These two categories have consistently been the top reasons for filing CFPB disputes ever since its dispute portal was made public.

The compliance firewall: enforcing compliance at scale

Human collectors are expected to remember dozens, maybe hundreds of compliance laws and regulations as well as creditor-imposed rules. It’s an impossible task, greatly simplified by Heartbeat’s Compliance Firewall. Since it controls all contact decisions by code, Heartbeat can enforce its compliance policy at scale on every interaction without needing to train human collectors. Contact timing or frequency, matching content to the right stage in a consumer’s process or preventing the use of unsubscribed contact methods, even making sure that a consumer doesn’t get a payment offer that the creditor didn’t approve – all are controlled by the Compliance Checker. Any attempted action outside of its well defined policy is dropped. Since it’s code controlled, it cannot forget to check the time and call a consumer after 9pm or before 8am.

The Compliance Firewall also allows updates to policies and procedures. Every new update can be implemented with accuracy within days, once the appropriate code is written. By taking judgement away from the collector and subjecting all contact decisions to a data-based, code-controlled system, Heartbeat makes the optimal decision for consumer experience and driving payments, without harassing the consumer or violating the myriad of restrictions that govern debt collection.

The easiest system to audit

Compliance requires tight monitoring, and creditors audit a large sample of collection activities by their vendors. With so many voice calls, even if they are all recorded, complete and accurate audits are impossible. Auditors need to sample cases and hope to find the right patterns, or employ a large and expensive team for sufficient coverage. Heartbeat eliminates almost 95% of phone calls (typically attempting to reach the consumer 3-5 times over a 90 day period), instead focusing on written communication. Back and forth written interactions are easier to capture, store, and search. The system also saves consumers’ browsing pattern on the website and their interactions with the content they receive. It’s easy to track consumer behavior and how the system responded to it, as well as why it made a specific decision. Code controlled compliance means that decisions are easy to replicate and trace back in case they’re questioned.

A readout from TrueAccord’s event-based audit trail

TrueAccord’s system also has an audit interface for creditor audits. Compliance staff can easily search for accounts and review all collection activity – including recorded calls, emails, and every other contact. It’s a much easier approach to compliance and controls than an unwieldy excel file or PDFs dropped in an FTP folder. TrueAccord’s data retention and tracking of consumer behavior provide a fuller snapshot of Heartbeat’s collection decisions and how consumers reacted to them.

Code driven compliance is the future

We examined the inherent risks in traditional collection activities and how sticking to the phone as the leading collection tool in a call center environment creates more risks than rewards. Then, we dove into how code controlled compliance offers predictable, pre-approved, and consistent collection strategies that are easy to audit and understand. The coming years will see more and more creditors and collectors move to these machine learning based systems, as they demonstrate dominance in returns and compliance. It’s time for risk averse compliance departments to realize that they are putting businesses at risk by sticking to their phone-based roots, and look beyond tradition. A whole world of mature, stable and trustworthy technologies awaits.

Machine Learning Based Liquidation Looks Different – and That’s a Good Thing

By on June 26th, 2017 in Industry Insights, Machine Learning, Product and Technology
TrueAccord Blog

TrueAccord serves major issuers, debt buyers and lenders across the US. We compete with traditional collection agencies and beat them: TrueAccord collects more than 1.5 times the competition in a typical 90 day placement period. We use a machine learning based system, HeartBeat, that replaces the traditional call-heavy model with digital first communications that compliment consumer behavior and mode of communication, but that makes our liquidation curves look different from traditional agencies.

Traditional liquidation curves

Traditional liquidation curves typically shoot up in the first 30-45 days, followed by a plateau around 60-80 day, with a possible bump towards the end of the placement window. This pattern is driven by several factors.

Routine: agents receive fresh accounts and are eager to call them. They fire up dialers and quickly reach consumers who can either pay or be lightly pressured to pay. After a few weeks of calls, agents are tired of calling the same consumers. They heard what they think are excuses, have driven all the easy payments they could drive, and are ready for new accounts. “Old” accounts, as old as 30 days, get a worse treatment. Collection managers know this, and try to trick collectors into thinking they got fresh accounts by pulling the accounts out from the system and re-entering them. This rarely works. Collectors lose focus and with it, performance.

Net present value: settlements are better than payment plans for collectors – they mean more money now, versus a payment plan that may fail, and require reminders and additional work by the collector. Collectors opt for more settlements earlier, if they can get the consumer on the line. Under the pressure of a call, the consumer may commit to a payment plan. In this case the collector prefers as high a monthly payment as possible, since they assume the payment plan will fail early. The consumers, struggling with irregular cash flow and a large payment they shouldn’t have committed to, fail payment plans at a staggering rate: as much as 50% of payment plans fail.

Remorse: consumers who agree to settlements or plans often feel remorse after getting off the call, and tend to charge back on payments they made. Chargeback rates in the debt collection industry are so high (rates as high as 2% are not rare), that most payment providers won’t work with collection companies.

The initial bump in liquidation is often enough to beat other phone based agencies. Since all agencies use the same methods, a slight advantage in selecting the right accounts to call first can get an agency ahead of its unsophisticated peers.

The TrueAccord liquidation curve

In contrast to the traditional curve, TrueAccord’s liquidation curve is somewhat linear. It often starts lower than the traditional agency, but continues to rise through the placement period until it crosses and exceeds its competitors. That inflection point can happen as late as day 80 (before the algorithms have been tuned, early in a pilot) and as early as day 15 (once the algorithms have learned how to handle a new product). The difference is driven by several factors.

Data driven treatment at scale: TrueAccord’s system is machine learning based and digital first. Since it starts with an email, it can initiate contact with all consumers easily, without having to call them often – and consumers are much more likely to respond to digital communications than to a phone call: while Right Party Contact rates often hover around 4-5%, email open rates on TrueAccord’s platform reach 65-70% and click through rates reach 30-35%. Once it sends its first email, it uses real time tracking of consumer responses to tailor its next steps. The system relies on hundreds of millions of historic contact attempts to optimize its contact strategy. If the consumer doesn’t reply, the system can automatically switch between channels (from email to text, call, letter, and so on) to reach the consumer. It also uses data to figure out what time of the day to contact the consumer that will yield the best response rates, call centers are limited to just making phone calls, which often consumers ignore because they are busy or just don’t pick up calls from unknown numbers. Since a machine doesn’t get bored, it continues contact attempts (3 a week on average) until it is told to stop. Targeted, consistent communications at scale mean that more consumers will interact with our system compared to a call center.

Optimizing for liquidation: a data driven system can use historical data to understand what best fits consumer needs and leads to better liquidation. It doesn’t need to push for early settlements because its automation lets it serve each consumer according to their needs – making custom tailored plans viable. Consumers get easier payment terms that fit their needs, and end up paying more. We convinced several of our clients to move from a default payment plan length of 6 months to 12 months. Contrary to call center based intuition, these longer plans get more consumers to sign up and don’t cannibalize settlements, in turn leading to an increase in liquidation. The machine learning system can service these plans at scale and reduce failure rates: TrueAccord payment plans complete as much as 89% of the time (as low as 11% breakage). By the time payment plans for traditional collectors fail their second payment, TrueAccord’s liquidation rates start soaring.

Best in class user experience: consumers don’t like phone calls or letters. They prefer 24/7, personalized, easy to use services – and collections aren’t any different. Using our system they can customize and sign up for settlements, payment plans, or ask for debt verification. Having access to their account information, and a sense of control over payment options, consumers don’t feel pressured or remorseful after paying. TrueAccord’s chargeback rates are next to not existent.

Bottom line

Machine learning based debt collection is different in many ways that benefit creditors and consumers. Our liquidation curve tells the story of how our system behaves differently than call center based collections – serving consumers at scale, using their preferred communication channel, and while tailoring payment solutions that work for them.

Using phone in a digital world. A Data Science story.

By on March 16th, 2017 in Data Science, Debt Collection, Machine Learning, Product and Technology
TrueAccord Blog

Contributors: Vladimir Iglovikov, Sophie Benbenek, and Richard Yeung

It is Wednesday afternoon and the Data Science team at TrueAccord is arguing vociferously. The white board is covered in unintelligible hand writing and fancy looking diagrams. We’re in the middle of a heated debate about something the collections industry has had a fairly developed playbook on for decades: how to use the phone for collections.

Why are we so passionately discussing something so basic? As it turns out, phone is a deceptively deep topic when you are re-inventing recoveries and placing phone in the context of a multi-channel strategy.


 

Solving Attribution of Impact

The complexity of phone within a multi-channel strategy is revealed when you ask a simple question: “What was the impact of this phone call to Bob?”

In a world with only one channel, this question is easy. We call a thousand people and measure what percentage of them pay. But in a multi-channel setting where these people are also getting emails, SMS and letters, there is an attribution problem. If Bob pays after the phone call, we do not know if he would have paid without the phone call.

To complicate matters further, our experiments have shown that phone has two components of impact:

  1. The direct effect — the payments that happen on the call.
  2. The halo effect — the remaining impact of phone; for example seeing a missed call from us and going back to an email from us to click and pay.

To solve the attribution problem and capture both components of impact, we define the concept of incremental benefit as:


 

Intuitively, the incremental benefit of a phone call is the additional expected value from that customer due to the phone call. For example, assume Bob has a 5% chance of paying his $100 debt. If we know that by calling him, the probability of him paying increases to 7%, then the incremental benefit is $2 (100 * (0.07 – 0.05)).

 

How we calculate incremental benefit

Consider the incremental benefit equation in the last section. It requires us to predict the probability of Bob paying for each scenario where we call him and do not call him.

Hence we created models that predict the probability of a customer paying. These models take as inputs everything we know about the customer, including:

  • Debt features: debt amount, days since charge-off, client, prior agencies worked, etc
  • Behavioral features: entire email history, entire pageview history, interactions with agents, phone history, etc
  • Temporal features: time of the day, day of the week, day of the month, etc

The output of the model is the probability of payment by the customer given all of this information. We then have the same model output two predictions: probability of payment with the current event history, and probability of payment if we add one more outbound phone call to the event history.

Back to our example of Bob, the model would output the probabilities of 7% and 5% chance of paying with and without an additional phone call respectively.

This diagram is a simplification that omits many variables and the actual architecture of our models

 

Optimal Call Allocation

The last step of the problem is choosing who to call, and when. The topic of timing optimization deserves its own write-up, so we will close with discussing who we call.

Without loss of generality, assume that we would only ever call a customer once. The diagram below has the percentage of customers called on the x-axis. And the y-axis is in dollars with 2 curves:

  • Incremental Benefit — this curve shows the marginal incremental benefit of calling the customer with the next highest IB
  • Avg cost — this horizontal curve shows the average cost of an outbound call

 

There are two very interesting points to discuss:

  • Profit max — calling everyone to the left of the intersection of incremental benefit and avg cost is the allocation that maximizes profit. Every one of these calls brings in more revenue than cost.
  • Conversion max — notice that incremental benefit dips below zero. This is especially true when you remove the assumption that we only call each customer once. The point that maximizes conversion for the client is to call everyone to the left of where incremental benefit intersects with zero.

Our default strategy is to call all customers to the left of the profit maximizing intercept. Interestingly, an intuitive investigation of the types of customers selected reveals customers at two extremes: we end up calling both very high value customers that have shown a lot of intent to pay (e.g. dropped off from signup after selecting a payment plan) and customers where email has been ineffectual (e.g. keeps opening emails with no clicks or no email opens.)

 

Conclusion

The world has become increasingly digital, and a multi-channel strategy is the right response. Bringing the traditional tool of phone, as just one channel within this strategy, forced us to rethink a lot of assumptions and see where the problem led us. We began by replacing the traditional “propensity to pay” phone metric with incremental benefit, found ways to predict this value, and implemented a phone allocation strategy that maximizes profits for the business.

Hear our CEO talk about AI in Fintech at LendIt

By on February 28th, 2017 in Industry Insights, Machine Learning
TrueAccord Blog

Our CEO , Ohad Samet, will be part of a panel discussing Artificial Intelligence Uses in Fintech. The panel will be held at 2:15pm Eastern on Tuesday, 3/7.

Continue reading “Hear our CEO talk about AI in Fintech at LendIt”

Why batch-sending emails aren’t all there is for debt collection

By on December 13th, 2016 in Industry Insights, Machine Learning
TrueAccord Blog

Though historically resistant to innovation, the collection industry feels pressured to make changes. Consumer preference, requirements from clients and mounting costs dictate increased use of technology – a welcome trend. Among those new tools, we are starting to see increasing adoption of emails for collections. Agencies have a small selection of vendors to blast out an email. Agencies with large call centers view this as a cost reduction exercise, and another way to get consumers to call in and talk to their agents.

Continue reading “Why batch-sending emails aren’t all there is for debt collection”

Self service portal vs. machine learning-based collections

By on December 6th, 2016 in Industry Insights, Machine Learning
TrueAccord Blog

Consumer behavior is changing. As more of us are glued to our mobile phones, emails, and social media accounts, it’s clear that the old ways of collecting debt are quickly becoming irrelevant. Still, the market doesn’t offer a multitude of collection solutions aimed at responding to the digital consumer. When we present our machine learning-based solution to prospective customers, we’re often asked about the difference between our solution and a self service portal. Although both solutions are digital, they cannot be less alike.

Continue reading “Self service portal vs. machine learning-based collections”

Augmenting your strategy with automation: part three of three

By on August 2nd, 2016 in Industry Insights, Machine Learning
Augmenting your strategy with automation: part two of three

Automation and digitization offer new tools for the collection strategist, augmenting the traditional building blocks. These new tools, introducing flexibility and sophistication that are usually attributed to other parts of the business, can mitigate common pitfalls.

Continue reading “Augmenting your strategy with automation: part three of three”

Augmenting your strategy with automation: part two of three

By on July 19th, 2016 in Industry Insights, Machine Learning
Augmenting your strategy with automation: part two of three

Automation and digitization offer new tools for your collection strategy, augmenting the traditional building blocks. These new tools, introducing flexibility and sophistication that are usually attributed to other parts of the business, can mitigate common pitfalls.

In this series, adapted from our free eBook Automating Debt Collection 101, we’ll review the three major areas where automation and digitization can boost a collection strategy:

  • Early contacts and improved segmentation
  • Persistent communication
  • Improved customer satisfaction

In this second part, we’ll focus on improving performance with persistent communication.

Customers in debt are in a dire situation, cannot pay the balance in full, and many times even a payment plan isn’t feasible. A call center is limited in its flexibility – beyond a certain number of payments or customizations, a human agent is just too expensive. These accounts risk being mishandled, and end up paying less than they could with some “hand holding”.

Automated collections have a tremendous advantage in handling complex cases. The platform consistently follows up with customers using multiple channels, offering various solutions according to an optimized offer strategy, and administers changes in those solutions (split payments, rescheduling and more) over time as needs change. These tools can accept and administer a monthly $5 payment that increases over time, even if the customer misses a few payments and needs consistent follow-ups. When the vast majority of contacts are automated, even small amounts are profitable – and add up. The system doesn’t get tired, doesn’t get angry, and doesn’t need to go home by the end of the day. It’s there to service the customer.

TrueAccord sees more than 35% of customers in an average placement click on a link and negotiate with an automated system, thanks to diligent and relevant follow ups. In tests, working on the long tail of underserved accounts yields 4-8% of additional recovery – dollars that would otherwise be considered lost.

Want to use our tools to optimize your strategy? Visit our website to learn more.

Augmenting your debt collection strategy with automation: part one

By on July 6th, 2016 in Industry Insights, Machine Learning
Augmenting your strategy with automation: part one of three

Automation and digitization offer new tools for the collection strategist, augmenting the traditional building blocks for your debt collection strategy. These new tools, introducing flexibility and sophistication that are usually attributed to other parts of the business, can mitigate common pitfalls.

In this series, adapted from our free eBook Automating Debt Collection 101, we’ll review the three major areas where automation and digitization can boost a collection strategy:

  • Early contacts and improved segmentation
  • Persistent communication
  • Improved customer satisfaction

In this first part, we’ll focus on using automation to facilitate early contacts and improved segmentation.

Automated collections are scalable. This means communicating with all customers as early as possible in the collection cycle, quickly working to resolution with those who can pay, and a more robust debt collection strategy. In traditional call-center collections, up to 50% of meaningful interactions are made within the first 30 days of communication. With an automated strategy, most of that value can be captured in a much more cost-effective manner, in a much shorter time span. No more guessing who to call first because everyone can be contacted at scale.

Further, automated and digital collections create a wealth of data that cannot be gleaned form calls. User clicks and browsing, time and day of activity and more. The data can be used to segment accounts to those who are engaged, those who’ll respond better to phones, and those who should be sold or handled in other ways. It allows much more flexible recall criteria than placing for a set number of months, no matter what happens with the account. This means giving accounts the treatment they need at the right time, improving liquidation as well as cost to collect thanks to the scale of operations.

Want to use our tools to optimize your strategy? Visit our website to learn more.

The three technology keys to automating debt collection

By on June 14th, 2016 in Industry Insights, Machine Learning
The three technology keys to automating debt collection

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

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

Data Infrastructure

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

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

Feedback Loop

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

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

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

Increasing Relevance

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

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

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

Bottom Line

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

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