At TrueAccord, we take our service availability very seriously. To ensure our service is always up and running, we are tracking hundreds of system metrics (for example, how much heap is used by each web server), as well as many business metrics (how many payment plans have been charged in the past hour). We set up monitors for each of these metrics on Datadog, that when triggered, will page an on call engineer. The trigger is usually based on some threshold for that metric. As our team grew and more alerts were added we noticed three problems with Datadog: Any member of our team can edit or delete alerts in Datadog’s UI. The changes may be intentional or accidental, though our team prefers to review changes before they hit production. In Datadog, the review stage is missing. Due to the previous problem, sometimes an engineer would add a new alert with uncalibrated thresholds to datadog to get some initial monitoring for a newly written component. As Murphy’s law would have it, the new alert would fire at 3am waking up the on call engineer, and it may not even indicate a real production issue, but a miscalibrated threshold. A review system could better enforce best practices for new alerts. Datadog also does not expose a way to indicate that an alert should only be sent during business hours. For example, for some of our batch jobs, it is okay if they fail during the night, but we want an engineer to address it first thing in the morning. To solve these problems, we made DogPush. It lets you manage your alerts as YAML files that you can check in your source control. So you can use your existing code review system to review them, and once they’re approved they get automatically pushed to DataDog -- Voila! In addition, it’s straightforward to setup a cron job (or a Jenkins job) to automatically mute the relevant alerts outside business hours. DogPush is completely free and open source - check it out here.

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.

There are multiple reasons for adopting one collections strategy over the other. Every collections strategy has built in areas of weakness that cause it to make less money than possible. Strategies shouldn’t be stagnant, and as new tools present themselves, strategists can continue to fine tune their strategy and improve returns. In the following three post series, adapted from our free eBook Building a Collection and Recovery Strategy, we’ll review the top three pitfalls we see with common collection strategies. They are: Under-charging when selling or over-paying when outsourcing Only focusing on a small percentage of customers in an outsourced strategy Losing customer relationships in a sell or litigation heavy strategy In this first part, we’ll focus on mis-pricing your portfolio when debt sales are part of your collections strategy. When selling debt or outsourcing, the lender’s interface with vendors is almost deceivingly simple. Companies tend to mix high yielding accounts with low yielding ones – and end up recovering less from the former so they can get rid of the latter. That is often the result of a rudimentary segmentation and pricing strategy at the seller. Even when segmenting, collection strategists often settle on a simple champion/challenger model for each segment to get the best price or lowest contingency rate. This limited model is based on two issues: First, the assumption that collection services are commoditized and don’t offer any unique technique, so price is the only differentiator. If everyone is the same, why segment? Second, there isn’t a lot of data feedback in collections to allow proper behavior-based segmentation. Collection agencies aren’t set up to provide high quality data feedback, and debt buyers will often not want to share. The only mode of operation is to sell or place and forget about the debt for a while. Want to use our tools to optimize your strategy? Visit our website to learn more.

News hit this week that Synchrony bank increased its charge-off guidance to 4.5-4.8%. This is expected, as multiple banks expect higher charge-off rates due to the change in credit cycle and deteriorating consumer trust. The "good years" of cheap credit fueled by low (and negative) interest rates seem to be over, and lenders are tightening their belts. We see that with the unfortunate crunch in interest in Marketplace-style consumer loans, but expect the effect across multiple types of loans. Synchrony, whose portfolio skews towards lower FICO-score customers, may just be the canary in the coal mine. Shares of other large issues tumbled accordingly this week, although not to the same magnitude as Synchrony's.

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: Interviewing your experts: presenting several cases that were successfully converted and those that weren’t, and asking what they have in common. Implementation: the resulting model is validated against data trends. 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

Debt collection is a regulated industry, but as our thought piece on American Banker states, there's room for more - positive - guidance. In the last few years, collection suit numbers have soared and the CFPB has responded by closing or fining what they call "lawsuit mills." Still, most collection agencies follow the law and will still find a technological way to file large volumes of lawsuits without violating federal measures. Consumers will still end up losing by being subjected to aggressive yet absolutely legal tactics in the collection process. Read more here.

Debt collection and account receivable departments often start with one person contacting late customers and evolve from there. Even third party collection agencies grow this way as they get more business. As a result, most collection departments are comprised of large teams of operators trying to negotiate with customers. Data science teams that are tasked with improving performance and profitability usually approach the task in one of two ways: process automation or agent-independent decision automation. Process Automation is the effort to automate manual tasks done by collection agents, replacing them with an automated process or a self-service portal. This may mean skip tracing, logging payments, or queuing up phone numbers to call. The data science team acquires data sources or builds a process that replaces manual work with automated one, reducing the amount of time an agent spends per case. It’s about optimizing agent time on the phone, making sure that every action an agent takes is a high yield one, while busy work is replaced by some level of automation. Decision automation means trying to teach a machine how to make the same quality of decision an agent makes in the collection process. For example: how to talk to debtors, what to tell them, how to respond to their issues. Because most agents have a hard time explaining in detail why they made one decision and not the other (they “just know”), often data science teams treat agents as an unreliable source of information. The team determines what they are trying to optimize – for example, right-party contact or the number of calls ending with a payment. They then build models that optimize these metrics, but without asking agents for feedback – only looking at long-term liquidation results. While both approaches are important and are often used at TrueAccord as well, there’s a third one that often gets overlooked because data scientists and agents don’t interact often: Agent Dependent Decision Automation, or Expert Based Automation. Interested to learn more? Pick up our free eBook: Automating Debt Collection 101

We're all about innovation at TrueAccord, and so were pleased to find this article from AccountsRecovery.net titled "Industry Experts Share Tips On How Agencies Should Modernize". We were a bit surprised to find old ideas reiterated, with a focus on problems and compliance challenges rather than solutions. This study was sponsored by Castel, a provider of solutions to call centers; furthermore, the experts interviewed have built successful businesses using call center technology. Therefore the focus on call solutions makes sense. We also understand that the TCPA is vague, that consent is an issue and that consumer attorneys are putting compliance teams on edge. As a licensed agency, we're in the same boat. However times are changing, and it's high time that we embrace the change. What technology solutions is the discussion raising? Mostly solutions to manually dial phone numbers, maybe a way to place a voice mail without a ring. The discussion offers little other options other than the iterated duo: those who don't believe in technology, and those who deem it too risky. The school of "technology won't work" The collection industry has been around for decades, and many of the businesses that comprise it were started long ago by "old school" collectors. That's why the following quote didn't surprise us. “We’ve looked at technology like online chat interface,” said Christian Lehr with Healthcare Collections in Phoenix. “But we haven’t moved forward because it’s a business decision, not a compliance decision. I’m not sure it is the best way to serve the consumer. Much like with emails or text messages, it can be hard to understand context. And there is a time lag for communication. We may be able to serve the consumer faster on a phone call.” As a company that uses machine learning to develop hybrid collection systems that collect better than call centers, we understand the sentiment but beg to differ. We also have the data to back this disagreement. Not only are email, text and website more effective for collections (from 30% better to 5 times better for low balance debts), consumers prefer them. More than 50% of TrueAccord's traffic is from mobile devices; more than 35% of payments are made on a mobile device; 25% of interactions with our system happen in non-FDCPA hours. If this isn't a "better way to service customers", what is? The school of "we need permission" Collectors have been trained by regulators and lawyers to be very compliance minded. This makes them pessimistic about any new technology that hasn't been tested by courts and lawyers. We hear a lot of the following from agency leaders. “The only way we can move forward and success is to embrace technologies that are available to us,” Strausser said. “We should be looking at contemporary means of communication and exploring how to pull the trigger when and we are granted approval.” We'd like to challenge this approach from two directions. First, when thinking about texting and emails, compliance minded collectors are worried agents on the floor are going to abuse these new tools. However email and texts can be pre-written, optimized, and sent at exactly the right moment. They actually present a much stronger compliance framework when handled properly. Second, collectors won't adopt new technologies without explicit approval form the CFPB, but hold on to old call center technology even though the FTC clearly signals it's all but forbidden. Is explicit approval, which the CFPB rarely provides, the thing to stop us - or can we have an honest analysis of the FDCPA to show us what reasonably can and cannot be done? Are we holding on to old and challenged technology due to inertia? Bottom line: progress There is much to do in debt collection. Consumer expectations, client requirements and regulatory pressure are mounting. The right thing to do is take a hard look at the old ways of doing business, and realize that the days of hiring to fight turnover and living off thin margins are almost over. Technology can help us service consumers at scale, provide great customer service, and get results that are better than anything we'd forecast based on old paradigms. We are excited to partner with some of the biggest financial institutions in investigating this possible future.

When we started working on our patented collection engine, Heartbeat, the industry told us: you’ll fail. Computers can’t collect. Humans do. The best you can do with automated communications is to drive inbound calls, so human collectors can “seal the deal”. Fast forward 18 months since our launch, and Heartbeat beats call-center based agencies in a growing number of segments.  It turns out that computers collect debt pretty well. How come? Debt collection is a numbers’ game. Consumers are ready and able to pay at different times, react to different stimuli, and need varying levels of support in the process. Teaching a machine to respond to these needs was historically more expensive than hiring humans, but as technology improves and compliance requirements grow, this is changing rapidly. Humans are great at acting on intuition and responding to a changing situation. We act well based on partial information, guesses, slight changes in tone of voice and intonation. Good sales people do so without thinking. Humans are great at identifying and understanding corner cases and responding to complex inquiries. Machines can’t learn these things unless explicitly taught, and many of these skills are nuanced and complicated. Machines are “robotic”, for better and worse, and can’t have empathy. Humans do have downsides, too. We are susceptible to biases. We make decisions based on the few past examples we remember and ones that fit what we believe. Collectors fixate on high balance accounts, worry about missing their goals, fight with their significant other and lose focus. Machines do not. Machines don’t forget a thing, and they always take as much data as available into consideration. Machines don’t talk back or get angry. Historical attempts failed because they either tried to replace humans with even lower-paid humans, or tried to automate and get rid of humans altogether. We realized that a hybrid approach was the best one: machines make accurate decisions based on historical data when available, and learn from humans when not. Humans understand corner cases. We had to create a combination of a strong engine, and a team of experts to continuously improve it. How does that work? When Hearbeat doesn’t “know’ what to do with a customer, it defers to our team of experts in San Francisco. They resolve the issue for the customer, and also give enough input so Heartbeat will know how to deal with the same situation in the future. The combination allows us to hit incredible productivity rates, while beating other “robotic” and passive “payment gateway” solutions. Can machines collect? They can, and apparently many who are in debt prefer their targeted approach. When you think about the user experience, the ease of use and the automation, it’s actually not that surprising.

PWC just posted an interesting research (PDF) showing how Fintech firms influence financial institution from the outside in. We especially liked these two graphs: More virtual channels, simpler products "Banks are moving towards non-physical channels by implementing operational solutions and developing new methods to reach, engage and retain customers." says PWC. We couldn't agree more: the time for digitization has come, in debt collection as much as in any other part of the business (read more about eDisputes). As a result, banks are listening: "As they pursue a renewed digital customer experience, many are engaging in FinTech to provide customer experiences on a par with large tech companies and innovative start-ups." Top Fintech opportunities: cost reduction and differentiation "B2B FinTech companies create real opportunities for incumbents to improve their traditional offerings", says PWC, "incumbents could simplify and rationalise their core processes, services and products, and consequently reduce inefficiencies in their operations." We see that in the marketplace: technology and automation help us scale (read more about 30,000 cases per agent) but it also helps us provide personalized, tailored treatment to consumers. As a result, lenders that work with us see better results (through complex recovery strategies), better customer satisfaction, and increased compliance. Bottom line Banks are leaders of the Fintech community. Often they are reluctant to adopt a trend until it visibly gains traction, but once they do, their scale draws attention from all participants. It's 2016, and banks have noticed Fintech and the upside it brings with it. It's going to be a fascinating year!