Tag: analytics

5 ways debt collection uses machine learning and artificial intelligence

By on February 28th, 2020 in Machine Learning

Machine learning algorithms are playing a key role in the collections industry’s technological growth. Companies are working to integrate artificial intelligence and machine learning into their strategies in response to changing regulations and evolving consumer preferences. These processes can look dramatically different from business to business!

Some technologies are being applied to optimize traditional call and collect strategies while others are building digital-first outreach platforms. Understanding how these algorithms are working for the industry can provide insight into the future of collections. 

Business intelligence and analytics

Business intelligence platforms are the foundation for the future of collections. They not only help companies understand how to best reach their existing accounts using traditional collections strategies but also integrate into other digital tools to create powerful automated systems. 

These algorithms process large sets of data such as call times, call effectiveness, the value of certain accounts, collections rates, and many other variables. By analyzing this information, teams can optimize their outreach strategies by focusing on accounts that are more likely to be collected on, understand what times of day or channels work the best, and even determine what language to use in conversation with specific subsets of accounts. 

Portfolio evaluation and exchange

By adding a clear scoring system to business analytics tools, teams can share their portfolios in an online marketplace with other creditors and debt buyers in order to buy, sell, and even outsource debts as needed.

While debt marketplaces are not new, real-time scoring updates and activity insights provide a dynamic, cloud-based view into a fluctuating market. 

Human-like contact center agents

As companies evaluate their data and optimize their outreach, they can also integrate digital agents to interact with consumers over the phone. Artificial intelligence software can be used to create human-like voices and personalized experiences for consumers.

These platforms can operate at scale more easily than sprawling call centers but still rely on a traditional call and collect model that consumers are shying away from. As consumer preferences shift toward digital channels, more machine learning tools can help to optimize for an omnichannel experience.  

Digital collections platforms

Digital collections software is able to optimize performance data and leverage it using a diverse, multi-channel communication approach. Phone calls may be included as part of a larger strategy, but these platforms are primarily built around modern consumer channels including email, SMS, push notifications, and direct drop voicemails.

Contextual bandit algorithms take channel selection to a level beyond traditional A/B testing. Even if 10% of your consumers prefer one message type to another, it’s important to understand all of your audience’s preferences.

Digital channels integrate seamlessly with decision making algorithms and can optimize communications in ways that call systems cannot. For example, digital channels like email can reach consumers outside of hours typically limited by the TCPA. 

25% of TrueAccord’s consumers access their accounts outside of the 9am to 9pm when traditional agencies cannot legally reach them.

Digital debt collection agencies

Each of these implementations of machine learning help to build a more personalized, more focused, and more forward thinking debt collecting experience for both consumers and creditors. One consistent factor that does limit their effectiveness is the need to build them into existing systems or alter processes at scale. 

A collection agency that bears the consumer in mind and has a machine learning-driven, digital-first strategy removes this hurdle and enables a full-service, easy to use experience for both companies and consumers. With these technologies built into a team rather than a product or service, digital debt collection agencies can provide the services outlined above alongside a dedicated infrastructure and a team of technology experts. 

Choosing the right tools and support for your company’s collection efforts is more important now than ever before, and understanding the options that are available can help you to future-proof your strategy before it’s too late.

Still have questions? Our team is happy to help make sense of what a digital-first collections agency can do. Set up some time to chat!

Tracking Performance Data With Digital Debt Collection

By on October 21st, 2019 in Product and Technology

Call centers are notorious for reaching hundreds, if not thousands, of consumers several times per week (and even several times per day!). The debt collection industry is plagued by the perception that collectors are relentless and uncaring, which makes resolving debts even more challenging. Digital debt collection strategies aim to alleviate the stress of incessant calling for consumers, and also provide unique, powerful solutions for creditors.

Collection metrics

Digital-first debt collection strategies provide creditors the ability to track and aggregate more objective performance metrics that help strengthen their collections strategy. Qualitative metrics from traditional call centers are still subject to the endlessly variable human element of a phone call. 

When outreach is entirely automated, it becomes easy to A/B test simple changes (new subject lines, different greetings, etc.) and determine which are the most effective. But how do we define effectiveness? At the end of the process, an effective collections strategy is one that leads customers to make a payment. 

There are a few key metrics that call centers use to drive customers to this end goal that can be easily supplemented or overtaken by digital collection strategies.

Calls per account and calls per agent

Traditional collection agencies, like any other sales call center system, track the total amount of calls made to each customer and by each agent on the team. When individual agents are responsible for contacting customers, they have to hit an outreach quota. This quota reflects directly back on the calls per account, or how many times an individual customer has been contacted. 

As agents are required to call customers and collect on accounts, the calls per account may increase to a point where customers feel overwhelmed and over-contacted (which can even lead to symptoms of anxiety and depression). At the same time, if countless calls are being made, and an account is not paying, there is a clear gap in effectiveness. 

One of the advantages of a digital debt collection strategy is that agencies can reach customers with relevant messaging at times that work for them. This can include hours in which call centers are no longer legally allowed to reach a customer—before 8am or after 9pm. With these legal limitations in place and the need for agents to meet quotes, traditional collections strategies encourage an artificial inflation of outreach numbers that may not be positive.

Hit rates, percentage of outbound calls resulting in promise to pay (PTP), and call quality 

Call volume is not the end-all-be-all of call center metrics though. Simply tracking output numbers isn’t enough when engagement is the key metric. Hit rate is defined as the total number of calls divided by number of those calls that are answered by customers. While this number can be helpful in narrowing which calls were more successful than others, it cannot reach the same level of detail as a full digital strategy.

In the case of a phone call, there are limited options once the phone has been dialed:

  • The customer does not answer
  • The customer answers but ends the call before promising payment
  • The customer promises to pay

Trying to understand what leads to a successful payment on a call is then dependent on the agent’s perspective. Digital debt collection conducted through machine learning is able to communicate using personalized and consistent content. Hit rate, PTP, and call quality analysis can then be expanded on, and performance can be measured by:

  • Email Deliverability
  • Email open rates
  • Link click rates
  • Website engagement (Including clicking on further links, filling out forms, viewing specific webpages, and more)
  • Online payments

These data points can help pinpoint where in the process a customer was lost, improve the next attempt at outreach with that data in mind, and eventually guide the account to a payment. With more data and longer periods of time, machine learning processes only continue to improve.

Updating your collections strategy 

TrueAccord takes our digital strategy a step further by looking beyond simply using digital channels and focuses on the power of machine learning to continuously improve our collections performance. We’ve come to understand that creating an effective, empathetic collections experience actually comes from creating a more analytical and AI-driven process.

With better visibility into performance, more granular data points, and more accurate reporting available than ever before, digital debt collection strategies strengthen the power of any collections team.