The Next-Generation
Collections Model

Regulatory concerns, consumer preferences, and increasing consumer debt levels have all created a need to revisit and renew the collections process. In expanding credit markets, new technologies have already been embraced to enhance the customer experience in the credit acquisition process. But now it’s time to apply the same approach elsewhere.

The new collections model needs to focus on analytics and new technologies, which were unavailable during the last downturn. If you’re a risk manager, it’s important to ensure that your organization is prepared to manage economic uncertainty. Embracing advanced analytics and outcomes-driven processes can help your organization stay ahead of the curve and maintain a competitive edge. Implement a new model that is optimized for success – and ensure your organization won’t fall behind.

The collections industry has been slow to embrace analytical methods. But advancements in analytical methods and machine learning, coupled with digital technologies, have created new opportunities, enabling more effective and efficient collections processes, and revolutionizing the way lenders interact with customers. Utilizing these advanced analytics means financial institutions, energy, telcom, utility companies, and retail finance companies can build a more efficient model, resulting in better performance at a lower cost.

Customer segmentation can also be improved, capturing a more holistic view of the delinquent customer. This includes their ability and willingness to pay, intent to pay, and contact channel preference. Driven by analytics, this new approach determines the best possible treatment strategy, the ideal way to communicate, and the optimal moment to make contact. By matching the most appropriate forbearance strategy for each customer and communicating via their preferred channel, financial institutions can optimize both the customer experience and the cost to collect.

For the past 30 years, traditional collections processes have heavily relied on behavior scoring, days past due, and balance to prioritize outbound call strategies. However, this approach is no longer sufficient in today’s market. Advanced analytics can enable the development of more effective collection strategies by providing finer segmentation and a wider variety of customer contact possibilities. This creates a more diverse suite of channels for customer communication, which improves customer experience and provides a greater degree of control in lender-customer interactions. This shift marks a dramatic change from the traditional collections process, which relies on static classifications like days past due or risk scores to drive decision-making. By adopting a more dynamic approach that focuses on outcomes and response propensity, lenders can provide more individualized treatments that better reflect customer preferences and circumstances.

Above all else, using advanced analytics and tech advancements like artificial intelligence and machine learning enables financial institutions to migrate to a deeper, more informed treatment of their at-risk customers. By learning from previous collections activities, the assignment of treatments becomes more fine-tuned and effective over time, generating considerable efficiencies while enhancing the overall customer experience.

Overall, a combination of on-us behavioral data, off-us behavioral data, previous contact history data, and socio-demographic data is required to build a comprehensive and holistic view of the delinquent customer.

Leveraging these various data sources and applying advanced analytics, allows you to build a more individualized approach to collections, based on customer preferences and circumstances. This new approach marks a significant departure from the current model, which relies on core static classifications such as days past due or single risk scores. With the next-generation collections model, the final customer treatment is much more personalized, focused on outcomes and response propensity.

It may seem daunting to implement more advanced technologies in your collections strategy, but the role of an automated decision engine is key. Using real-time data and and automated risk decisioning is the background superstar that enhances your collections process in a variety of ways:

Traditional collections processes heavily relied on simplistic measures like behavior scoring, days past due, and balance to prioritize outbound call strategies. But in today’s dynamic, rapidly changing market, this approach falls short. As the industry continues to evolve, it’s imperative for collections professionals to recognize the transformative potential of analytics and leverage them to create a competitive advantage in the dynamic collections landscape. To do so may require a new look at the decisioning platform used in collections – because if you aren’t adapting to the conditions, your competitors will.