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The economic environment is changing, and your organization needs to adapt to remain competitive. Financial institutions, energy, telcom, auto, utilities, and retail finance companies have each recognized the need to build a new collections model that utilizes advanced analytics and outcomes to drive processes, rather than simply relying on static info like days past due.

Unfortunately, the collections industry has been relatively slow to embrace new techniques in analytics compared to other areas of organizations (i.e. like loan origination) yet nearly 30% of Americans have at least one debt currently in collections. Investment in the collections process is often overlooked in favor of projects that aim to grow the customer base. However, with consumer debt levels returning to 2008 recession levels (total household debt in the United States rose by $148 billion in Q1 2023, totalling $17.05 trillion) and the threat of another recession on the horizon, collections centers are finally getting the attention they deserve. In this blog, we’ll look at the new technologies available, how they impact the process, and ways to utilize new tech to stay ahead of your competition.

The New 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.

Advanced Analytics and Technology for Next-Gen Collections

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.

What data elements are required?

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.

The Role of the Decision Engine

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.

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It’s just over 30 years since The Simpsons first aired. The show built a reputation on predicting the future; Presidencies, Super Bowl wins, mobile phone technology, Olympic feats… even accurately depicting London skyscrapers, almost 20 years before construction. In a world where truth can often be stranger than fiction, these moments have manifested themselves into real-life. Rather than complex data-driven insight, it was the astute cultural awareness of Matt Groening et al that formed the basis of these ‘predictions’. A keen eye and razor-sharp interpretation of social nuances, cultural insight, and intuition to look at things with a wide lens to determine predictive outcomes.

The world has undoubtedly changed in recent months. In today’s challenging landscape, banks and financial organisations are aware that it’s now as important as ever to make fast and well-informed credit decisions for onboarding new customers; largely driven by increased competition and a rise in customer demands for fully digital processes. But, in a time of economic instability, where financial stress is being experienced by a growing number of households, proactively managing existing customers as effectively as possible is a high priority for lenders; enhancing collections strategies through data insight and predictive analytics is now essential to this process.

Today’s reality is that an increasing number of customers have become, or are becoming exposed, with a reduced or uncertain-level of income and a recent history of payment holidays across multiple products. A recent study from Transunion highlights that 53% of UK consumer household income has been impacted, with 68% now concerned with how they are going to pay current bills and loans. When asked which bills are most concerning, credit cards (37%), mortgages (24%), personal loans (22%), and car payments (18%) rank high. These concerns are undoubtedly being shared by an ever-growing number of global households.

The industry is facing challenges and change. The existing customer-base is concerned about meeting repayments, and many are faced with reduced income or redundancy in higher impacted sectors like hospitality, travel, and engineering. Adaptation is now required from both borrowers and lenders to ensure the best outcomes are achieved through the collections process. Lenders need to be equipped to handle increased volumes of cases and mitigate losses, all while building strong relationships with customers who need their proactive support. We recently launched our latest eBook where we outlined some of the key ways predictive analytics, supported by technology, can combat the increasing challenges faced by collections teams. Through early-warning triggers, predictive models, ML solutions that deliver default rate predictions, and wide-ranging real-time data sources, lenders can access advanced capability when it comes to predicting delinquencies and deploying multi-channel customer communication strategies. Banks and lenders can no longer rely on credit bureaus alone to inform their collection strategies or time-delayed manual assessments to identify higher-risk customers.

Supported by technology, here are my top three actionable methods that businesses can adopt to increase their ability to predict and enhance their collections strategies:

1. Proactively predict delinquencies: Actively monitor accounts for early-warning triggers that could signal impending trouble, such as increasing credit line usage, changing payment behaviour, change in income, and decreasing credit scores. Deploy predictive models to determine which combination of factors often lead to customers entering the collections process. Using batch, real-time, or hybrid processing methods, risk can be identified early by using predictive risk scores and teams can work with customers to ensure the best outcomes.
2. Optimize payment/settlement offers: Empowering your team to make the right offer at the right time is an essential part of every collection’s strategy. Agents need to be able to see and analyse all data – from all accounts that have previously defaulted across the portfolio. Analytics tools can rapidly gather and assess this data to predict the optimal offer. Predictive analytics can help agents understand what percentage of debt is normally recovered in similar cases and set the benchmark for likely repayment amounts that can be achieved – supporting lower write-offs and protecting loan loss reserves.
3. Optimize contact strategies: To create brand-defining customer experiences, collections teams need to adopt sophisticated and cohesive collections strategies powered by insight. All relevant data and information that informs the best contact method needs to be in one place and easily accessible. This will allow analytics models to be implemented easily, taking into account all documented customer interactions. In a time when customers are choosing which debts to prioritize, creating a brand experience through the tried-and-tested channels (phone, text, email, in-app), can improve engagement, willingness to pay, and customer retention.

We are now in a place where instantly accessible and accurate predictions are now achievable for lenders. With diverse, rich data sources and powerful technology to provide the razor-sharp interpretation of data and insights, lenders can widen the lens on the collections process to maximize the best outcomes for all customers; from Springfield US to Sheffield UK.