• Kathy Stares, Executive Vice President of North America at Provenir


• 08.09.2023 06:15 am
#data #analytics

To remain competitive in today’s ever-changing economic environment, financial institutions, energy, telecom, automotive, utilities, and retail finance companies have each recognized the need to build a new collections model that utilizes advanced analytics to inform and drive processes.

Unfortunately, the collections industry has been relatively slow to embrace new techniques in analytics compared to other areas of organizations such as loan origination, as investment in the collections function 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, totaling $17.05 trillion), and the threat of challenging economic conditions on the horizon, collections centers are finally getting the attention they deserve. 

Regulatory concerns, consumer preferences, and increasing consumer debt levels have all created a need to revisit and renew the collections process. In this article, we’ll examine new technologies available, how they can shape and enhance the collections process, and ways collections centers can utilize new technology to create win-win opportunities for customers and creditors.

Advanced Analytics and Technology for Next-Generation Collections

Advancements in analytical methods and machine learning, coupled with digital technologies, have created new opportunities to enable more effective and efficient collections processes, and revolutionize the way lenders interact with customers. 

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, creditors can optimize the customer experience, the chance of collecting, and reduce 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 enables 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. 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.

Additionally, using advanced analytics and technology advancements such as artificial intelligence and machine learning enables financial institutions to migrate to a deeper, more informed treatment of 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. 

Key Data Elements of the Next-Generation Collections Model

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. 

• On-us behavioral data includes the customer’s payment history, delinquency history, and returned checks, among other attributes.

• Off-us behavioral data involves third-party data sources that provide insights into a customer’s financial obligations and commitments, as well as updates on their behavior based on almost real-time updates. 

• Previous contact history data is critical in learning from previous contact attempts and modifying the treatment approach accordingly. 

• Socio-demographic data can be used to build customer profiles to assist in selecting the appropriate channel of communication.

Leveraging these various data sources and applying advanced analytics allows organizations 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.

Gain Deeper Insights with Automated Decisioning

It may seem daunting to implement more advanced technologies in a collections strategy, but the role of an automated decision engine is key. Combined, real-time data and automated risk decisioning enhance the collections process in the following six ways:

1. Prioritization of Debtors: Use machine learning algorithms to analyze payment history, financial status and other data to immediately predict likelihood of default or late payment and allows organizations to prioritize collection efforts to improve efficiency and effectiveness.

2. Personalized Collection Strategies: Tailored treatment strategies mean more effective outcomes and higher recovery rates.

3. Real-Time Decision Making: Making decisions in real-time allows organizations to move quickly and adjust collection strategies as new data becomes available.

4. Reduced Operational Costs: Limit the need for manual work and enable 24/7 operations without additional staffing costs, thanks to automation of decisions, real-time data integration, and machine learning optimizations.

5. Improved Compliance: Automated risk decisioning processes, for collections or otherwise, can be programmed to follow relevant regulations and policies (allowing for regional differences too), and reduces the risk of non-compliance.

6. Enhanced Customer Experience: No one enjoys the collections process, but the more personal, respectful, and appropriate the treatment strategy, the more easily you can preserve the customer relationship.

In today’s dynamic, rapidly changing market, it’s imperative for collections professionals to recognize the transformative potential of analytics and leverage it 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 organizations aren’t adapting to the conditions, their competition will.