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Naeem Abraham | 07 February, 2023

Strengthen your debt collection operation with transformation technology

Factors including the pandemic, rise of short-term payment plans and increasing cost of living has catalyzed the global rise of household debt. More customers have entered collections than ever before, highlighting the inadequacy of traditional collections processes to meet the challenges today’s consumers present.

Technology has aided current collections teams in adapting to these new challenges with tools that improve efficiency, effectiveness and provide a more customer-centric experience. These tools are constantly evolving to suit new and different needs; customer communication preferences are changing, and the modes of accessing credit are becoming more technology-orientated and wide-spread. Organizations collecting debt need to not only bolster their collections performance but make smart utilization of artificial intelligence (AI) to optimize results.

What are the prominent forms of technology used within collections?

Machine learning

Machine learning (ML) acts as a foundation for other forms of AI and technology within the collections industry. Through the thorough analysis of customer data, ML provides accurate predictive analytics on delinquency risk of customers. Characteristics such as customer balance, account lifetime, and quantity of missed payments are compared with vast amounts of historical data. Arming collection teams with insights that inform educated decisions make the collection process more personal, humanized and productive.

ML is an invaluable tool for collections teams because it gives them the ability to segment their customers into groups and match them with relevant treatment plans that guide them through the collections process. For example, a customer with a significant balance and multiple missed payments will likely require direct help from an agent, whereas another customer with significantly lower balance will likely need a basic SMS message to resolve their situation. ML can automate this process and bring efficiency to operations while providing customers with support which best suits their situation.

Self-service chatbots

Another branch of AI which is becoming an increasingly popular part of an effective collections strategy is self-service chatbots. These chatbots are able to interpret text and language and can conduct conversations with customers without the need of an agent. ChatGPT is becoming more prevalent in modern media for their impressive language interpretation ability and potential as a tool across a long list of industries. Questions remain with regards to ChatGPT in collections. Will AI be able to interpret tone of voice and the complexities often presented?

Chatbots can provide up-to-date information regarding customer balance, contact details, and present pre-determined offers for outstanding payments. Their ability to provide customers with a specific treatment path based on the predictors of ML, and then offer multiple options to suit the customer’s situation are still being reviewed.

Collections teams can utilize self-service AI to automate messages to customers based on factors like debt cycle, data predictors or workflows implemented by the collections team. This is particularly effective for early stage arrears; if a customer misses their first payment, AI can automate a predetermined offer via their preferred communication channel. As a result, collections teams can save considerable time and resources via self-service while providing customers with self-serving capabilities and the support they need.

Audio analytics

As collections teams use AI and ML to manage more portfolios of delinquent accounts, the ability to effectively communicate becomes increasingly convoluted. Agents using an intuitive user interface are a good option for the more complex cases since they offer a broad understanding of business processes, compliance and customer-centric communications. But emerging audio analytics are beginning to play a role here as well.

Audio analytics is a form of AI that provides a better understanding of calls and customers. It can interpret language, tone and patterns to distinguish emotion and analyze speech in real-time. From this data, the AI can identify vulnerable customers based on their speech, identify whether scripts have been followed, and provide insight to better improve future communications. This information can be stored in a centralized system to make it accessible and easier to identify vulnerable customers and improve training standards.

Leading the charge of technology in collections

These technologies act in unison to improve the efficiency of collections teams and processes. ML is able to perform advanced data analytics, chatbots give customers the ability to self-serve and automate messages, and audio analytics provides invaluable data to improve their approach to customer calls.

The Debt Manager collections platform provides all three of these features in a seamless package that reduces the time spent looking at data and maximizes the time spent working directly with customers. To learn more about how the AI-driven technology of Debt Manager can enhance your collections, contact us today.

 Naeem Abraham
About the author

Naeem Abraham

Naeem Abraham is leading the charge to implement our decision management tool: FitLogic. With prior experience at a top EMEA bank, Naeem’s expertise lies in credit management, data-driven decisioning, and utilizing AI/ML to improve collections performance.

A guide maximizing customer experience during debt collection
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A guide to maximizing customer experience during debt collection

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