AI provides collections businesses with a range of tools and capabilities to both humanize the process for customers and enhance collection performance for credit issuers. To realize these benefits, AI tools need to be integrated and used in an efficient and effective way, otherwise known as operationalizing.
AI-tools in their pre-operational state
Machine learning
In the context of collections, machine learning (ML) is a form of AI that specializes in data analysis. It operates based on a set of predetermined rules which come in the form of analytical models created by analysts. These models include historical data and act as the foundation for ML to perform in-depth analysis based on historical trends and patterns which suit each application.
Natural language understanding
Natural language understanding (NLU) is an AI technology that enables human and computer interaction. Technologies that utilize NLU can both interpret human language and formulate responses to a certain degree of complexity. These capabilities are based on predetermined rules and models, as well as some additional help from ML to provide the correct data-driven response. Simply put, NLU is the bridge that connects humans and machines via conversation.
Integrating AI to operational use cases in collections
Segmentation
An operationalized form of ML in the collections process can be found in the example of segmentation. This is the process of identifying risk profiles of customers based on their characteristics compared to historical data. A customers’ risk profile translates to their likelihood to repay a debt, which can be accurately predicted should they exhibit certain characteristics such as outstanding balance, number of missed payments and history of engagement.
Once ML has identified customers by their risk profiles, they’re then segmented into groups based on their likelihood of repayment. Workflows provide specific groups with treatment paths that are likely to lead to debt resolution. This takes place in an automated fashion without the need for a technical specialist or any input from your team, saving valuable resources and enhancing collection performance.
Chat bots
NLU’s primary operationalized application is in the form of chat bots. These are particularly vital in the collections process for a number of reasons:
- Freedom of engagement - Customers that feel uncomfortable directly engaging with your team can communicate with a chat bot to receive guidance, payment offers and plans to help resolve their debt.
- Data-driven responses - Chat bots can recall the vast information stored on each customer, which enables them to provide responses and offers that suit the specific customer.
- Real-time interaction - Chat bots are able to identify and evaluate the probability of a customer repaying their debt, and adjust payment offers and responses in real-time to suit that probability.
Chat bots are a form of operationalized AI that benefit both your team and your customers. They provide self-service opportunities that are data driven to your customers specific needs, and free up time for your team to focus on customers that require human interaction.
Effectively operationalize AI with a configurable collections platform
By operationalizing and integrating AI into the collections process, you can build a stronger level of engagement with each group of your customers and be better equipped to guide them to better financial health.
To effectively operationalize AI, you need a configurable collections platform that acts as a holistic orchestrator of different functions and processes. The C&R Software industry leading Debt Manager fulfills this role by seamlessly integrating a range of AI tools into one configurable place, without the need for technical specialists.
To learn more about the capabilities of Debt Manager and how it can effectively operationalize AI across your processes, contact a member of our team today.
