AI and machine learning can play a crucial role in the collection activities of financial institutions due to their ability to analyze vast amounts of data quickly and accurately. By leveraging these technologies, financial institutions can improve their debt recovery processes, identify high-risk customers, and personalize collection strategies. AI-powered algorithms can assess customer behavior patterns, predict payment patterns, and optimize collection efforts, resulting in increased efficiency, reduced costs, and improved overall collection outcomes.
Additionally, AI can help automate routine tasks, freeing up human agents to focus on more complex cases and providing a better customer experience.
Let’s examine typical use cases based on our experience of how financial institutions begin utilizing (or can potentially utilize) AI in their daily practices. We believe these use cases will be much more widespread in the not-too-distant future, providing first-mover advantages to early adopters.
1. Customer Segmentation
AI algorithms can segment customers based on their financial behavior, risk profile, or other criteria. This enables banks to tailor collection strategies and communications according to each segment’s characteristics, increasing the chances of successful debt recovery. We can observe a tendency in the industry of moving from the regular segmentation based on static variables towards VaR (Value at Risk) based segmentation and then with the help of AI to hyper-personalized plans and next best action based approaches, thus providing more bespoke collection strategies for the delinquent clients.
“Do you know what the optimal time and communication method for reaching your delinquent clients is?”
2. Automated Communication
When optimizing communication, it is vital to decide what is the optimal time and communication method to reach the delinquent client, furthermore in case of phone calls the proper prioritization of the calls highly influences the profitability of the collection. The vast amount of available data during collection activities enables AI-based technologies to utilize this data and optimize the communication along the above-mentioned parameters.
Beyond these analytical advantages AI-powered chatbots and virtual assistants can handle routine customer interactions, such as payment reminders, overdue notices, and general inquiries. With the recent rise of ChatGPT and ChatGPT-based services we can clearly see how these systems can provide personalized and timely communication, improving customer experience and increasing efficiency.
3. Payment Arrangement Optimization
AI can analyze customer financial data and preferences to propose personalized payment plans or settlement offers. By considering various factors like income, expenses, and affordability, AI can help create realistic and mutually beneficial payment arrangements without involving human workforce.
“How could proactive measures improve your collection outcomes?”
4. Predictive Analytics for pre-collections
AI algorithms can analyze vast amounts of customer data (especially if the bank collects data about the use of mobile banking apps) to predict payment behaviors and identify customers at higher risk of delinquency. This allows banks to prioritize collection efforts, allocate resources effectively, and take proactive measures to prevent defaults. For example, identifying a group who is often delinquent but pays regularly may not be worth the extra cost and effort, while people with sudden multiple credit line draws might require extra attention.
5. Decision Support, Exit strategy selector
AI systems can provide insights and recommendations to collection agents, helping them make informed decisions. By analyzing customer profiles, payment history, and other relevant data, AI can suggest suitable collection strategies, negotiation approaches, or help in selecting an optimal exit strategy tailored to individual customers.
“How much of your staff’s time is spent on repetitive tasks that could be automated?”
6. Workflow Automation
AI can automate repetitive and time-consuming tasks involved in collections, such as document processing or payment reconciliation. This improves operational efficiency, reduces manual errors, and allows staff to focus on more complex and value-added activities.
Future challenges – explainable AI (XAI)
We have explored numerous use cases above illustrating how AI can genuinely improve the effectiveness of collections. However, it’s important to emphasize that, particularly in financial institutions, it’s crucial to understand the underlying reasons for a decision that might significantly impact the clients’ finances. Black-box models are often criticized from both regulatory and ethical standpoints.
In the context of AI-driven decision-making, it’s important to understand and interpret the reasoning behind the outcomes. By employing explainable AI techniques, financial institutions can provide clear and transparent explanations to borrowers regarding their collection processes and decision-making mechanisms. This helps build trust, ensures fairness, and avoids potential biases or discriminatory practices. Clients have the right to know how decisions are made, and explainable AI enables financial institutions to fulfill this obligation.
Moreover, explainable AI allows financial institutions to identify and mitigate any biases or errors in their collection models. It provides insights into the factors influencing decisions, enabling institutions to address any issues and make necessary adjustments to improve fairness and accuracy.
If you are interested in discussing the opportunities AI offers, please don’t hesitate to contact our expert team.
However, if you’re considering a comprehensive redesign of your collection strategy to make it even more customer-centric, we highly recommend our latest white paper. Start shaping the future of your financial collections today.