In the digital era, financial institutions face a persistent dilemma: how to manage collections efficiently without compromising the customer experience.
In the digital era, financial institutions face a persistent dilemma: how to manage collections efficiently without compromising the customer experience.
For years, debt collection was considered an operational function, often an uncomfortable one, focused solely on recovering debt. The pressure for immediate results created friction with customers and internal tensions between finance and customer service.
For decades, debt collection has been seen as an operational task—almost a necessary evil. Now, new AI-based technologies have arrived to change the rules of the game for good.
Over the past few years, large language models (LLMs) have dominated the conversation around Artificial Intelligence.
𝐇𝐨𝐰 𝐜𝐨𝐦𝐛𝐢𝐧𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 + 𝐚 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭 𝐢𝐧 𝐛𝐚𝐧𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐟𝐢𝐧𝐭𝐞𝐜𝐡 (𝐰𝐢𝐭𝐡 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲).
AI Under Watch – The Expert Take brings together leading experts to examine the technology, risks, ethical considerations, and implications of algorithmic decision-making. In each edition, we decode what happens behind the code and explore its real-world significance for professionals, businesses, and society as a whole.
In modern organizations, every automated decision represents a customer touchpoint, a value opportunity, or a potential friction source. That’s why decisions shouldn’t be managed in isolation – they must be orchestrated as part of an integrated strategy
Many organizations fell into the “automation at any cost” trap. They implemented automatic processes without considering logic, context or adaptability. The result? Fast but inefficient or insecure systems.
A major concern when implementing AI for critical decisions is losing the ability to explain, audit and control outcomes. The good news? Today you can have fully governed AI that automates decisions without sacrificing traceability or compliance
For years predefined rules were the backbone of decision engines. They’re clear, auditable, and create a sense of control, but they have critical limits.
