Contributions on Automating the Financial Dunning Process Using Machine Learning

Dunning refers to the process of methodically pursuing payment of a debt. The practice of dunning has been around for centuries, but technological developments, especially in Machine Learning (ML), present a chance to automate and optimize important processes, increasing recovery rates and improving customer satisfaction. The research’s objectives were to learn more about the difficulties arising from the dunning process’s lack of automation through machine learning (ML) and to create a solution that effectively automates the financial process by incorporating ML. To determine the primary obstacles, inefficiencies, and places in the dunning process that are best suited for automation, this article first offers a survey and a review of the literature. It provides a better grasp of how businesses currently manage dunning and the issues that impede productivity by examining industry data and professional viewpoints. The paper expands on these discoveries by offering a specific machine learning-driven automation solution intended to optimize the dunning procedure. By using predictive analytics to improve client segmentation, optimize communication scheduling, and predict payment patterns, this strategy eventually improves operational effectiveness and financial results. The essay illustrates how intelligent automation may make the dunning process a more efficient and data-driven function by bridging the gap between theory and reality.