Debt Collection Model for Mass Receivables Based on Decision Rules—A Path to Efficiency and Sustainability

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amount of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recover at the initial stage of the recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help to avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model also confirms the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess.