Developing an AI Model for the Detection of Financially Distressed Companies by Belgian Commercial Courts

Abstract

Financial distress among companies poses a significant challenge to economic stability. Timely and effective intervention is needed. In Belgium, the Chambers for Companies in Difficulty (CCDs) within commercial courts play a crucial role in detecting and addressing financial distress through both preventive and regulatory measures. However, the current manual selection process for identifying companies at risk, based on so-called ‘red flags’, is resource-intensive and inconsistent across CCDs.

This paper explores the potential of artificial intelligence (AI) to improve the efficiency and objectivity of the CCD’s selection process. Using machine learning techniques, we are currently developing an AI model to assist the CCD in prioritising companies for review by ranking them according to urgency and providing an indication of the likely CCD decision. The model aims to streamline the selection process, reduce judicial workload, and enable CCDs to focus on companies requiring urgent attention.

As a contribution to the growing field of AI-driven legal decision support systems, our research offers insights for policymakers and courts seeking to integrate AI into insolvency proceedings. The proposed AI model will be a supportive tool to enhance efficiency and consistency; final decisions will always be made by CCD judges, thus preserving judicial discretion and ensuring procedural fairness.