Research
Limited liability companies can face serious financial difficulties due to adverse economic conditions or unsuccessful business activities. In severe cases, these challenges may lead to bankruptcy. Early identification of financial distress is therefore essential, as it enables managers and stakeholders to take timely corrective actions and reduce potential losses.
Our research focuses on developing predictive models that can detect signs of bankruptcy several years in advance. Since only a small proportion of companies actually go bankrupt, the problem involves highly imbalanced data — a common challenge in real-world financial analytics. We address this by applying advanced classification and imbalanced learning techniques to financial data from thousands of companies.
To support research transparency and benchmarking, we compiled a comprehensive dataset of small and medium-sized enterprises (SMEs). The dataset is publicly available on the Mendeley Data repository and is documented in detail in our Small- and Medium-Enterprises Bankruptcy Dataset publication in the journal Data in Brief.