Multi-instance Learning for Bankruptcy Prediction

Forecast of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms and governments. Early identification of firms´ impending failure is very desirable. The scope of this paper is to investigate the efficiency of multi-instance learning in such an environment. For this reason, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 150 failed and solvent Greek firms in the recent period. It was found that multi-instance learning algorithms could enable experts to predict bankruptcies with satisfying accuracy.