The concept of financial distress refers to a multidimensional process that arises when firms’ capacity to meet their financial obligations fully and on time weakens and that may often result in bankruptcy. The early identification of this process is of critical importance, particularly for managers, investors, creditors, and regulatory/supervisory authorities. The literature offers a wide range of models developed to predict financial distress, which differ significantly in terms of the methods employed, data structures, and areas of application.

The aim of this study is to examine the principal models developed for financial distress prediction within a systematic framework, to explain their theoretical foundations and methodological characteristics, and to compare their strengths and weaknesses. Accordingly, the study reviews the models proposed by Beaver (1966), Tamari (1966), Altman (1968; 1983; 2000), Meyer and Pifer (1970), Deakin (1972), Sinkey (1975), Martin (1977), Springate (1978), Ohlson (1980), and Fulmer et al. (1984). The historical evolution of the financial distress prediction literature—from univariate approaches to multivariate discriminant analysis and probability-based models—is presented, and comparative inferences across models are drawn.

The overall findings indicate that the identification of corporate financial distress involves a level of complexity that cannot be adequately explained by a single model. While discriminant analysis–based models provide strong classification performance, probability-based models offer more flexible and interpretable results. Consequently, the study concludes that employing multiple models jointly, rather than relying on a single approach, is more likely to yield robust and reliable outcomes in financial distress analysis. In this respect, the study contributes to the literature and provides practical guidance for managers, investors, creditors, and researchers.