With the emergence of the COVID-19 crisis, the world economy suddenly shook, and the same thing happened in Croatia. Such circumstances brought into question the survival of companies, especially retail, which suffered the most due to the introduction of anti-epidemic measures. Therefore, it is very useful to predict the retail bankruptcy in the mentioned conditions with great precision, in order to prevent bankruptcy with the correct bankruptcy decisions and to gain a competitive advantage in the ideal situation. That is why a stratified sampling of companies is carried out so that the ratio of successful and unsuccessful companies in the wholesale and retail trade sector corresponds to the population with regard to their heterogeneity, and in order to determine the optimal probability threshold for the purposes of classification. A standard and Bayesian logistic regression model is designed and developed with one financial indicator from each group of financial indicators that have proven to be significant for predicting the bankruptcy of wholesale and retail trade companies; current liquidity ratio, debt ratio, short-term asset turnover ratio and EBIT margin. The goal is to determine which financial indicators best discriminate between successful and unsuccessful companies and contribute to a greater predictive ability of the Bayesian approach to logistic regression under conditions of uncertainty. The current asset turnover ratio discriminates best between active and bankrupt companies, while EBIT margins do the least. Furthermore, the logistic regression model predicts with 90.01% accuracy, and the Bayesian logistic regression model with 90.22% accuracy. Such performance of the model indicates greater precision of Bayesian logistic regression, and that it is more suitable for predicting bankruptcy under conditions of uncertainty. The Bayesian logistic regression model predicted bankruptcy two years before bankruptcy of companies with 87.47% accuracy and 76.45% accuracy three years before bankruptcy.