Linguistic borrowings occur in all languages. Andic languages of the Caucasus have borrowings from different donor-languages like Russian, Arabic, Persian. To automatically detect these borrowings, we propose a logistic regression model. The model was trained on the dataset which contains words in IPA from dictionaries of Andic languages. To improve model’s quality, we compared TfIdf and Count vectorizers and chose the second one. Besides, we added new features to the model. They were extracted using analysis of vectorizer features and using a language model. The model was evaluated by classification quality metrics (precision, recall and F1-score). The best average F1-score of all languages for words in IPA was about 0.78. Experiments showed that our model reaches good results not only with words in IPA but also with words in Cyrillic.