  Today, machine learning is applied in almost any field. In machine learning,
where there are numerous methods, classification is one of the most basic and
crucial ones. Various problems can be solved by classification. The feature
selection for model setup is extremely important, and producing new features
via feature engineering also has a vital place in the success of the model. In
our study, fraud detection classification models are built on a labeled and
imbalanced dataset as a case-study. Although it is a natural language
processing method, a customer space has been created with word embedding, which
has been used in different areas, especially for recommender systems. The
customer vectors in the created space are fed to the classification model as a
feature. Moreover, to increase the number of positive labels, rows with similar
characteristics are re-labeled as positive by using customer similarity
determined by embedding. The model in which embedding methods are included in
the classification, which provides a better representation of customers, has
been compared with other models. Considering the results, it is observed that
the customer embedding method had a positive effect on the success of the
classification models.
