The concept of gender created by the society referencing to biological sex, and the rules, sanctions, anticipations, officials put on it, is a question that crowns injustice towards women today. This problem has caused and sustained great injustices and losses not only in daily life but also in economical area. In this study, it was tried to draw attention to the fact that the study that we are currently doing are shared so that the society should be shaken as soon as possible and away from the "gender" perception. The purpose of this study is to identify data mining classification algorithms that can be used to predict default risks using data on demographic and socioeconomic characteristics of individuals, to avoid possible payment difficulties and to reduce the problems that may arise when lending. Also going into default risks for women and men are examined and so indeed it was found that women are more sensitive to their repayments. From this point of view, variables affecting the going into default rate of women were examined.