Big data and its analysis have become a widespread practice in recent times, applicable to multiple industries. Data mining is a\ntechnique that is based on statistical applications. This method extracts previously undetermined data items from large quantities\nof data. The banking and insurance industries use data mining analysis to detect fraud, offer the appropriate credit or insurance\nsolutions to customers, and better understand customer demands. This study aims to identify data mining classification algorithms\nand use them to predict default risks, avoid possible payment difficulties, and reduce potential problems in extending\ncredit. The data for this study, which contains demographic and socioeconomic characteristics of individuals, were obtained from\nthe Turkish Statistical Institute 2015 survey. Six classification algorithmsâ??Naive Bayes, Bayesian networks, J48, random forest,\nmultilayer perceptron, and logistic regressionâ??were applied to the dataset using WEKA 3.9 data mining software. These algorithms\nwere compared considering the root mean error squares, receiver operating characteristic area, accuracy, precision,\nF-measure, and recall statistical criteria. The best algorithmâ??logistic regressionâ??was obtained and applied to the real dataset to\ndetermine the attributes causing the default risk by using odds ratios. The socioeconomic and demographic characteristics of the\nindividuals were examined, and based on the odds ratio values, the results of which individuals and characteristics were more\nlikely to default, were reached. These results are not only beneficial to the literature but also have a significant influence in the\nfinancial industry in terms of the ability to predict customersâ?? default risk.
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