RePEc: Research Papers in Economics · 2018 · 24 citations · 0 references
ComputingComputer ScienceData ScienceFeature EngineeringMachine LearningPredictive AnalyticsClassificationCredit ScoringDecision TreesDeep LearningPattern RecognitionSupport Vector MachinesArtificial Intelligence In BankingCredit Risk Machine Learning ModelsDeep Learning-based ClassificationDefault Risk PredictionLoan Default PredictionLogistic RegressionNaive BayesNaive Bayes Classification
In this study, we applied a Deep Neural Networks (DNN) based classification model along with the conventional classification methods (Logistic Regression, Decision Tree, Naïve Bayes and Support Vector Machines) on a two distinct datasets containing characteristics of the loan clients in a medium-sized Turkish commercial bank. Python programming language and libraries (Sklearn, Tensorflow and Keras) have been used in data cleaning, data preparation, feature engineering and model implementation processes. Our empirical findings document that the accuracy of the deep learning classification model increases with the size of the dataset, implying that the deep learning models might yield better results than regression-based models in more complex datasets.