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Banks utilize credit scoring as an important indicator of financial strengthand eligibility for credit. Scoring models aim to assign statistical odds orprobabilities for predicting if there is a risk of nonpayment in relation tomany other factors which may be involved in. This paper aims to illustrate thebeneficial use of the eight machine learning (ML) methods (Support VectorMachine, Gaussian Naive Bayes, Decision Trees, Random Forest, XGBoost,K-Nearest Neighbors, Multi-layer Perceptron Neural Networks) and LogisticRegression in finding the default risk as well as the features contributing toit. An extensive comparison is made in three aspects: (i) which ML models withand without its own wrapper feature selection performs the best; (ii) howfeature selection combined with appropriate data scaling method influences theperformance; (iii) which of the most successful combination (algorithm, featureselection, and scaling) delivers the best validation indicators such asaccuracy rate, Type I and II errors and AUC. An open-access credit scoringdefault risk data sets on German and Australian cases are taken into account,for which we determine the best method, scaling, and features contributing todefault risk best and compare our findings with the literature ones in related.We illustrate the positive contribution of the selection method and scaling onthe performance indicators compared to the existing literature.

The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring