An Enhanced Comparative Assessment of Ensemble Learning for Credit Scoring

One of the most important aspects of financial risk is credit risk management. Effective credit rating models are crucial for the credit institution in assessing credit applications, they have been widely studied in the field of statistics and machine learning. Given that small improvements in credit rating systems can generate significant profits, any improvement is of high interest to banks and financial institutions. The ensemble methods are a set of algorithms whose individual decisions are combined to perform classification tasks. In this work, we propose an enhanced experimental comparative study of five ensemble methods associated with seven base classifiers using six public credit scoring datasets. Four popular evaluation metrics, including area under the curve (AUC), accuracy, false positive rate (FPR) and Time taken to build the model, are employed to measure the performance of models. The experimental results and statistical tests show that Pegasos model has a better overall performance than the other methods analyzed her for Boosting and Credal Decision Tree (CDT) model has a better overall performance than the other algorithms in the case of Bagging, Random Subspace, DECORATE and Rotation Forest.