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A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
Jennifer L. Priestley
Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar
Jennifer L. Priestley
In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs …