Developing and Improving Risk Models using Machine-learning Based Algorithms

TL;DR

This study develops risk classification models for business delinquency using various machine learning algorithms, optimizing hyper-parameters, and applying ensemble methods to improve predictive performance.

Contribution

It introduces a comprehensive approach combining regularization, hyper-parameter tuning, and ensembling to enhance risk model accuracy for business delinquency prediction.

Findings

Bagging on KNN with 9 neighbors achieved 0.90 accuracy.

Optimal base classifiers identified include LR, KNN, DT, and ANN.

Ensemble methods improved model performance significantly.

Abstract

The objective of this study is to develop a good risk model for classifying business delinquency by simultaneously exploring several machine learning based methods including regularization, hyper-parameter optimization, and model ensembling algorithms. The rationale under the analyses is firstly to obtain good base binary classifiers (include Logistic Regression ($LR$), K-Nearest Neighbors ($KNN$), Decision Tree ($DT$), and Artificial Neural Networks ($ANN$)) via regularization and appropriate settings of hyper-parameters. Then two model ensembling algorithms including bagging and boosting are performed on the good base classifiers for further model improvement. The models are evaluated using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC of ROC), recall, and F1 score via repeating 10-fold cross-validation 10 times. The results show the optimal base classifiers along with the hyper-parameter settings are $LR$ without regularization, $KNN$ by using 9 nearest neighbors, $DT$ by setting the maximum level of the tree to be 7, and $ANN$ with three hidden layers. Bagging on $KNN$ with $K$ valued 9 is the optimal model we can get for risk classification as it reaches the average accuracy, AUC, recall, and F1 score valued 0.90, 0.93, 0.82, and 0.89, respectively.