A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data

Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a synthetic penalized logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients can get stabilized in the recursive procedure, and (iii) reduces the risk of overfitting. We consider a mortgage lending case study using publicly available data to illustrate our method. Results show that errors are smaller in many extreme prediction scores, outperforming a number of existing methods. Our interpretations are consistent with results obtained using a classic econometric model.

Automated summary

A synthetic penalized logitboost method based on weighting corrections is proposed to improve prediction performance and reduce overfitting in binary imbalanced data. Results from a mortgage lending case study demonstrate superior performance in extreme predictions compared to existing methods, with interpretations consistent with a classic econometric model.