Bagging Supervised Autoencoder Classifier for Credit Scoring

TL;DR

This paper introduces the Bagging Supervised Autoencoder Classifier (BSAC), a novel model that improves credit scoring accuracy by addressing data imbalance and feature heterogeneity through autoencoder embeddings and bagging techniques.

Contribution

The paper presents a new ensemble classifier that combines supervised autoencoders with bagging and undersampling to enhance credit scoring performance on imbalanced datasets.

Findings

BSAC outperforms traditional models on benchmark datasets.

The approach effectively handles data imbalance and feature heterogeneity.

Experimental results demonstrate robustness and improved accuracy.

Abstract

Credit scoring models, which are among the most potent risk management tools that banks and financial institutes rely on, have been a popular subject for research in the past few decades. Accordingly, many approaches have been developed to address the challenges in classifying loan applicants and improve and facilitate decision-making. The imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring datasets, pose difficulties in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. In this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC) that mainly leverages the superior performance of the Supervised Autoencoder, which learns low-dimensional embeddings of the input data exclusively with regards to the ultimate classification task of credit scoring, based on the principles of multi-task learning. BSAC also addresses the data imbalance problem by employing a variant of the Bagging process based on the undersampling of the majority class. The obtained results from our experiments on the benchmark and real-life credit scoring datasets illustrate the robustness and effectiveness of the Bagging Supervised Autoencoder Classifier in the classification of loan applicants that can be regarded as a positive development in credit scoring models.