Credit risk evaluation by using nearest subspace method

Abstract

In this study, we use a classification strategy called the closest subspace approach to assess credit risk.
Identifying "good" and "bad" creditors via credit risk assessment is a common categorization challenge. There
has been a lot of talk lately about using machine learning techniques like support vector machine (SVM) to
assess credit risk. Yet there is plenty No tried-and-true pattern recognition or AI-based classification techniques
for use in assessing creditworthiness exist. This work proposes using the closest subspace classification
technique, a robust approach to facial recognition, in the context of credit scoring. When evaluating
creditworthiness, the nearest subspace credit evaluation method uses the subspaces spanned by creditors in the
same class to extend the training set, with the Euclidean distance between a test creditor and the subspace serving
as the similarity measure for classification.