Transfer Learning Using Logistic Regression in Credit Scoring

TL;DR

This paper explores transfer learning with logistic regression to improve credit scoring accuracy for non-customers by modeling heterogeneity between customer and non-customer populations, demonstrating enhanced classification performance.

Contribution

It introduces a novel transfer learning approach using logistic regression links to better predict creditworthiness of non-customers, extending previous discrimination models.

Findings

Transfer learning improves classification accuracy for non-customer borrowers.

Models based on links between subpopulations outperform traditional methods.

Experimental results on German credit data validate the approach.

Abstract

The credit scoring risk management is a fast growing field due to consumer's credit requests. Credit requests, of new and existing customers, are often evaluated by classical discrimination rules based on customers information. However, these kinds of strategies have serious limits and don't take into account the characteristics difference between current customers and the future ones. The aim of this paper is to measure credit worthiness for non customers borrowers and to model potential risk given a heterogeneous population formed by borrowers customers of the bank and others who are not. We hold on previous works done in generalized gaussian discrimination and transpose them into the logistic model to bring out efficient discrimination rules for non customers' subpopulation. Therefore we obtain several simple models of connection between parameters of both logistic models associated respectively to the two subpopulations. The German credit data set is selected to experiment and to compare these models. Experimental results show that the use of links between the two subpopulations improve the classification accuracy for the new loan applicants.