Deep Transfer Learning for Cross-Market Credit Scoring in Multinational Financial Institutions

Abstract

In today's globalized economy, Multinational Financial Institutions (MFIs) face the challenge of accurately assessing creditworthiness across different markets characterized by diverse economic conditions, regulatory frameworks, and consumer behaviors. Traditional credit scoring models, typically developed for specific regions, fail to generalize well across countries. This paper proposes a novel deep transfer learning (DTL) framework tailored for cross-market credit scoring. By leveraging domain adaptation and knowledge transfer techniques, the proposed model utilizes data-rich markets to enhance predictive performance in low-resource target markets. We present an extensive experimental evaluation on multi-country credit datasets and demonstrate significant improvements in accuracy and robustness compared to traditional machine learning and baseline deep learning methods. The proposed approach comprises components such as shared feature extractors, domain-specific classifiers, and adversarial domain discriminators, all designed to facilitate effective knowledge transfer. We also explore data normalization techniques, missing value handling, and feature alignment strategies crucial for achieving generalization. Furthermore, this paper discusses practical considerations for deploying such models in real-world MFIs, including regulatory compliance, interpretability, and scalability. Our findings suggest that DTL can bridge data scarcity gaps and improve credit inclusion efforts in underrepresented regions.