Navigating the Credit Landscape with Minimal Data: A Transfer Learning and Image-Based Classification Strategy

Abstract

The rise of financial technologies has transformed access to financial services, introducing diverse products such as microloans, personal loans, and insurance. While this progress enhances financial inclusion, it also presents a significant challenge: accurately assessing credit risk for individuals with limited credit histories and businesses managing complex data. To address this issue, we propose a novel transfer learning framework for credit risk prediction using minimal data. Our approach involves converting sparse tabular customer data into image format and fine-tuning pre-trained image classification models, including VGG-16, ResNet-50, and GoogleNet. Additionally, we explore training these models from scratch on source domain data, followed by fine-tuning with target domain data. Rigorous testing on public and specialized datasets demonstrates the robustness of our method in handling data scarcity. Our framework consistently outperforms benchmark transfer learning algorithms in prediction accuracy, showcasing its potential to bridge the gap in financial risk assessment and promote broader financial inclusion. This study highlights the innovative application of transfer learning in addressing critical challenges in the financial sector, offering a scalable and effective solution for credit risk evaluation in data-constrained environments.