Emerging Computing Techniques for Credit Scoring of Thin-File Consumers: A Multi-Factor Model with Empirical Evaluation using Synthetic Data

Access to credit is crucial for economic participation, yet traditional credit scoring models often fail those with limited credit history ("thin-file" consumers). This research presents a novel multi-factor credit scoring model specifically designed to address this challenge. Leveraging machine learning and alternative data sources, our model aims to provide a more comprehensive and inclusive assessment of creditworthiness. We detail the model's architecture, data inputs (including synthetic data from the Harvard Dataverse), and algorithm selection, followed by a rigorous performance evaluation. Results demonstrate the model's superior predictive accuracy compared to traditional methods, particularly for thin-file individuals. This research contributes to the growing body of knowledge on financial inclusion and offers a practical solution for lenders seeking to expand credit access responsibly.