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September 10, 2025

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

Key Points

Results indicate that the novel multi-factor credit scoring model improves predictive accuracy, especially for those with thin credit files.
The model outperforms traditional scoring methods, demonstrating the importance of incorporating alternative data sources.
An empirical evaluation using synthetic data illustrates the model's robustness and potential for real-world applications.
This research supports financial inclusion by providing lenders with a reliable tool to assess creditworthiness of thin-file individuals.

Abstract

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.

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