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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

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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.

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Implication

This research demonstrates improved predictive accuracy in credit scoring models for thin-file consumers, highlighting financial inclusion implications.

Cite This Study

Shukla et al. (2025) studied this question.

synapsesocial.com/papers/68c1954e9b7b07f3a0618ad8 https://doi.org/10.64820/aepjcser.21.9.20.62025

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Implication

This research demonstrates improved predictive accuracy in credit scoring models for thin-file consumers, highlighting financial inclusion implications.

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

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Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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