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September 10, 2025

Exploring Alternative Datasets for Credit Scoring of Thin-File Consumers: A Comprehensive Review

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Alternative datasets improve credit scoring accuracy for thin-file consumers, enhancing financial inclusion.
Integration of machine learning techniques offers predictive capabilities, addressing the challenges of traditional credit scoring.
Hybrid models that combine various datasets outperform legacy models in credit risk assessment and decision-making.
Data privacy and ethical considerations present significant barriers to the adoption of these advanced credit scoring practices.

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Comprehensive review explores alternative datasets and machine learning methods for credit scoring, suggesting improvements for financial inclusion.

Shukla et al. (2025) studied this question.

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Comprehensive review explores alternative datasets and machine learning methods for credit scoring, suggesting improvements for financial inclusion.