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Jun 29, 2025
Shukla, Deepa, 2025, "Replication Data for: Credit Scoring of Thin File Consumers", https://doi.org/10.7910/DVN/EGAIKO, Harvard Dataverse, V1, UNF:6:tIIKPwlCPuBzLVc1RbTvlQ== [fileUNF]
Abstract: This dataset contains synthetic data designed for evaluating machine learning-based credit scoring models for thin-file consumers. It includes borrower profiles with alternative data attributes such as digital payments, utility bills, and behavioral features. This dataset supports research in AI-driven credit risk assessment, fairness aud... |
Jul 26, 2024 - Harvard Dataverse
Shukla, Deepa, 2024, "Replication Data for: Alternative Datasets for Credit Scoring of Thin File Consumers", https://doi.org/10.7910/DVN/TJ6RMQ, Harvard Dataverse, V1
Credit scoring is essential in financial services, allowing institutions to assess consumers' creditworthiness. Traditional credit scoring models heavily rely on extensive transaction history, which often poses a significant challenge for thin-file consumers—individuals with limited credit history. This comprehensive review aims to explore and eval... |
May 12, 2024 - Harvard Dataverse
Deepa Shukla, 2024, "Replication Data for: Credit scoring of thin file consumers", https://doi.org/10.7910/DVN/6MLVVI, Harvard Dataverse, V1, UNF:6:tIIKPwlCPuBzLVc1RbTvlQ== [fileUNF]
The rapid evolution of machine learning (ML) offers transformative potential for the credit scoring industry, especially in addressing the challenges faced by "thin-file" consumers who lack substantial credit histories. Traditional credit scoring models often fail to accurately assess these consumers due to insufficient data, leading to potential e... |