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Jun 29, 2025 -
Replication Data for: Credit Scoring of Thin File Consumers
Plain Text - 35 B -
MD5: 9e90a7ca8f0bb96008c5a2f4bc274e77
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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... |
MS Excel Spreadsheet - 12.9 KB -
MD5: 9747d03475c95d6ef173f3414edb3e79
The findings indicate that social media data provides valuable insights into consumer behaviour and financial reliability, while web browsing patterns and digital footprints offer additional dimensions to assess creditworthiness. Telecom data, including call records and mobile payment history, has proven to be a reliable indicator of financial beha... |
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... |
May 12, 2024 -
Replication Data for: Credit scoring of thin file consumers
Tabular Data - 49.7 KB - 12 Variables, 500 Observations - UNF:6:tIIKPwlCPuBzLVc1RbTvlQ==
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