Digital Credit Scoring Dataverse is a curated open-access repository dedicated to advancing AI-driven, ethical, and explainable credit scoring frameworks, especially tailored for thin-file and underserved consumers in rural and semi-urban economies. This Dataverse hosts datasets, synthetic benchmarks, modeling workflows, code scripts, evaluation reports, and domain-specific descriptors that underpin data-driven financial inclusion research. It integrates alternative data sources such as mobile transaction records, digital utility payments, behavioral attributes, and remote sensing proxies for livelihood and risk profiling. Rooted in fairness-aware modeling, multi-task learning, and responsible financial AI, the repository aligns with global mandates such as the World Bank’s Digital Financial Inclusion agenda (2025), IMF guidelines on Responsible AI in Finance, and the UN Sustainable Development Goals. All resources emphasize transparency, reproducibility, and equity in algorithmic decision-making. The repository also includes support materials such as data dictionaries, ethical model cards, usage protocols, and evaluation metrics relevant to creditworthiness prediction and sustainability-linked assessments, including carbon scoring. Keywords: Digital Credit, Credit Scoring, Alternative Data, Thin-File Consumers, Machine Learning, Financial Inclusion, ESG, Explainable AI (XAI), Responsible AI, Fairness in AI, Multi-Task Models, Carbon-Aware Scoring.
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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...
Tabular Data - 49.7 KB - 12 Variables, 500 Observations - UNF:6:tIIKPwlCPuBzLVc1RbTvlQ==
DataSynthetic credit score
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