IBM AML HI-Small dataset adapted for testing machine learning pipelines with consent management. The dataset's original purpose is likely for anti-money laundering research. Its specific adaptation focuses on scenarios involving consent-managed data usage.
Use Cases
- Testing consent-aware data processing pipelines based on the described adaptation for consent-managed ML.
- Benchmarking anti-money laundering (AML) models on a known, adapted financial transactions dataset.
- Simulating data governance workflows in machine learning pipelines based on the consent-management theme.
Strengths
- Based on the established IBM AML HI-Small dataset, which provides a known foundation for AML research.
- Specifically adapted for a defined testing purpose related to consent-managed ML pipelines.
Limitations
- Description metadata is limited; actual data quality, column definitions, and adaptation specifics require manual inspection after download.
- Row count, file formats, and license information are unknown, which may limit suitability assessment.
Provenance
- Source
- IBM AML HI-Small dataset, adapted.
- Collection Method
- Adaptation of an existing dataset for a specific testing purpose.
- Time Range
- null
- Freshness
- Last update date is unknown; freshness unverified.
- Geography
- null