Synthetic Financial Transaction Data for AML Research

A synthetic dataset designed for anti-money laundering and fraud detection research. The data is hosted on Kaggle, but the author, organization, and creation date are unknown. The specific number of records and features is not provided.

Use Cases

• Training anomaly detection models based on synthetic financial transaction patterns.
• Benchmarking AML algorithm performance based on simulated transaction data.
• Developing fraud detection systems based on synthetic transaction features.

Strengths

• Data is explicitly designed for AML and fraud detection research.
• The synthetic nature of the data likely avoids privacy concerns associated with real transaction logs.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle

Collection Method

Synthetically generated.