Financial Fraud Transaction Data with 25 Labeled Features

Labeled transactions with 25 features for fraud detection and anomaly analysis. The dataset originates from Kaggle and is labeled for the year 2026. The author, organization, and specific collection methodology are not detailed in the provided metadata.

Use Cases

• Train supervised fraud detection classifiers based on the provided transaction labels.
• Conduct anomaly analysis to identify unusual patterns within the 25 transaction features.
• Benchmark the performance of different machine learning algorithms on a labeled financial fraud task.

Strengths

• Data is explicitly labeled for supervised learning tasks.
• Includes 25 features per transaction, which suggests a multi-dimensional representation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaggle

Time Range

2026