Fraud Dataset Benchmark Eval

fraud-dataset-benchmark-eval

Fraud Dataset Benchmark and Applications — Grover et al. (2022) (arXiv:2208.14417, 2022)

What this evaluates

This benchmark evaluates the robustness of fraud detection models to label noise in training data. It measures how effectively various noise-removal techniques preserve predictive performance when tested on clean, unseen data. The protocol specifically probes a model's ability to mitigate artificially injected label corruption across multiple real-world fraud datasets.

Datasets

• Fraud Dataset Benchmark (FDB) — total ?; splits: train (-1), test (-1); repo https://github.com/amazon-research/fraud-dataset-benchmark

Metrics

• ROC-AUC (primary) — range: [0, 1]
  • Area under the Receiver Operating Characteristic curve, measuring the trade-off between true positive rate and false positive rate across classification thresholds.

Input / output format

Input: Tabular feature vectors with potentially noisy training labels; clean ground-truth labels are reserved for the test set.

Output: Predicted probabilities for the positive (fraud) class, or binary fraud/non-fraud predictions.

Scoring recipe

def evaluate(predictions, gold):
    from sklearn.metrics import roc_auc_score
    return roc_auc_score(gold, predictions)

Common pitfalls

• Noise is artificially injected into training labels at fixed rates (10–20%) rather than reflecting natural label noise distributions.
• Evaluation is performed exclusively on clean test data to isolate the effect of training-time noise removal, not to test robustness to noisy test labels.
• Some datasets (e.g., fraudefcom) are highly imbalanced and difficult; without feature engineering, models may perform near random chance regardless of cleaning method.

Evidence (verbatim from paper)

First, in Figure 3 we show the ROC-AUC attained on clean test data by a model trained on data cleaned by the various approaches under consideration. Along the x-axis is the amount of noise added to the training data before cleaning, and the y-axis shows the resulting AUC.

Citation

@misc{grover2022frauddatasetbenchmark,
  title={Fraud Dataset Benchmark and Applications},
  author={Grover et al. (2022)},
  year={2022},
  note={arXiv:2208.14417}
}

• arXiv: 2208.14417