IEEE-CIS fraud
==============

Status
------

.. admonition:: Best quality among tested unsupervised baselines, but slow
   :class: warning

   ``RegularizedCauchy`` achieved the best F1, ROC-AUC, and PR-AUC among the
   tested unsupervised baselines, but it was much slower than
   ``IsolationForest``.  This should be reported as a quality/interpretability
   result, not as a speed win.

Why this matters
----------------

IEEE-CIS is a large heterogeneous tabular fraud dataset.  It contains mixed
numeric/categorical behavior, missingness, and fraud signals that are often
better handled by supervised gradient boosting.  This makes it a good stress
case for honest reporting: robust covariance can help, but it is not a magic
solution for all tabular fraud problems.

Result summary
--------------

.. list-table:: IEEE-CIS fraud external benchmark
   :header-rows: 1

   * - Method
     - F1
     - PR-AUC
     - ROC-AUC
     - Seconds
   * - robustcov RegularizedCauchy
     - 0.1550
     - 0.0931
     - 0.7641
     - 1367.0149
   * - sklearn IsolationForest
     - 0.1390
     - 0.0838
     - 0.7387
     - 1.1571
   * - sklearn EllipticEnvelope
     - 0.0914
     - 0.0753
     - 0.7578
     - 3045.0699
   * - sklearn LocalOutlierFactor
     - 0.0633
     - 0.0452
     - 0.6539
     - 27.7558

.. figure:: ../_static/external_results/ieee_cis_fraud/pr_auc.png
   :alt: IEEE-CIS PR-AUC comparison
   :width: 82%

   PR-AUC comparison.  ``RegularizedCauchy`` gives the best quality among these
   unsupervised baselines, but the margin over ``IsolationForest`` is modest.

.. figure:: ../_static/external_results/ieee_cis_fraud/f1.png
   :alt: IEEE-CIS F1 comparison
   :width: 82%

   F1 comparison at the same detection budget.

.. figure:: ../_static/external_results/ieee_cis_fraud/runtime.png
   :alt: IEEE-CIS runtime comparison
   :width: 82%

   Runtime comparison on a log scale.  The large runtime gap is the main reason
   this result is classified as ``competitive/slow`` rather than a strong win.

Output from the run
-------------------

.. literalinclude:: ../_static/external_results/ieee_cis_fraud/output.txt
   :language: text

Interpretation
--------------

This benchmark is useful but should be framed carefully.  ``RegularizedCauchy``
improves unsupervised quality metrics, but the dataset is large and heterogeneous
and the runtime is not yet competitive with ``IsolationForest``.  In practice,
this robust anomaly score is most useful as an additional feature for a larger
fraud pipeline, or as an interpretable unsupervised diagnostic.

Engineering follow-up
---------------------

The next improvement for large Kaggle-style tabular data is a sampled-fit/full-
score mode, for example fitting the robust scatter on 50k representative rows
and scoring all rows.  This would preserve much of the robust-distance signal
while making the workflow much faster.