# Ieee Cis Fraud Detection Eval

> This evaluation probes a model's ability to detect financial fraud in a federated, privacy-preserving setting using quantum-enhanced neural networks. It measures classification performance on imbalanced transaction data while assessing robustness against simulated quantum hardware noise. Use when the user wants to benchmark on IEEE-CIS Fraud Detection, or asks about evaluating this task. Reports binary classification accuracy.

- Skill: `qhjqhj00/ieee-cis-fraud-detection-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ieee-cis-fraud-detection-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ieee-cis-fraud-detection-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ieee-cis-fraud-detection-eval

---


# ieee-cis-fraud-detection-eval

> QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection — Innan et al. (2024) (arXiv:2404.02595, 2024)

## What this evaluates

This evaluation probes a model's ability to detect financial fraud in a federated, privacy-preserving setting using quantum-enhanced neural networks. It measures classification performance on imbalanced transaction data while assessing robustness against simulated quantum hardware noise.

## Datasets

- **IEEE-CIS Fraud Detection** — total 144233; splits: train (115386), val (28847)

## Metrics

- `binary classification accuracy` **(primary)** — range: [0, 1]
  - Fraction of correctly classified transactions (fraud vs. non-fraud) out of the total validation set instances.
- `MSE` — range: [0, 1]
  - Mean Squared Error between predicted probabilities and actual binary labels, used as the optimization loss.

## Input / output format

**Input**: Preprocessed numerical and categorical features from transaction and identity files, linked by TransactionID. Categorical variables are one-hot encoded, numerical variables are standardized, and the dataset is up-sampled to balance fraud/non-fraud classes.

**Output**: Binary classification prediction (fraud or non-fraud) per transaction instance.

## Scoring recipe

```python
def compute_metrics(predictions, labels):
    accuracy = np.mean(predictions == labels)
    mse = np.mean((predictions - labels) ** 2)
    return {'accuracy': accuracy, 'mse': mse}
```

## Common pitfalls

- The dataset is heavily imbalanced; up-sampling during training can inflate accuracy if the validation set retains the original imbalance or is evaluated without accounting for the sampling strategy.
- Quantum noise parameters range from 0 to 1, but accuracy drops to 0 at maximum noise for some models (e.g., depolarizing), making threshold-based comparisons across noise types sensitive to the exact parameter cutoff.
- Results are averaged over 10 trials with random initialization; reporting a single run without confidence intervals may misrepresent convergence stability.

## Evidence (verbatim from paper)

> We focus on binary classification accuracy and MSE as key metrics. This setup is characterized by 32 initially random parameters, which are optimized through evaluations on a training set comprising 115,386 instances (80% of the total dataset of 144,233 instances) and a validation set comprising 28,847 instances, which is 20% of the total dataset.

## Citation

```bibtex
@misc{innan2024qfnnffd,
  title={QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection},
  author={Innan et al. (2024)},
  year={2024},
  note={arXiv:2404.02595}
}
```

- arXiv: 2404.02595