---
tags:
- fraud-detection
- tabular-classification
- lightgbm
- shap
library_name: sklearn
---

# FraudLens fraud detector (lightgbm, version 2)

Credit card fraud scoring model from **FraudLens** (https://github.com/YASHR2002/fraudlens): leakage-free behavioural
features, a cost-based decision threshold, SHAP explanations and LLM analyst notes.
This repo is what the public demo API downloads at startup.

| | Test set (fraudTest, used once) | Validation |
|---|---|---|
| PR-AUC | 0.9745 | 0.9853 |
| Recall at threshold | 95.2% | 97.6% |
| Precision at threshold | 86.1% | 90.1% |
| Total cost (missed fraud + $5 per false alarm) | $25,150 | $2,975 |

Decision threshold: **0.4326** (minimises total cost on validation).

## Contents

- `champion/model/`: scikit-learn pipeline in MLflow format, serialised with **skops**
  (load with the trusted types listed in `fraudlens.models.estimators.SKOPS_TRUSTED_TYPES`).
- `champion/metadata.json`: version, threshold, metrics, feature importance, fairness.
- `features.json`: the 18 input features with plain-English descriptions.
- `state/`: per-card state snapshot (end of the training data) and demo transactions.

## Data and limitations

Trained on the synthetic Sparkov dataset (CC0), so scores are higher than real fraud detection
would achieve; card numbers and personal fields in the demo files are synthetic. Gender is not a
model input; age is, and fairness gaps by gender and age are reported in the project's model card.
Not for production use.