Creditprint Eval

---
name: creditprint-eval
description: Evaluates a model's ability to predict user creditworthiness based on geographic mobility footprints. It probes whether spatiotemporal visitation patterns and region-level credit signals can reliably distinguish users who pay their mobile phone bills from those who do not. Use when the user wants to benchmark on Hangzhou user mobility dataset, or asks about evaluating this task. Reports AUC.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1910.08734
  bibtex_key: han2019creditprint
  confidence: high
---

# creditprint-eval

> CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning — Han et al. (2019) (arXiv:1910.08734, 2019)

## What this evaluates

Evaluates a model's ability to predict user creditworthiness based on geographic mobility footprints. It probes whether spatiotemporal visitation patterns and region-level credit signals can reliably distinguish users who pay their mobile phone bills from those who do not.

## Datasets

- **Hangzhou user mobility dataset** — total 5000; splits: train (-1), val (-1), test (-1)

## Metrics

- `AUC` **(primary)** — range: [0, 1]
  - Area Under the Receiver Operating Characteristic Curve, measuring the probability that a randomly chosen positive instance ranks higher than a randomly chosen negative instance across all classification thresholds.

## Input / output format

**Input**: Sequences of 1km×1km geographic regions visited by a user over a one-month period.

**Output**: Binary credit label indicating whether the user pays their mobile phone bill (1) or not (0).

## Scoring recipe

```python
def compute_auc(y_true, y_pred):
    # y_true: ground truth binary labels (1 = pays bill, 0 = does not)
    # y_pred: predicted probabilities of paying the bill
    # Compute Area Under the Receiver Operating Characteristic Curve
    from sklearn.metrics import roc_auc_score
    return roc_auc_score(y_true, y_pred)
```

## Common pitfalls

- The dataset is proprietary and collected from a single mobile operator in Hangzhou, China, limiting generalizability to other regions or operators.
- The credit label is binary (mobile bill payment status) rather than a comprehensive financial credit score, which restricts direct comparison with standard credit scoring benchmarks.
- Manual feature baselines rely on a fixed set of 6 handcrafted mobility features, which may not represent the full state-of-the-art in feature engineering.

## Evidence (verbatim from paper)

> We adopt the widely-used binary classification metric, AUC (Area Under Curve) to verify the effectiveness of user credit investigation.

## Citation

```bibtex
@misc{han2019creditprint,
  title={CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning},
  author={Han et al. (2019)},
  year={2019},
  note={arXiv:1910.08734}
}
```

- arXiv: 1910.08734