Blame for README.md

---
license: cc-by-4.0
pretty_name: Lending Club Model Drift Dataset with Synthetic Outliers
task_categories:
- tabular-classification
tags:
- finance
- credit-scoring
- model-drift
- distribution-shift
- synthetic-data
- outliers
- tabular
size_categories:
- 1M<n<10M
configs:
- config_name: lending_club
  default: true
  data_files:
  - split: train
    path: lending_club/train.parquet
  - split: base_test
    path: lending_club/base_test.parquet
  - split: shock_test
    path: lending_club/shock_test.parquet
- config_name: synthetic
  data_files:
  - split: no_outliers
    path: synthetic/no_outliers.parquet
  - split: outliers_0_01
    path: synthetic/outliers_0_01.parquet
  - split: outliers_0_03
    path: synthetic/outliers_0_03.parquet
  - split: outliers_0_05
    path: synthetic/outliers_0_05.parquet
  - split: outliers_0_07
    path: synthetic/outliers_0_07.parquet
  - split: outliers_0_1
    path: synthetic/outliers_0_1.parquet
  - split: outliers_0_5
    path: synthetic/outliers_0_5.parquet
  - split: outliers_1_0
    path: synthetic/outliers_1_0.parquet
- config_name: lending_club_full_train
  data_files:
  - split: train
    path: lending_club_full/train.parquet
---

# Lending Club Model Drift Dataset with Synthetic Outliers

Data for the paper **"Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers"**, accepted as a poster at the [NeurIPS 2025 Workshop on Generative AI in Finance](https://openreview.net/forum?id=zfTaFD0B5Z).

- **Paper (OpenReview):** https://openreview.net/forum?id=zfTaFD0B5Z
- **Preprint (arXiv):** https://arxiv.org/abs/2510.09294
- **Code:** https://github.com/zypl-ai/stabilization_uplift (version `v1.0`)
- **Metrics package (PyPI):** [`pip install stabilization-uplift`](https://pypi.org/project/stabilization-uplift/)
- **Metrics calculator and `evaluate` module:** [zyplai/stabilization-uplift Space](https://huggingface.co/spaces/zyplai/stabilization-uplift)

The dataset supports the study of model drift in credit scoring: models are trained on a stable period, evaluated before and after a macroeconomic shock, and compared with and without synthetic training data containing different proportions of synthetic outliers. Stability is measured with the Stabilization Score (SS) and Stabilization Uplift (SU) metrics introduced in the paper.

> The paper also reports experiments on proprietary financial datasets from Central Asia and the Caucasus. Those datasets are confidential and are not included here; this dataset covers the experiments on the open Lending Club data.

## Configurations

### `lending_club` (default)

Preprocessed [Lending Club](https://www.kaggle.com/datasets/wordsforthewise/lending-club) loans enriched with macroeconomic features. These are the splits the paper experiments are drawn from.

| Split | Rows | Issue dates | Description |
|---|---|---|---|
| `train` | 50,000 | 2007-07 – 2018-03 | Random sample of the base training period |
| `base_test` | 42,322 | 2016-10 – 2016-12 | Test set from the stable (pre-shock) regime |
| `shock_test` | 42,322 | 2018-04 – 2018-12 | Test set from the shock period |

112 columns: the original Lending Club loan attributes plus `unemployment_rate`, `federal_funds_rate`, `year_month`, `region`, `emp_length_int`, `loan_condition` and `loan_condition_int`.

**How the paper experiments use these splits.** The models are not trained on the full splits: the notebooks draw a random subsample of 4,000 rows from each of `train`, `base_test`, `shock_test` and the synthetic split with `random_state=100` (8,000 rows in the notebook with LightGBM, NGBoost, TabNet, HGBoosting, XGBoost and FT-Transformer). Only the columns of the `synthetic` configuration are used: 21 features and the target for CatBoost and the other models (`issue_d` dropped), 22 features including `issue_d` for TabPFN.

```python
import pandas as pd

REPO = "hf://datasets/zyplai/stabilization-uplift@v1.0"
columns = pd.read_parquet(f"{REPO}/synthetic/no_outliers.parquet").drop(columns="issue_d").columns
train = pd.read_parquet(f"{REPO}/lending_club/train.parquet")[columns].sample(n=4000, random_state=100)
```

**Target:** `loan_condition_int` (1 = bad loan, 0 = good loan). The columns `loan_status` and `loan_condition` are the same label in raw and text form and must not be used as features.

### `synthetic`

Synthetic training data generated with [zGAN](https://arxiv.org/abs/2410.20808) on the Lending Club training data, each with 50,000 rows and 23 columns: 21 features, the issue date `issue_d` and the target `loan_condition_int`.

| Split | Description |
|---|---|
| `no_outliers` | Synthetic data without synthetic outliers |
| `outliers_0_01` … `outliers_1_0` | Synthetic data generated with the outlier proportion parameter 0.01, 0.03, 0.05, 0.07, 0.1, 0.5 and 1.0 |

In the experiments, a subsample of each synthetic split (same size and `random_state` as the real data: 4,000 rows, or 8,000 in the notebook with the other models) is concatenated with the real training sample to train model B, which is compared with model A trained on the real sample only.

### `lending_club_full_train`

The full base training period (1,297,677 rows, same columns as `lending_club`), from which the `train` split was sampled. Provided for further research; it was not used directly in the paper experiments.

## Usage

```python
from datasets import load_dataset

real = load_dataset("zyplai/stabilization-uplift", "lending_club", revision="v1.0")
synthetic = load_dataset("zyplai/stabilization-uplift", "synthetic", revision="v1.0")

train = real["train"].to_pandas()
shock_test = real["shock_test"].to_pandas()
synth = synthetic["outliers_0_05"].to_pandas()
```

The notebooks reproducing the experiments are in the [GitHub repository](https://github.com/zypl-ai/stabilization_uplift).

The SS and SU metrics are available as a Python package, [`stabilization-uplift`](https://pypi.org/project/stabilization-uplift/), whose README includes an end-to-end example on this dataset:

```sh
pip install "stabilization-uplift[shift]"
```

### Building the training data of models A and B as in the paper

The mixes of real and synthetic data used to train model B are not stored separately: they are built from the `lending_club` and `synthetic` configurations. This reproduces the notebooks (CatBoost and other models; for TabPFN keep `issue_d`, and the notebook with LightGBM, NGBoost, TabNet, HGBoosting, XGBoost and FT-Transformer uses `N_SAMPLES = 8000`):

```python
import pandas as pd

REPO = "hf://datasets/zyplai/stabilization-uplift@v1.0"
N_SAMPLES, RANDOM_STATE = 4000, 100


def sample(data):
    return data.sample(n=N_SAMPLES, random_state=RANDOM_STATE)


columns = pd.read_parquet(f"{REPO}/synthetic/no_outliers.parquet").drop(columns="issue_d").columns
train, base_test, shock_test = (
    sample(pd.read_parquet(f"{REPO}/lending_club/{split}.parquet")[columns])
    for split in ("train", "base_test", "shock_test")
)

# Model A is trained on `train`. Model B is trained on a mix of `train` with one synthetic split:
mixes = {}
for split in ["no_outliers", "outliers_0_01", "outliers_0_03", "outliers_0_05",
              "outliers_0_07", "outliers_0_1", "outliers_0_5", "outliers_1_0"]:
    synthetic = sample(pd.read_parquet(f"{REPO}/synthetic/{split}.parquet")[columns])
    mixes[split] = pd.concat([synthetic, train]).reset_index(drop=True).drop_duplicates()
```

Both models are then evaluated on `base_test` and `shock_test`, and compared with the Stabilization Score and Stabilization Uplift.

## Sources and License

- **Lending Club data.** The `lending_club` and `lending_club_full_train` configurations are derived from the [Lending Club dataset on Kaggle](https://www.kaggle.com/datasets/wordsforthewise/lending-club), released there under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). These files remain subject to the original dataset's license and terms of use.
- **Macroeconomic features.** `unemployment_rate` and `federal_funds_rate` were obtained from [FRED](https://fred.stlouisfed.org/), Federal Reserve Bank of St. Louis.
- **Synthetic data.** The `synthetic` configuration is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) by zypl.ai.

## Citation

```bibtex
@inproceedings{varshavskiy2025mitigating,
  title     = {Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers},
  author    = {Varshavskiy, Ilyas and Boboeva, Bonu and Khalilbekov, Shuhrat and Azimi, Azizjon and Shulgin, Sergey and Nizamitdinov, Akhlitdin and S{\'a}ez de Oc{\'a}riz Borde, Haitz},
  booktitle = {NeurIPS 2025 Workshop on Generative AI in Finance},
  year      = {2025},
  url       = {https://openreview.net/forum?id=zfTaFD0B5Z}
}
```

## Authors

Ilyas Varshavskiy, Bonu Boboeva, Shuhrat Khalilbekov, Azizjon Azimi, Sergey Shulgin, Akhlitdin Nizamitdinov (zypl.ai), Haitz Sáez de Ocáriz Borde (University of Oxford, University of Cambridge).