--- 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 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).