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---
license: apache-2.0
tags:
- tabular-regression
- finance
- settlement-fails
---
# Fails-Forecasting v1 (XGBoost + LightGBM)
Predicts next-day settlement-fail notional for US Treasuries & corporates.
| metric | value |
|--------|-------|
| MAE | **4.2 bn** |
| RMSE | 5.8 bn |
## Files
* `xgb_next_day_fails_model.joblib` – point prediction
* `lgb_quantile90_next_day_fails.joblib` – 90-percentile upper-bound
<img src="streaming forecaster.PNG" alt="Streaming Fail-Forecaster: training & inference flow" width="80%">
Trained on FINRA FTD + TRACE (2009-2025).
**Usage**
```python
import joblib, lightgbm as lgb, pandas as pd
xgb = joblib.load("xgb_next_day_fails_model.joblib")
lgbm = lgb.Booster(model_file="lgb_quantile90_next_day_fails.txt")
y_hat = xgb.predict(X_new)
y_q90 = lgbm.predict(X_new)
alert = y_hat > y_q90
```
## Citation
> Musodza, K. (2025). Bond Settlement Automated Exception Handling and Reconciliation. Zenodo. https://doi.org/10.5281/zenodo.16828730
>
> ➡️ Technical white-paper & notebooks: https://github.com/Coreledger-tech/Exception-handling-reconciliation.git
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