An explainable hybrid fraud detection system combining a Variational Autoencoder (VAE) and XGBoost. The VAE learns the underlying patterns of non-fraudulent ERP financial transactions and generates reconstruction errors and latent features, which are then used to enhance the accuracy of an XGBoost classifier. The system integrates SHAP and LIME based explainable AI to provide transparent and interpretable fraud predictions.
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