Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder

2020-07-01 · ACL 2020 6 · Fan Zhou, Shengming Zhang, Yi Yang

Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.

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Classification General Classification Management Semi-Supervised Text Classification text-classification Text ClassificationVariational Inference

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