We study the operational risk classification problem, a critical yet challenging problem in the banking industry. In practice, banks build supervised multi-label classification models to identify the pre-defined risks using financial news sources. However, the models are often suboptimal due to the lack of labeled data and diverse combinations of risk types. To address these practical issues, we re-frame multi-label supervised operational risk classification as a semi-supervised few-shot learning problem, named MetaRisk, which can then be effectively learned using the prototypical network. We also propose a weighted scheme to help obtain accurately prototype vectors of multi-risk classes. We evaluate the proposed approach MetaRisk using a real-world operational risk classification dataset, and the results demonstrate that it outperforms a set of standard baselines. Especially, MetaRisk is capable of predicting risk types that are new to the system. We expect our work provides a direct and relevant toolkit that may assist risk officers to predict and intervene risks in the banking industry.

我们研究了操作风险分类问题，这是银行业中一个关键但具有挑战性的问题。在实践中，银行建立有监督的多标签分类模型，以使用金融新闻来源识别预先定义的风险。但是，由于缺乏标记数据和风险类型的多种组合，模型通常不是最优的。为了解决这些实际问题，我们将多标签监督的操作风险分类重新构建为半监督的几次快照学习问题，称为MetaRisk，然后可以使用原型网络对其进行有效学习。我们还提出了一种加权方案，以帮助准确地获取多风险类别的原型向量。我们使用真实的操作风险分类数据集评估提出的方法MetaRisk，结果表明它优于一组标准基准。特别是，MetaRisk能够预测系统中新出现的风险类型。我们希望我们的工作提供直接且相关的工具包，可以帮助风险管理人员预测和干预银行业的风险。