Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder (2020)

Abstract: 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.

## introduction in the decade since the global financial crisis, banks and regulators have become increasingly alert to operational risks (or). however, the banks still struggle to deal with operational risks effectively @xcite . it is reported that major banks global wide have suffered nearly $210 billion in operational risk losses since 2011 foot_0 . operational risks refer to the risks of loss due to errors, breaches, interruptions or damages caused by people, internal processes, systems or external events @xcite . one of the daily jobs of risk officers is screening potential operational risks from a massive amount of online news outlets. therefore, there is an urgent need for financial organizations to use artificial intelligence methods for or prediction. while this task can be easily formulated as a classic document classification problem, there are at least two challenges in designing such an intelligent or prediction system. first, acquiring labels from risk officers is time-costly, and there is no standard labeled dataset for this task. second, providing explanations is critical for or prediction as risk officers cannot solely rely on prediction outcomes for subsequent decision making. therefore, these practical issues call for an interpretable semi-supervised text classification framework for or prediction. however, little prior literature has specifically studied these issues in one framework. to tackle the above-mentioned practical challenges, we propose a semi-supervised text classification model based on the semi-supervised variational autoencoder (semivae) @xcite and multi-head attention mechanism @xcite for or prediction task. semi-vae allows effective learning of latent representation from both labeled and unlabeled data, and multi-head attention mechanism produces the direct visualization of informative words associated with multi-label predictive outcomes. learning the model parameters is effective and scalable under the variational inference method. this paper contributes to the burgeoning body of research on using nlp techniques in key financial applications. for example, the prior study leverages the textual features in firm annual reports to predict a firm's stock price volatility using firm annual reports @xcite and earnings announcement transcripts @xcite . other researches make use of news articles and social media data to predict financial markets variables, such as stock return, firm performance, default prediction and market sentiment @xcite @xcite . it is worth emphasizing that the pre-requisites of using nlp in key financial applications are effective and transparent. in many cases, it requires extensive domain expertise to annotate the variable of interests. moreover, figure 1 : the framework of the proposed semiorc. denotes the matrix multiplication. in the training process, the predicted labels ŷi and observed labels y i are used to compute the classification loss for labeled data. the black-box model does not meet the needs for actionable managerial insights. thus, we hope that this work, which aims at addressing common issues in financial nlp system, provides valuable design guidance for financial applications with a significant societal and economic impact. ## the proposed method we now proceed with the details of our model semiorc, and the overall architecture is shown in figure 1 . in a nutshell, semiorc consists of an encoder, a decoder and a semi-supervised classifier. specifically, the encoder network combines the document representation and label embedding to learn latent variables of words. the decoder is used to generate document representation based on these latent variables. we model the semi-supervised classifier by the lstm, the fully-connected layer, and the softmax function. problem definition. let @xmath0. each labeled document d @xmath1, where @xmath2, y 2 , • • • , y r } is a set of r risk labels (e.g., data privacy and bank prosecution, etc.). we consider operational risk classification (orc) problem that labels the unlabeled documents with possible operational risks, i.e., d i (∈ d u ) → ŷi (⊆ y). document representation. in semiorc, we employ a bidirectional lstm (bi-lstm) @xcite as the basic content learning module. let d i be the i-th document with k words and w i,k denotes the one-hot representation of the kth word. we first embed the k-th word into lowdimensional vectors using an embedding matrix m: w i,@xmath3,k m, where w i,@xmath4. then, we use the two-layer bi-lstms as the document encoder to obtain the representation of k-th word by concatenating the forward and backward hidden states of the second bi-lstm layer: the classifier is a semi-supervised classifier, which can predict risk labels and learn the corresponding label embedding based on both labeled and unlabeled document representation. inspired by prior work @xcite @xcite , we incorporate two final states f i and m i into label embedding e i through another bi-lstm, which is beneficial to learn the specific label embedding of i-th document: ## conclusion to conclude, in this paper, we work on a significant practical problem in the financial industry: operational risk prediction. we design a text classification framework with the multi-head attention mechanism and semivae. in sum, our framework aims to address two common issues in the financial industry: lacking labeled data and the need for transparency in prediction outcomes.