STINM atch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023)

Abstract: Commercial news provide rich semantics and timely information for automated financial risk detection. However, unaffordable large-scale annotation as well as training data sparseness barrier the full exploitation of commercial news in risk detection. To address this problem, we propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph (NEKG) to endorse the risk detection enhancement. The proposed model incorporates a label correlation matrix and interactive consistency regularization techniques into the iterative joint learning framework of text and graph modules. The carefully designed framework takes full advantage of the labeled and unlabeled data as well as their interrelations, enabling deep label diffusion coordination between article-level semantics and label correlations following the topological structure. Extensive experiments demonstrate the superior effectiveness and generalization ability of STINMatch.

## introduction financial risk detection for enterprises is an essential task to assess and estimate the dynamic fragility of the market. efforts need to be made to probe the vulnerable enterprises and enable timely preparedness. traditional methods often treat each enterprise individually and leverage the official information or relevant structured data from government agencies to assess risk @xcite . however, these official data are often biased and lagged, making it difficult to identify risks accurately and timely @xcite . commercial news mining offers another effective perspective for financial risk detection owning to the mas-sive and timely information embedded in news articles @xcite @xcite . nevertheless, challenges are persisting on how to efficiently utilize news for detecting financial risks for enterprises. one of the key issues is the multi-label diffusion problem. one news document may carry multiple risk labels (e.g., a 'debt risk' can be accompanied by 'litigation threat'), and conventional methods are difficult to handle the mutual influences among these labels given the rapid growth and variety of streaming media. recently, deep learning methods have achieved great success in the field of natural language processing. deep multi-label text classification (mltc) methods can be applied to explore the label correlations @xcite . unfortunately, pure text-based methods cannot handle risk diffusion in the business ecosystem. in order to address this problem, we propose a new model, semantic-topological iteration network (stin), to estimate the 'risk diffusion' on the established news-enterprise knowledge graph (nekg). unlike previous graph neural network (gnn) methods for text analysis @xcite @xcite , our stin model focus on the multi-label-correlation guided text-graph joint learning, hoping to capture the dissemination for various types of financial risks following the nekg topological structure. another challenge is the limited annotation data of financial news due to domain expert scarcity or expensive labor cost. semi-supervised learning (ssl) is a common method for solving data scarcity problems. many ssl methods based on entropy minimization, consistency regularization or generic regularization have been proposed for low-resource analysis scenarios @xcite @xcite . however, few studies have been carried out on the semi-supervised integration for text-graph joint learning framework, which could improve the performance for both modules by leveraging the unlabeled data more efficiently in scenarios similar to our task. the contributions are summarized as follows: • we propose a pioneer semi-supervised text-graph joint learning framework stinmatch. it fully exploits the semantic information and topological association for risk diffusion with limited annotation data. ## preliminary in this section, we introduce the task goal of stin-match, detail the nekg construction, and provide an intuitive example for financial risk diffusion. semi-supervised risk diffusion. given a set of news x and risk labels @xmath0, 1} k , the dataset @xmath1, y i )|x @xmath2, y @xmath3, @xmath4, 2, . . . , n} and m unlabeled news d @xmath5, 2, . . . , m}. the risk diffusion model aims to learn the mapping f : @xmath6. nekg construction. the news-enterprise knowledge base is formulated as a graph @xmath7, c, e), where n is a vertex set denoting news, c is a vertex set denoting enterprises, and @xmath8, q), @xmath9, @xmath10, q), @xmath11, @xmath12. among n , each news in d l is annotated with k binary risk labels, represented by a k-hot vector. resource and statistics. specifically, our nekg contains 99,666 news nodes and 50,193 enterprise nodes. each news node is initialized by its title and content, and each enterprise node is initialized by the company name. the edge between a news node and an enterprise node indicates that the enterprise is mentioned in the content of the news. the edges between different enterprises belong to five realworld relationship types: subsidiary, investment, share-manager, share-investor, and share-legalentity. in total, nekg contains 135,340 newsenterprise edges and 121,938 enterprise-enterprise edges. annotation. we sample |d l | = 15, 000 news from all news data, and d l is annotated by three domain experts. each news can be identified as correlating to one or more financial risks from the following labels: bankruptcy, liquidation, business closure, production halts, debt, corruption, dispute, counterfeit, fraud, and litigation. the annotation standard is summarized through three preliminary rounds of annotation with 500 pieces of news. after the adjustment through preliminary rounds, each annotator labels d l independently in more than a month's time, and the annotation results achieve a 0.803 fleiss's kappa. topological labels from diffusion 𝑐 3 𝑛 11 𝑛 12 𝑛 31 𝑐 2 c 1 𝑐 4 𝑛 32 𝑛 33 𝑛 41 semantic labels from news itself news-company relation real-world company relation intuitive example. tional mltc methods only consider the content of the news itself. suppose the n 0 node are connected with three different enterprise nodes c 2,3,4 according to co-occurrence relations. each enterprise node can be connected with other enterprise nodes according to the real-world enterprise relations (e.g. [c 2 , c 1 ]), as well as some extra news nodes (e.g. [c 3 , n 31 ]). the risk information from expanded news can be propagated to the center news node n 0 through the jump enterprise nodes. if most of the neighbor news contain the risk information of liquidation, the probability for the target n 0 news having liquidation risk label also increases. on the other hand, the probability distribution of risk labels for each enterprise node can also be obtained when the diffusion model converges. 4 stinmatch: methodology 4.1 overview architecture. we propose an end-to-end semisupervised semantic-topological iteration network to endorse multiple risk diffusion. as illustrated in fig. 2 , stinmatch is composed of a text classifier (i.e., m t ), a gnn-based node classifier (i.e., m g ), and a label-correlation matrix r. m t contains a base text encoder and several classification layers. it takes textual inputs and learns text embeddings through classification objectives on labeled news. the hidden representations and predictions of m t are utilized to initialize node features of m g at each iteration round. m g propagates risk information from labeled samples to unlabeled samples through nekg topology to achieve risk diffusion and boost risk detection. we iteratively train m t and m g in turn until convergence, and we seek to optimize the integration of the correlation matrix r into an iterative joint learning framework in order to maximize performance and efficiency. upon the diffusion model reaching convergence, the enterprise risk labels are adopted as signals for precisely quantifying risk evaluation. our work advocates for an innovative approach to financial risk detection that integrates label correlation effectively into the text-graph iterative learning process. the integration of label correlation into the iterative learning process offers a mutual benefit, significantly improving the overall performance of the system. on the one hand, the label correlation, serving as domain-specific knowledge, plays an important role to guide the label diffusion for both text and graph modules in each iteration; on the other hand, the predictions of the successively enhanced model help involve more previously unlabeled samples into the calculation of label correlation, resulting in a more generalized label correlation in each iteration round. iterative learning framework. below we elaborate on the iterative semi-supervised learning framework and the workflow from one iteration to the next iteration. we ignore the subscription denoting the iteration round for readability. for a certain iteration round, we denote the key information layer in m t as h t . h t carries two embeddings: text semantic embedding x t , and graph context embedding x g from the last iteration. m t performs data augmentation techniques on both labeled and unlabeled data to compute supervised and unsupervised loss functions for updating model parameters. we detail the module components and semi-supervised learning strategy for m t in section 4.2. after retraining m t , we take the value of its representation layer ψ t as the learned text representation to initialize the node representations h g of m g in this iteration round. the m t 's prediction ℓ t := (p 1 , p 2 , . . . , p k ) t will also participate in the calculation of graph aggregation process. similarly, we train m g with a joint loss function, consisting of a supervised loss and an unsupervised loss, and take the values of its last hidden layer ψ g as learned node representations. then we use ψ g to reinitialize the x g of m t for the next iteration round. we detail the semi-supervised learning of m g and data filtering strategy in section 4.3. with the retrained m t and m g , stinmatch filters ∆n confidently-predicted samples from m unlabeled samples to update the risk label correlation matrix @xmath13,@xmath14, y 2i , . . . , y n i ), (y 1j , y 2j , . . . y n j )⟩, where @xmath15, and (y 1i , y 2i , . . . , y n i ) is the vector consisting of the i-th risk labels for the n samples. r is initially calculated from the labeled set d l and updated in each iteration round. fig. 3 shows a visualized calculation process for r. ## conclusion in this paper, we introduce the nekg for helping detect financial risks from commercial news. the proposed stinmatch method outperforms existing state-of-the-art models on news risk detection task, as well as the downstream enterprise risk evaluation task. such improvements mainly come from 1) additional nekg topology enables article-level risk diffusion; 2) the carefully designed stin model brings deep interactions among semantic module, topological module and label-correlation matrix, enhancing the sound diffusion upon nekg.