Tax evasion, an unlawful practice in which taxpayers deliberately conceal information to avoid paying tax liabilities, poses significant challenges for tax authorities. Effective tax evasion detection is critical for assisting tax authorities in mitigating tax revenue loss. Recently, machine-learning-based methods, particularly those employing positive and unlabeled (PU) learning, have been adopted for tax evasion detection, achieving notable success. However, these methods exhibit two major practical limitations. First, their success heavily relies on the strong assumption that the label frequency (the fraction of identified taxpayers among tax evaders) is known in advance. Second, although some methods attempt to estimate label frequency using approaches like Mixture Proportion Estimation (MPE) without making any assumptions, they subsequently construct a classifier based on the error-prone label frequency obtained from the previous estimation. This two-stage approach may not be optimal, as it neglects error accumulation in classifier training resulting from the estimation bias in the first stage. To address these limitations, we propose a novel PU learning-based tax evasion detection framework called RR-PU, which can revise the bias in a two-stage synergistic manner. Specifically, RR-PU refines the label frequency initialization by leveraging a regrouping technique to fortify the MPE perspective. Subsequently, we integrate a trainable slack variable to fine-tune the initial label frequency, concurrently optimizing this variable and the classifier to eliminate latent bias in the initial stage. Experimental results on three real-world tax datasets demonstrate that RR-PU outperforms state-of-the-art methods in tax evasion detection tasks.

In noisy label learning, estimating noisy class posteriors plays a fundamental role for developing consistent classifiers, as it forms the basis for estimating clean class posteriors and the transition matrix. Existing methods typically learn noisy class posteriors by training a classification model with noisy labels. However, when labels are incorrect, these models may be misled to overemphasize the feature parts that do not reflect the instance characteristics, resulting in significant errors in estimating noisy class posteriors. To address this issue, this paper proposes to augment the supervised information with part-level labels, encouraging the model to focus on and integrate richer information from various parts. Specifically, our method first partitions features into distinct parts by cropping instances, yielding part-level labels associated with these various parts. Subsequently, we introduce a novel single-to-multiple transition matrix to model the relationship between the noisy and part-level labels, which incorporates part-level labels into a classifier-consistent framework. Utilizing this framework with part-level labels, we can learn the noisy class posteriors more precisely by guiding the model to integrate information from various parts, ultimately improving the classification performance. Our method is theoretically sound, while experiments show that it is empirically effective in synthetic and real-world noisy benchmarks.

In label-noise learning, accurately identifying the transition matrix is crucial for developing statistically consistent classifiers. This task is complicated by instance-dependent noise, which introduces identifiability challenges in the absence of stringent assumptions. Existing methods use neural networks to estimate the transition matrix by initially extracting confident clean instances. However, this extraction process is hindered by severe inter-class imbalance and a bias toward selecting unambiguous intra-class instances, leading to a distorted understanding of noise patterns. To tackle these challenges, our paper introduces a Class Rebalance and Geometric Regularization-based Framework (CRGR). CRGR employs a smoothed, noise-tolerant reweighting mechanism to equilibrate inter-class representation, thereby mitigating the risk of model overfitting to dominant classes. Additionally, recognizing that instances with similar characteristics often exhibit parallel noise patterns, we propose that the transition matrix should mirror the similarity of the feature space. This insight promotes the inclusion of ambiguous instances in training, serving as a form of geometric regularization. Such a strategy enhances the model's ability to navigate diverse noise patterns and strengthens its generalization capabilities. By addressing both inter-class and intra-class biases, CRGR offers a more balanced and robust classification model. Extensive experiments on both synthetic and real-world datasets demonstrate CRGR's superiority over existing state-of-the-art methods, significantly boosting classification accuracy and showcasing its effectiveness in handling instance-dependent noise.

In complementary-label learning (CLL), the complementary transition matrix, denoting the probabilities that true labels flip into complementary labels (CLs) which specify classes observations do not belong to, is crucial to building statistically consistent classifiers. Most existing works implicitly assume that the transition probabilities are identical, which is not true in practice and may lead to undesirable bias in solutions. Few recent works have extended the problem to a biased setting but limit their explorations to modeling the transition matrix by exploiting the complementary class posteriors of anchor points (i.e., instances that almost certainly belong to a specific class). However, due to the severe corruption and unevenness of biased CLs, both anchor points and complementary class posteriors are difficult to predict accurately in the absence of true labels. In this article, rather than directly predicting these two error-prone items, we instead propose a divided-T estimator as an alternative to effectively learn transition matrices from only biased CLs. Specifically, we exploit semantic clustering to mitigate the adverse effects arising from CLs. By introducing the learned semantic clusters as an intermediate class, we factorize the original transition matrix into the product of two easy-to-estimate matrices that are not reliant on the two error-prone items. Both theoretical analyses and empirical results justify the effectiveness of the divided- T estimator for estimating transition matrices under a mild assumption. Experimental results on benchmark datasets further demonstrate that the divided- T estimator outperforms state-of-the-art (SOTA) methods by a substantial margin.

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.

The identification of tax evasion plays an important role in ensuring tax order, promoting the level of tax collection and management, and reducing tax losses. With the advancements in data mining technology, many machine learning techniques have yielded results in identifying tax evasion. However, to realize satisfactory performance, these models require large amounts of human annotated data. In the tax field, unlabeled tax data are abundant, data annotation in a single region is expensive, and the distributions of characteristics differ among regions; these factors pose substantial difficulties in the development of an identification model. Existing tax evasion detection methods are either trained for single-region tasks, in which case they perform poorly on inter-region tax evasion identification due to the discrepancies in feature distributions, or utilize labeled data from both the target-task field and different but related auxiliary fields to reuse and transfer knowledge of the target domain data, in which case they cannot deal with scenarios in which there are no labeled data in target audit tasks. Although current unsupervised transfer learning techniques can train models in labeled regions for unlabeled regions, large intra-class distribution discrepancies cannot be perfectly minimized in tax evasion detection scenarios. To better address the above challenges, this paper proposes a general architecture, namely, the unsupervised conditional adversarial networks (UCAN) for tax evasion detection, which is the first approach to solve audit tasks in unlabeled target domains via inter-region transfer. Our architecture establishes an adversarial neural network adding label information in the distribution adapter, which can granularly adapt the joint probability distribution (JPD) of the data. We introduce a constraint that is based on the conditional maximum mean discrepancy (CMMD) of the extracted features to align the conditional probability distribution (CPD) of the deep representation. Our model is formed by combining the distribution adapter and the label predictor to realize end-to-end learning of unsupervised feature transfer. The experimental results demonstrate the outstanding performance of our model in all migration tasks compared with state-of-the-art approaches.

Accurate industry classification of national economic activities as an important component in the construction of economic structure and as the basis of the formulation of economic policies and management of national economic activities has been gaining increasing attention. However, owing to the rapid growth in the number of industries, it is become increasingly difficult for tax bureaus to classify the registered taxpayers' industries. Conventional industrial classification methods only focus on the text features, which can not be analyzed and judged comprehensively according to the registration information, and can only carry on single-label classification since they neglect the primary and secondary relationships between the main and subsidiary industries, which can not meet application requirements. To better address these challenges, this paper proposes a model known as attention based bidirectional RNN for hierarchical industry classification (ABR-HIC), which is the first approach, to the best of our knowledge, to simultaneously address comprehensive registration information utilization and multi-label classification for the main and subsidiary industries. Our architecture establishes a bidirectional RNN using a word-attention mechanism, which is able to capture and fully utilize the text and non-text registration information for feature representation. By separating the taxpayer's primary and secondary multi-label classification problem corresponding to the main and subsidiary industries, respectively, into two subtasks and through multi-task learning, our model can provide comprehensive primary and secondary multi-industrial labels. Experiments were conducted on real tax data-sets of the Shaanxi Province, China and the results demonstrate the outstanding performance of our architecture in terms of both the classification effect and training time compared with those of state-of-the-art approaches.

In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.