Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach

Roux, Daniel de; Pérez, Boris; Moreno, Andrés; Villamil, María del Pilar; Figueroa, César

doi:10.1145/3219819.3219878

Cited by 56 publications

(35 citation statements)

References 18 publications

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“…We leave metadata management from varying systems as future work. Interested readers can refer to specific metadata database, e.g., Project Brick [BETS Research Group, Accessed 1 DEC 2017], CANSIM [Dunstan and Humphrey, 2005] and METeOR [Australian Institute of Health and Welfare, 2018], and metadata standards from ISO and ANSI, e.g., ISO/IEC 11179 [ISO/ IEC JTC 1, 2018].…”

Section: Discussionmentioning

confidence: 99%

“…The first is to conduct TRD through apriori information with additional human effort. Task similarity graphs are given and leveraged to make the model parameters of similar tasks close to each other, based on domain knowledge [Evgeniou and others, 2005;Kato et al, 2008;Han and others, 2014]. However, in real-world applications, such apriori information constructed according to domain knowledge may not be easy to obtain, bringing difficulties to the wide use of such approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Second, historical samples are used in MTL for automatic TRD which reduces the human effort [Zhang et al, 2016;Liu and others, 2017]. However, these methods merely rely on the training samples of the task for relation discovery, which can result in missing intrinsic information in complex systems, as reported in [de Roux and others, 2018].…”

Section: Related Workmentioning

confidence: 99%

“…When it comes to nodes of categorical metadata, there are quantifying approaches including the straightforward 1-hot or 1-of-K encoding, the more-widely-used Gower's similarity coefficient [Gower, 1971] and its extensions [Legendre and Legendre, 2012;Podani, 1999]. However, these approaches basically treat categorical metadata as constant ordinal or numerical ones, which encodes a hidden ranking/ order relation across the metadata and brings noise to the final distance function.…”

Section: Stage Ii: Affinity Measurement With Metadatamentioning

confidence: 99%

“…That is especially critical in multi-task transfer learning scenarios where data samples of targeted tasks are usually insufficient due to sensing networking, or privacy issues, i.e., the data size is too small to automatically extract reliable and valuable intrinsic information without integrating additional domain knowledge. However, in real-world applications, TRD with domain knowledge as apriori information requires non-trivial human effort, bringing difficulties to the wide use of such approaches [Evgeniou and others, 2005;Kato et al, 2008;Han and others, 2014].…”

Section: Introductionmentioning

confidence: 99%

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Metadata-driven Task Relation Discovery for Multi-task Learning

Zheng

Wang

Dai

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Task Relation Discovery (TRD), i.e., reveal the relation of tasks, has notable value: it is the key concept underlying Multi-task Learning (MTL) and provides a principled way for identifying redundancies across tasks. However, task relation is usually specifically determined by data scientist resulting in the additional human effort for TRD, while transfer based on brute-force methods or mere training samples may cause negative effects which degrade the learning performance. To avoid negative transfer in an automatic manner, our idea is to leverage commonly available context attributes in nowadays systems, i.e., the metadata. In this paper, we, for the first time, introduce metadata into TRD for MTL and propose a novel Metadata Clustering method, which jointly uses historical samples and additional metadata to automatically exploit the true relatedness. It also avoids the negative transfer by identifying reusable samples between related tasks. Experimental results on five real-world datasets demonstrate that the proposed method is effective for MTL with TRD, and particularly useful in complicated systems with diverse metadata but insufficient data samples. In general, this study helps in automatic relation discovery among partially related tasks and sheds new light on the development of TRD in MTL through the use of metadata as apriori information.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Stage Ii: Affinity Measurement With Metadatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Metadata-driven Task Relation Discovery for Multi-task Learning

Zheng

Wang

Dai

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels

Deng

Ruan

Zhang

et al. 2020

Lecture Notes in Computer Science

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Automated fraud behaviors detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc, which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. Besides, the continuous and rapid evolution of fraud users makes it hard to find new fraud patterns based on existing detection rules. In our work, we propose a realworld data oriented detection paradigm which can detect fraud users and upgrade its detection ability automatically. Based on the new paradigm, we design a novel fraud detection model, FraudJudger, to analyze users behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from unlabeled data with the help of Adversarial Autoencoder (AAE). Furthermore, FraudJudger can find new fraud patterns from unknown users by cluster analysis. Our experiment is based on a real-world electronic payment dataset. Comparing with other well-known fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. CCS CONCEPTS• Security and privacy → Economics of security and privacy.

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