A Bayesian dichotomous model with asymmetric link for fraud in insurance

Bermúdez, Lluı́s; Pérez, Joaquín; Ayuso, Mercedes; Gómez, Elena Carretón; Vázquez, Francisco Javier Luna

doi:10.1016/j.insmatheco.2007.08.002

Cited by 54 publications

(28 citation statements)

References 21 publications

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“…Financial fraud is an issue which has serious reaching consequences in both the finance industry and daily life. Therefore, a great number of literatures have been studied on different types of the fraud, like financial statement fraud [7]- [9], credit card fraud [10], [11], insurance fraud [12], [13] and so on. Earilier works mainly use the rule-based methods for fraud detection.…”

Section: A Financial Fraud Detectionmentioning

confidence: 99%

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

Wang

Yuan

Lin

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

281

108

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With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial services, users have rich interactions and they themselves always show multifaceted information. These data form a large multiview network, which is not fully exploited by conventional methods. Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semisupervised attentive graph neural network, named SemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection. Moreover, we propose a hierarchical attention mechanism to better correlate different neighbors and different views. Simultaneously, the attention mechanism can make the model interpretable and tell what are the important factors for the fraud and why the users are predicted as fraud. Experimentally, we conduct the prediction task on the users of Alipay, one of the largest third-party online and offline cashless payment platform serving more than 4 hundreds of million users in China. By utilizing the social relations and the user attributes, our method can achieve a better accuracy compared with the state-of-the-art methods on two tasks. Moreover, the interpretable results also give interesting intuitions regarding the tasks.

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Section: A Financial Fraud Detectionmentioning

confidence: 99%

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

Wang

Yuan

Lin

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

281

108

View full text Add to dashboard Cite

show abstract

“…Previously it was mentioned that early fraud detection research focussed on statistical models and neural networks; however, it may be noted that these methods still continue to be popular. Many used at least one form of neural network [12], [19], [4], some investigated logistic regression [3], [17], [23], [19], while others applied Bayesian belief networks [8], [12], [2]. Application of CDA has been relatively uncommon [4].…”

Section: Classification Based On Detection Algorithmmentioning

confidence: 99%

Intelligent Financial Fraud Detection Practices: An Investigation

West

Bhattacharya

Islam

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract.Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.

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“…In Viaene et al (2005)'s field of automobile insurance fraud, Bermúdez et al (2007) use an asymmetric or skewed logit link to fit a fraud database from the Spanish insurance market. Afterwards they develop Bayesian analysis of this model.…”

Section: Fraud Detection/prevention Literature Reviewmentioning

confidence: 99%

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

Jans¹,

Lybaert²,

Vanhoof³

2009

IJDAR

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Abstract. Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.

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A Bayesian dichotomous model with asymmetric link for fraud in insurance

Cited by 54 publications

References 21 publications

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection

Intelligent Financial Fraud Detection Practices: An Investigation

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

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