Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

Issa, Hussein; Vasarhelyi, Miklos A.

doi:10.2139/ssrn.1910468

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…1. Support Vector Machine (SVM): SVMs are familiar with the domain containing ordinary occasions utilizing a single class learning technique (Issa & Vasarhelyi, 2011). It is generally used in one class to detect the anomaly.…”

Section: Classification-basedmentioning

confidence: 99%

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Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

et al. 2021

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The next wave in smart transportation is directed towards the design of renewable energy sources that can fuel automobile sector to shift towards the autonomous electric vehicles (AEVs). AEVs are sensor-driven and driverless that uses artificial intelligence (AI)-based interactions in Internet-of-vehicles (IoV) ecosystems. AEVs can reduce carbon footprints and trade energy with peer AEVs, smart grids (SG), and roadside units (RSUs). It supports green transportation vision. However, the sensor information, energy units, and user data are exchanged through open channels, and thus, are susceptible to various security and privacy attacks. Thus, AEVs can be remotely operated and directed by malicious entities that can propagate false updates to the peer nodes in IoV environment. This can cause the failure of components, congestion, as well as the entire disruption of IoV network. Globally researchers and security analysts have addressed solutions that pertain to specific security requirements, but still, the detection and classification of malicious AEVs is a widely studied topic. Malicious AEVs exhibit an anomaly behavior that differentiates them from normal AEVs, and thereby, the detection of anomalous AEVs and classification of anomaly type is required. Motivated from the aforementioned facts, the survey presents a systematic outlook of AI techniques in anomaly detection of AEVs.A solution taxonomy is proposed based on research gaps in the existing surveys, and the evaluation metrics for AI-based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique. Thus, the proposed survey is designed to guide the manufacturing industry, AI practitioners, and researchers worldwide to formulate and design accurate and precise mechanisms to detect anomalies.

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Section: Classification-basedmentioning

confidence: 99%

“…The initial stage is to prepare a neural system on the named information occasions and becomes familiar with the typical classes, and afterward, new cases are presented. In any case, the occurrence is dismissed by the neural system, then it is named anomaly (Issa & Vasarhelyi, 2011).…”

Section: Clustering-basedmentioning

confidence: 99%

Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

et al. 2021

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“…Various aspects of anti-money laundering are currently being investigated. For example, the study of bank risk management (Bauer & Ryser, 2004), the study of the risk theory features (John H. Boyd & Gianni De Nicolo, 2005), consideration of bitcoin user identification in terms of identifying shortcomings in identification and transaction history (Reid & Harrigan, 2011), and studying the algorithms for detecting anomalies in customers' transactions in the field of banking and detecting illegal cash flows (Issa & Vasarhelyi, 2011). Improving public administration in the field of combating money laundering is an area of research for public administration and law professionals (Baranov, 2015;Bashtannyk, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Formation of a new paradigm of anti-money laundering: The experience of Ukraine

Vitvitskiy

Kurakin²,

Pokataev

et al. 2021

Problems and Perspectives in Management

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The increase in the level of money laundering is associated with digitalization and technification of all spheres of society, the globalization of financial markets, the consequences of quarantine measures caused by the COVID-19 pandemic, and the new ways of committing money laundering crimes. The paper aims to identify the peculiarities of anti-money laundering activities in Ukraine and outline approaches to increase the effectiveness of combating money laundering in the country. The current state of the problem of money laundering is analyzed based on the study literary sources, regulatory framework and their discussion in the Ukrainian society. An attempt was made to systematize the factors influencing the increase in the level of money laundering. The consequences of increasing the level of money laundering for Ukraine have been determined: a shortfall in state budget revenues, a decrease in the level of the social sphere financing, reduced living standards of the population. It has been emphasized that there is a need for a comprehensive approach to the problem of money laundering, which will include ongoing training for financial audit specialists, the establishment of special units to investigate money laundering crimes, enshrining the classification of money laundering crimes in regulatory documents and the establishment of criminal liability for their commission.

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“…Its neural network consisted of a hidden layer and the same number of input and output neurons. Outlier detection, classification and clustering methods were discussed in [24].…”

Section: Literature Reviewmentioning

confidence: 99%

A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions

Kargari¹,

Eshghi²

2018

World Congress on Electrical Engineering and Computer Systems and Science

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In recent years, fraud in banking transactions has turned into a serious problem for which different supervised and unsupervised algorithms have been suggested. In this paper, a semi-supervised combined model based on clustering algorithms and association rule mining is devised in order to detect frauds and suspicious behaviors in banking transactions. To this end original and non-fraud transaction data of the customers is collected for the analysis. Next, repetitive patterns of customer behaviors are extracted through association rules and used as normal rules so that any new transaction must conform to at least one of these rules. In behavior analysis component, a fuzzy clustering algorithm is employed to extract the normal behavior patterns of customers. Abnormal transactions belong to none of these clusters and will be recognized as high risk. The final understanding of a transaction will be gained through combining the results of association rules and clustering patterns. Findings suggest that the employment of both rule-based and clustering-based components leads to the detection of more frauds while fewer alarms will go off.

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Application of Anomaly Detection Techniques to Identify Fraudulent Refunds

Cited by 15 publications

References 19 publications

Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey

Formation of a new paradigm of anti-money laundering: The experience of Ukraine

A Model Based on Clustering and Association Rules for Detection of Fraud in Banking Transactions

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