A Statistical Approach Towards Fraud Detection in the Horse Racing

Min, Moohong; Lee, Jemin Justin; Park, Hyunbeom; Shin, Hyo-Joung; Lee, Kyung-Ho

doi:10.1007/978-3-030-65299-9_15

Cited by 1 publication

(4 citation statements)

References 6 publications

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“…A previous study investigated statistical detection methods in horse racing gambling [6], using data from the Korea Racing Authority to analyze betting patterns. They arranged data at intervals of 15, 30, and 60 min in a graph and then tried to identify a particular set of characteristics or patterns to distinguish normal and abnormal transactions.…”

Section: Statistical Fraud Detection In the Horse Racing Gamblingmentioning

confidence: 99%

“…If the SafeZone had been designed using a statistical approach similar to previous research [6], it could have estimated the number of purchases, which is "y", at the time point of "x". The normal range of the y value is determined by averaging or the MinMax method.…”

Section: Addressing the Issues With A Safezonementioning

confidence: 99%

“…The main difference in this approach compared with the traditional statistical approach for fraud detection [6] is how the SafeZone is decided. Herein, the use of time-series algorithms for predicting the size of bets for an improved SafeLine is proposed, which eventually contributes to the development of the SafeZone.…”

Section: Suggestions For Safeline and Safezonementioning

confidence: 99%

“…Previous research mainly focused on the minimum and maximum range of sales in gambling to construct a SafeZone [6], but this approach reported too many anomalies. To overcome this limitation, this research pursued further steps to detect and monitor anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

Min

Lee

Park³

et al. 2021

Sensors

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During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have place strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

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Section: Statistical Fraud Detection In the Horse Racing Gamblingmentioning

confidence: 99%

Section: Addressing the Issues With A Safezonementioning

confidence: 99%

Section: Suggestions For Safeline and Safezonementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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