A comprehensive survey of fraud detection methods in credit card based on data mining techniques

Alwan, Raghad H.; Hamad, Murtadha M.; Dawood, Omar A.

doi:10.1063/5.0112422

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Gao [24] proposed the SSlsomap activity clustering method, SimLOF outlier detection method, and the Dempster-Shafer Theory-based evidence aggregation method to detect the unusual categories and frequencies of behaviours simultaneously. Alwan [25] shows how combining machine learning techniques with existing methods for detecting fraud can make it easier to find fraud. Specifically, the paper examines the effectiveness of several data mining techniques, including Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Hidden Markov Model, in detecting credit card fraud.…”

Section: Unsupervised Machine Learningmentioning

confidence: 99%

Healthcare insurance fraud detection using data mining

Hamid,

Khalique,

Mahmood

et al. 2024

BMC Med Inform Decis Mak

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Background Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. Methodolgy In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. Results Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. Conclusion The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

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Section: Unsupervised Machine Learningmentioning

confidence: 99%