Abstract
This paper conducted a comprehensive scientometric review of Fraud Detection between 1984 and 2021 to depict the landscapes, research hotspots, and emerging trends in this field. Besides scientific outputs evaluation using statistical analysis and comparative analysis, scientometric methods such as co-occurrence analysis, cocitation analysis, and coupling analysis were used to analyze the knowledge structure of Fraud detection. Results showed that Fraud Detection research went up significantly in the past two decades, in addition to conventional scientometric results, keywords with the strongest citation burst such as Intrusion Detection, Audit Planning, Pattern Recognition, Data Mining, Insurance Fraud, Benfords Law, Business Intelligence, Outlier Detection, Genetic Algorithm, Big Data, and Deep Learning, demonstrate the emerging trends of Fraud Detection.
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