Journal
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Volume -, Issue -, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0219622023400023
Keywords
Financial fraud; COVID-19 pandemic; financial regulatory; graph neural network; big data
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The COVID-19 pandemic has caused economic downturns and accelerated digital transformation, resulting in increased financial fraud motives and more complex fraud schemes. This paper comprehensively analyzes the lessons learned from the pandemic and summarizes the characteristics of financial fraud activities. It also examines the regulatory challenges posed by financial fraud during the outbreak and proposes policy recommendations to improve supervision of fraud activities, including the use of panoramic data and graph-based techniques for fraud detection.
The ongoing coronavirus disease 2019 (COVID-19) pandemic has brought unexpected economic downturns and accelerated digital transformation, leading to stronger financial fraud motives and more complicated fraud schemes. Although scholars, practitioners, and regulators have begun to focus on the new characteristics of financial fraud, a systematic and effective anti-fraud strategy during the pandemic still needs to be explored. This paper comprehensively analyzes the lessons of anti-fraud that we should learn from the COVID-19 pandemic. By exploring the complex motives and schemes of fraud, we summarize the characteristics of financial fraud activities and further analyze the regulatory challenges posed by financial fraud during the outbreak. To better cope with the fraudulent activities during the pandemic, policy proposals on how to improve the supervision of financial fraud activities are put forward. In particular, the panoramic data and graph-based techniques are powerful tools for future fraud detection.
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