Intelligent financial fraud detection practices in post-pandemic era

Abstract

The great losses caused by financial fraud have attracted continuous attention from academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus pandemic (COVID-19) unexpectedly shocks the global financial system and accelerates the use of digital financial services, which brings new challenges in effective financial fraud detection. This paper provides a comprehensive overview of intelligent financial fraud detection practices. We analyze the new features of fraud risk caused by the pandemic and review the development of data types used in fraud detection practices from quantitative tabular data to various unstructured data. The evolution of methods in financial fraud detection is summarized, and the emerging Graph Neural Network methods in the post-pandemic era are discussed in particular. Finally, some of the key challenges and potential directions are proposed to provide inspiring information on intelligent financial fraud detection in the future.

Graphical abstract

Public summary

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  Financial fraud in the post-pandemic era is becoming more sophisticated and insidious

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  We reiew the development of financial fraud detection from data and method perspectives

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  Graph neural network methods are emphasized due to their capacity for heterogeneous data analysis

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  Future directions of financial fraud detection are discussed from task, data, and model-oriented perspectives