Intelligent financial fraud detection practices in post-pandemic era

被引：74
|
作者：
Zhu, Xiaoqian [2 ,4 ,5 ]
Ao, Xiang [1 ,3 ,7 ]
Qin, Zidi [1 ,3 ]
Chang, Yanpeng [4 ,5 ,6 ]
Liu, Yang [1 ,3 ]
He, Qing [1 ,3 ]
Li, Jianping [2 ]
机构：
[1] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
[3] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
[4] Chinese Acad Sci, Inst Sci, Beijing 100190, Peoples R China
[5] Chinese Acad Sci, Inst Dev, Beijing 100190, Peoples R China
[6] Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China
[7] Chinese Acad Sci, Inst Intelligent Comp Technol, Suzhou, Peoples R China
来源：
INNOVATION | 2021年 / 2卷 / 04期
基金：
中国国家自然科学基金;
关键词：
financial fraud detection; COVID-19; pandemic; artificial intelligence; CREDIT-CARD FRAUD; AUTOMOBILE INSURANCE FRAUD; CORPORATE FRAUD; CHOICE MODELS; SOCIAL MEDIA; NETWORK; RATIOS; RISK;
D O I：
10.1016/j.xinn.2021.100176
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Y 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.
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页数：12
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