scholarly journals Intelligent Financial Fraud Detection Practices in Post-Pandemic Era: A Survey

2021 ◽  
pp. 100176
Author(s):  
Xiaoqian Zhu ◽  
Xiang Ao ◽  
Zidi Qin ◽  
Yanpeng Chang ◽  
Yang Liu ◽  
...  
2021 ◽  
pp. 215-228
Author(s):  
Mustafa Reha Okur ◽  
Yasemin Zengin-Karaibrahimoglu ◽  
Dilvin Taşkın

2020 ◽  
Vol 1616 ◽  
pp. 012093
Author(s):  
Jie Zhang ◽  
Jianrong Yao ◽  
Lu Wang ◽  
Yuangao Chen ◽  
Yanqin Pan

2019 ◽  
Vol 12 (10) ◽  
pp. 1
Author(s):  
Nirosh Kuruppu

Benford’s Law relies on a recently proven mathematical distribution about the frequencies of naturally occurring numbers that can be efficiently applied to the detection of financial fraud. Despite the value of Benford’s Law for detecting fraud, most financial professionals are often unaware of its existence and how to best utilise the method for fraud detection. The purpose of this paper is therefore to present a systematic methodology for incorporating Benford’s Law for detecting and flagging potentially fraudulent financial transactions, that can be further investigated. This paper describes the development of Benford’s Law and demonstrates how it can be implemented systematically through a spreadsheet program to detect potential fraud. Given that the cost of financial fraud is significant with firms losing up to a tenth of their revenues, the methodology presented in this paper for implementing Benford’s Law can be a valuable tool for auditors and other financial professionals for detecting fraud.


2015 ◽  
Vol 118 (19) ◽  
pp. 1-8
Author(s):  
Fazlul Hoque ◽  
Md. Jahidul Islam ◽  
Swakkhar Shatabda

Author(s):  
Prof. Sangeetha J. ◽  
Jegatheesh B. S. ◽  
Balaji B ◽  
Hemnath N

Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.


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