scholarly journals A Two-Tier Classification Model for Financial Fraud Detection

2015 ◽  
Vol 118 (19) ◽  
pp. 1-8
Author(s):  
Fazlul Hoque ◽  
Md. Jahidul Islam ◽  
Swakkhar Shatabda
Keyword(s):  
Fraud Detection ◽  
Classification Model ◽  
Financial Fraud

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

From Conventional Methods to Contemporary Neural Network Approaches: Financial Fraud Detection

2021 ◽  
pp. 215-228
Author(s):  
Mustafa Reha Okur ◽  
Yasemin Zengin-Karaibrahimoglu ◽  
Dilvin Taşkın
Keyword(s):  
Neural Network ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Conventional Methods ◽  
Network Approaches

scholarly journals A Financial Fraud Detection Model Based on Organizational Impression Management Strategy

2020 ◽  
Vol 1616 ◽  
pp. 012093
Author(s):  
Jie Zhang ◽  
Jianrong Yao ◽  
Lu Wang ◽  
Yuangao Chen ◽  
Yanqin Pan
Keyword(s):  
Impression Management ◽  
Management Strategy ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Detection Model ◽  
Model Based

scholarly journals The Application of Benford’s Law in Fraud Detection: A Systematic Methodology

2019 ◽  
Vol 12 (10) ◽  
pp. 1
Author(s):  
Nirosh Kuruppu
Keyword(s):  
Fraud Detection ◽  
Benford’S Law ◽  
Financial Fraud ◽  
Spreadsheet Program ◽  
Naturally Occurring ◽  
Systematic Methodology ◽  
Benford's Law ◽  
The Cost ◽  
Financial Transactions

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.

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