Results 21 to 30 of about 131,240 (196)

A prescription fraud detection model [PDF]

open access: yesComputer Methods and Programs in Biomedicine, 2012
Prescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database.
Aral, K. D.   +3 more
openaire   +6 more sources

Fraud: An SMME perspective

open access: yesThe Southern African Journal of Entrepreneurship and Small Business Management, 2008
Given the important socio-economic role performed by Small, Medium and Micro Enterprises (SMMEs) and the negative consequences of fraud on their businesses, the objective of this study was to investigate the perceptions and management of fraud by SMME ...
Suzette Viviers, Danie Venter
doaj   +1 more source

Fraud detection in energy consumption: a supervised approach [PDF]

open access: yes, 2016
Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter ...
Alcoverro, Santiago   +4 more
core   +1 more source

Document Fraud Detection

open access: yesInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
In today scenario for data and fund transfer we are mainly depended on the internet. So prevention of fraud, abuse and data alteration through internet has become a major concern of many organizations. Our paper focuses the direction towards the document fraud detection.
Ashifa. T, Sathya. R
openaire   +1 more source

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

open access: yes, 2012
With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries.
Panigrahi, Prabin Kumar, Sharma, Anuj
core   +1 more source

An Introduction to Machine Learning Methods for Fraud Detection

open access: yesApplied Sciences
Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their ...
Antonio Alessio Compagnino   +6 more
doaj   +1 more source

A Systematic Literature Review of Fraud Detection Metrics in Business Processes

open access: yesIEEE Access, 2020
Fraud is a primary source of organization losses, amounting to up to 5% of yearly revenues. Process-based fraud (PBF) is fraud involving a deviation from the standard operating procedure (SOP) of business processes.
Badr Omair, Ahmad Alturki
doaj   +1 more source

Assessing and Improving Data Integrity in Web-Based Surveys: Comparison of Fraud Detection Systems in a COVID-19 Study

open access: yesJMIR Formative Research
BackgroundWeb-based surveys increase access to study participation and improve opportunities to reach diverse populations. However, web-based surveys are vulnerable to data quality threats, including fraudulent entries from ...
Stephen Bonett   +7 more
doaj   +1 more source

A machine learning based credit card fraud detection using the GA algorithm for feature selection

open access: yesJournal of Big Data, 2022
The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud.
Emmanuel Ileberi   +2 more
doaj   +1 more source

Search Rank Fraud De-Anonymization in Online Systems

open access: yes, 2018
We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems.
Akoglu Leman   +6 more
core   +1 more source

Home - About - Disclaimer - Privacy