Four Fraud Detection Research Papers Worth Reading.
This paper has investigated the various data mining techniques involved in credit card fraud detection and their results, drawbacks and future directions of the credit card fraud detection.
Ekata's products are powered by a team of data scientists and product managers that live and breathe machine learning. Read summaries of a few research papers that cover a variety of machine learning techniques for fraud detection that we’ve found interesting.
View Fraud Detection Research Papers on Academia.edu for free.
Detecting Financial Statement Fraud: Three Essays on Fraud Predictors, Multi-Classifier Combination and Fraud Detection Using Data mining Johan L. Perols ABSTRACT The goal of this dissertation is to improve financial statement fraud detection using a cross-functional research approach. The efficacy of financial statement fraud detection depends.
Credit card fraud and detection techniques: a review Abstract Fraud is one of the major ethical issues in the credit card industry. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. The.
The aim of the research is to distinguish financial ratios, the values of which could indicate the fraud in financial statements. Moreover, the logistic regression model of fraud detection in financial statements has been developed. The research is unique for being the first empirical study of its type in Lithuania.
A Comprehensive Survey of Data Mining-based Fraud Detection Research ABSTRACT This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud.
This research paper explores some of the data mining techniques used for mobile telecommunication, credit card and medical insurance fraud detection as well as the use of data mining for intrusion detection. In addition, it presents a case in which data mining techniques were successfully.
In this paper, we propose a fraud detection method based on the SOM visualization and classification. Consequently, the proposed approach consists of the two main steps: Step 1. SOM visualization of the multidimensional data of the user accounts. Step 2. The actual fraud detection on the basis of the threshold-type binary classification algorithm.
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. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting.
Highlights of fraud research Recent research brings new insights into fraud prevention and detection. By Cynthia E. Bolt-Lee, CPA, CGMA, and Sara Kern, CPA, Ph.D.
This paper discusses automated credit card fraud detection by means of machine learning. In an era of digitalization, credit card fraud detection is of great importance to financial institutions. We apply two machine learning techniques suited for reasoning under. Credit card fraud detection using meta-learning: Issues and initial results free.
An Effective Fraud Detection System Using Mining Technique Syed Ahsan Shabbir, Kannadasan R DSchool of Computing Science and Engineering, VIT University, Vellore, India Abstract- “Detection of fraud in e-commerce payment system” or “An effective fraud detection system using mining technique” is some more related to Mobile computing.
