Data Mining Techniques for Financial Fraud Detection
Keywords:
Data mining, Financial Fraud, Fraud detection techniquesAbstract
This presented article mainly circumspect the idea of the increasing number of frauds in recent times. Frauds can deliberately cause accident for payout or intentional losses. With all the different methods of fraud, detection still becomes an upheaval task. In this article, we shed light on the various frauds that take account and the fraud detection techniques used with the help of data mining. The broad-based definition of data mining is ‘processes and activities designed to obtain and evaluate data to extract useful information’. It is definitely something very important when it comes to detection, which can result in taking immediate action to minimize cost.
References
- Prof. Gupta Rajan, Gill N.S. 2012 "Data Mining Techniques–A Key for detection of financial statement fraud
- D.WHITLEY,"Genetic Algorithm And Neural Network."2003
- H.C. Koh, C.K. Low, Going concern prediction using data mining technique 2004
- Maes, S., Tuyls, K., and Vanschoenwinkel, B., Machine Learning Techniques For Fraud Detection. 2000
- Hoogs Bethany, Thomas Kiehl, Christina Lacomb and DenizSenturk(2007). A Genetic Algorithm Approach to Detecting Temporal Patterns Indicative Of Financial Statement Fraud
- P.Ravisankar, V. Ravi, G.RaghavaRao, I., Bose, Detection of financial statement fraud and feature selection using data mining techniques,Decision Support System. 2011
- Jenson, F.V., An Introduction To Bayesian Networks. 1998
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT

This work is licensed under a Creative Commons Attribution 4.0 International License.