Fraud Detection Using Data Mining Techniques

Abstract :

The purpose of this study is to develop a data mining model for fraud detection in various fields such as online transactions (i.e) net banking, tax payers, credit cards etc. and securing data from the third parties(intruders). For fraud detection, very large volumes of data are to be mined and needs some complex mechanism in order to extract full information about those integrators and frauds. It is truly based on baseline study. Some base line studies might help in establishing the extent of fraud detection. These baseline studies uses some current technologies such as ID3 algorithm, Hashing algorithms, Apriori algorithm, query outlier analyses, use case diagrams, pattern reorganization, etc. In order to detect fraud, they depend upon some random audits, informants and some undercover operations. To overcome this problem we used a hashing association mining technique. This study might increase the efficiency and accuracy in fraud identification. For fraud detection, there is no need of any electronically signatures and ensures that some process would not be rejected so that it would increase the performance or working of false alarm rate for fraud detection. Not only have credit cards, but also in every fields such as money laundering, taxed payers and so on. The algorithm is based on the traversal path using hashing approach which is a theoretical approach. Not only managing fraud detection in tax payers but also it is applicable for integrators in network areas in changing data. It uses various tools in finding the frauds and also the integrators. This paper is mainly concentrates on fraud detection and integrators. There is a major need to develop the hierarchical tree structure into different constraints for the fraud detection system. The rate efficiency of this system will determine by analysing raw data and compare with previous techniques. This algorithm will build or improve the alertness by alarm rate which may be classified into two types, one is false alarm rate and the other is actual alarm rate. With the help of these alarm rates, frauds and as well as integrators might be easily detected. So there is a necessity of alarm or alertness for the frauds and integrators.

Authors: E. Henrik, R. Anitha

Article : Fraud Detection Using Data Mining Techniques

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