Study of Fraud Detection using Big Data Approach

Fraud is increasing proportionally with the expansion of cutting edge technology and the e-globalization that cause loss of billions of dollar worldwide each year. In spite of having modern technology and worldwide superhighway communication we are failed to achieve our goal of secure e-globalization. To achieve our goal we need an efficient and effective fraud detection system. Fraud detection is a method of isolating illegal acts that are increasing worldwide. The aim of fraud detection system is to reveal the nature of fraudsters by applying appropriate methodology and specific domain knowledge. The amount of data produced in fraud detection growing large day by day. This cause difficulty to analyze huge amount of data that require more knowledge to gain. Today, in real world to create an efficient fraud detection system it is not enough to apply only data mining technique because data has become an indispensable part of every economy, industry, organization, business function and individual. The Big Data conceive unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation and measurement errors. This paper includes different types of fraud that we may face in our everyday life and how the big data can improve the acceptability of fraud detection system now a days.