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Fraud Detection is one of the oldest areas of research. The requirement of an effective system that detects frauds effectively with zero loss exists until now. This is due to the increase in the technology, that influences both the ends; the user and the fraudster. Hence it becomes mandatory that the users need to stay a step ahead in this scenario. This paper discusses the changes that had taken place in the area of fraud detection. The flow of research from data mining approaches to machine learning approaches that were developed to defy the attacks are discussed here. It discusses the evolution of heuristic based mechanisms and graph based technologies that emerged in recent years. Further, it also discusses the need for Big Data based analysis in this domain. A few case studies are also discussed here to enable better understanding. Research challenges that exist in this domain in the current scenario are discussed along with the research directions.

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