A state-of-the-art techniques on fraud detection in smart meter data analytics
The area of fraud prevention has been traditionally correlated with data mining and text mining. Even before the “big data” phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper review the existing research done in the fraud detection across different application areas with the aim of investigating the techniques used and find out their strengths and weaknesses. It used the systematic quantitative literature review methodology to review the research studies in the field of fraud detection within the last decade using Data analytics techniques. Various combinations of keywords were used to identify the pertinent articles and were retrieved from ACM Digital Library, IEEE Xplorer Digital Library, Science Direct, Springer Link, etc. This search used a sample of 150 relevant articles (peer-reviewed journals articles and conference papers). A comparison of various data analytics techniques was shown across different applications areas. Finally the conclusion and recommendations for further works have identified.
Mon 28 May (GMT+02:00) Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
|11:00 - 11:30|
|11:30 - 12:00|
|12:00 - 12:30|
Andrew LukyamuziMbarara University of Science and Technology, Uganda