An Academic Review: Applications Of Data Mining Techniques In Finance Industry
With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by
Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational
technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get
insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset
of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining
techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by
the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management,
customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to
survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock
prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering
and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been
deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely
followed by Neural Network technique.
This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of
methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in
computational finance for beginners who want to work in the field of computational finance.
Keywords- Data mining, Computational finance, Credit rating, Loan prediction, Money laundering, Stocks prediction.