An academic review: applications of data mining techniques in finance industry

Show simple item record

dc.contributor.author Jadhav, Swati
dc.contributor.author He, Hongmei
dc.contributor.author Jenkins, Karl W.
dc.date.accessioned 2017-05-18T10:33:30Z
dc.date.available 2017-05-18T10:33:30Z
dc.date.issued 2017-05-31
dc.identifier.citation Jadhav S, He H, Jenkins K, An academic review: applications of data mining techniques in finance industry, International Journal of Soft Computing and Artificial Intelligence, Volume 4, Issue 1, Pages 79 – 95. en_UK
dc.identifier.issn 2321-404X
dc.identifier.uri http://ijscai.iraj.in/volume.php?volume_id=258
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/11911
dc.description.abstract 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. en_UK
dc.language.iso en en_UK
dc.rights Attribution-NonCommercial 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
dc.subject Data mining en_UK
dc.subject Computational finance en_UK
dc.subject Credit rating en_UK
dc.subject Loan prediction en_UK
dc.subject Money laundering en_UK
dc.subject Stocks prediction en_UK
dc.title An academic review: applications of data mining techniques in finance industry en_UK
dc.type Article en_UK


Files in this item

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International

Search CERES


Browse

My Account

Statistics