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

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dc.contributor.authorJadhav, Swati
dc.contributor.authorHe, Hongmei
dc.contributor.authorJenkins, Karl W.
dc.date.accessioned2017-05-18T10:33:30Z
dc.date.available2017-05-18T10:33:30Z
dc.date.issued2017-05-31
dc.description.abstractWith 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.identifier.citationJadhav 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.issn2321-404X
dc.identifier.urihttp://ijscai.iraj.in/volume.php?volume_id=258
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11911
dc.language.isoenen_UK
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectData miningen_UK
dc.subjectComputational financeen_UK
dc.subjectCredit ratingen_UK
dc.subjectLoan predictionen_UK
dc.subjectMoney launderingen_UK
dc.subjectStocks predictionen_UK
dc.titleAn academic review: applications of data mining techniques in finance industryen_UK
dc.typeArticleen_UK

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