Research Project
Large Scale Mining of Banking and Finance Data for Customization of
Face-to-Face and Internet Based Financial Services
Research Goal
Design intelligent techniques which summarize, cluster and predict internet,
branch and region based customer transaction behavior on a range of products
including loan accounts, credit card accounts, saving account, cheque
accounts and insurance.
Funding
$20000
Acknowledgements
The project has been undertaken with a regional financial institution
(name withheld). The support from School of Business, Albury /Wodonga
campus and Dianne McGrath is gratefully acknowledged.
Problem Space
The ability of banks and financial institutions to collect data today
far outstrips their ability to explore, analyze, and understand it. Advances
in technology have enabled businesses today to gather data at an ever-increasing
pace. Data that was measured in gigabytes until recently, is now being
measured in terabytes, and will soon approach the petabyte range. Intelligent
technologies need to be developed to help automate the exploration and
analysis of very large data sets consisting of millions of records and
integration of these technologies with decision making models of financial
services managers. Large scale data mining still poses a number of research
problems. These include issues related to mapping/integration of decision
support models of the users into the data mining process and technologies,
optimisation of the performance of the data mining technologies and load
balancing.
Team Members
Rajiv Khosla, Qiubang Li, Dianne McGrath and staff from the industry partner
Publications
[1] R.Khosla, E Damiani and W. Grosky Human-Centred e-Business, Kluwer
Academic Publishers, Massachusetts, USA, March 2003, 315pages.
[2] R. Khosla, “Multi-layered Distributed Agent Ontology for Soft
Computing Systems.” to appear in International Journal Knowledge-Based
Intelligent Engineering Systems, 2004.
[3] Customer Relationship Management and Internet-banking “chapter
in Human-Centred e-Business, Kluwer Academic Publishers , Massachusetts,
USA, March 2003, 315pages
[4] Q. Li and R. Khosla, "Adaptively Improving Recommendation Quality
of ERecommendation Systems with application in e-Banking,” in IEEE
2003 International Symposium on Computational Intelligence for Measurement
Systems and Applications, IEEE Computer Society Press, Lugano, Switzerland,
pp. 265-70, July 2003.
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