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dc.contributor.authorIkasari, Novita
dc.contributor.authorHadzic, Fedja
dc.contributor.editorAo, S. I., Gelman, L., Hukins, D.W.L., Hunter, A. & Korsunsky, A.M.
dc.date.accessioned2017-01-30T13:31:24Z
dc.date.available2017-01-30T13:31:24Z
dc.date.created2015-03-03T20:17:39Z
dc.date.issued2012
dc.identifier.citationIkasari, N. and Hadzic, F. 2012. Assessment of micro loan payment using structured data mining techniques: the case of Indonesian People' credit bank, in Proceedings of the World Congress on Engineering Vol I WCE 2012, Jul 4-6 2012, pp. 511-517. London, UK: Newswood Ltd.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/32536
dc.description.abstract

Providing financial service to Micro, Small and Medium Enterprises (MSMEs) in Indonesia presents a challenge for small rural banks such as People’ Credit Banks. These banks are required to infer risks about customers’ loan repayment from structured (quantitative, financial) and unstructured (qualitative, non-financial) type of credit information. In this study, the complex nature of credit related information is contextualised and represented in domain specific way using the eXtensible Markup Language (XML). An approach that enables the application of wider selections of data mining techniques on XML data is utilized. Experiments are performed using real world credit data obtained from an Indonesian bank. The results demonstrate the potential of the approach to generate reliable and valid patterns useful for evaluation of existing lending policy.

dc.publisherNewswood Ltd
dc.relation.urihttp://www.iaeng.org/publication/WCE2012/
dc.titleAssessment of micro loan payment using structured data mining techniques: the case of Indonesian People' credit bank
dc.typeConference Paper
dcterms.source.titleProceedings of the World Congress on Engineering 2012
dcterms.source.seriesProceedings of the World Congress on Engineering 2012
dcterms.source.isbn9789881925138
dcterms.source.conferenceWorld Congress on Engineering 2012
dcterms.source.conference-start-dateJul 4 2012
dcterms.source.conferencelocationLondon, UK
dcterms.source.placeHong Kong
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available


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