Assessment of micro loan payment using structured data mining techniques: The case of Indonesian people’ credit bank

Novita Ikasari, Fedja Hadzic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering 2012, WCE 2012
EditorsLen Gelman, Andrew Hunter, A. M. Korsunsky, S. I. Ao, David WL Hukins
PublisherNewswood Limited
Pages511-517
Number of pages7
ISBN (Print)9789881925138
Publication statusPublished - 1 Jan 2012
Event2012 World Congress on Engineering, WCE 2012 - London, United Kingdom
Duration: 4 Jul 20126 Jul 2012

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2197
ISSN (Print)2078-0958

Conference

Conference2012 World Congress on Engineering, WCE 2012
Country/TerritoryUnited Kingdom
CityLondon
Period4/07/126/07/12

Keywords

  • Credit risk assessment
  • Data mining techniques
  • Database structure model
  • Indonesian people’ credit bank
  • XML

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