A data mining approach for branch and ATM site evaluation

Chi Keung Simon Shiu, James N.K. Liu, Jennie L.C. Lam, Bo Feng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In the past, some sites selected for closure by a large international bank in Hong Kong were based on personal experience of a group of experts by formulating a set of evaluation guidelines. The current 300 existing sites are therefore considered to represent a set of rules and expert decisions which are manually recorded on paper files and de-centralized. In order to validate the guidelines/rules and discover any hidden knowledge, we employ a data mining approach to examine the data comprehensively. Several modeling techniques including neural network, C5.0 and General Rule Induction systems are used to determine the significance of those attributes in the data set. Various models based on the historical data set of sites in different forms are constructed to deduce a rule-based model for subsequent use. Promising result has been obtained which can be applied in future Branch and ATM Site Evaluation with a view of providing a better solution. The useful patterns and knowledge discovered will further add benefit to exploring customer intelligence and devising marketing planning strategies.
Original languageEnglish
Title of host publicationData Mining
Subtitle of host publicationTheory, Methodology, Techniques, and Applications
Pages303-318
Number of pages16
Publication statusPublished - 1 Dec 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3755 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Branch and site evaluation
  • Datamining
  • Model analysis
  • Rule induction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this