Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.creator | Lam, Lai-ching Jennie | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/3143 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Branch and ATM site evaluation (BASE) using data mining techniques | en_US |
| dcterms.abstract | This dissertation concerns a Branch and ATM Site Evaluation in retail banking using data mining techniques. In the past, some sites selected for closure by a large international bank in Hong Kong were based on personal experience from a group of experts by formulating a set of evaluation guidelines. The existing sites are therefore considered to represent a set of rules and expert decisions which are manually recorded on paper files and de-centralized. As data mining has grown its popularity in decision-making processes, applying this advanced technology in business computing to explore the real-world raw data can provided an insight on the validity of the above guidelines/rules and discover any hidden knowledge which would otherwise remain undetected. In this dissertation, a proven and readily made data mining software known as "Clementine V4.0" was used to analyse data of the existing sites. These data consist of various attributes based on transactions performed at Automatic Teller Machines and their physical locations. These are further derived, filtered and examined by adopting a series of manipulation, graph and output nodes for extensive exploratory analysis. Several modeling techniques, that is, neural network, C5.0 and General Rule Induction systems were used to determine how significant the attributes are in the training and testing data set. The results of exploratory analysis and modeling do support two important attributes - distance and cash withdrawal count per site - in the site evaluation. These also provide interesting hidden thresholds for various sets of sites with distinct patterns/features, such as branch sites in strategic locations, unmanned sites n prestige areas, hospitals and dedicated areas. Exceptions to the mined threshold, rules and guidelines are detected for establishment of future large branch in new sites, closure of existing ones or re-configuration of those sites with extremely low transactions. The major benefit of this study is to examine the relationships among strategic decisions, management assumptions and historical data to see whether there is any gap between them, leading to a reformulation of strategic decisions as well as adjustment in management assumptions. | en_US |
| dcterms.extent | xi, 111, [44] leaves : ill. (some col.) ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Banks and banking -- Location -- China -- Hong Kong -- Data processing | en_US |
| dcterms.LCSH | Data mining -- China -- Hong Kong | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15395145.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 8.53 MB | Adobe PDF | View/Open |
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