Data Mining Application in Supporting Fraud Detection on Mobile Communication: The Case of Ethio-Mobile
| dc.contributor.advisor | Biru, Tesfaye (PhD) | |
| dc.contributor.author | Gebreselasie, Jember | |
| dc.date.accessioned | 2020-06-09T08:15:48Z | |
| dc.date.accessioned | 2023-11-18T12:45:25Z | |
| dc.date.available | 2020-06-09T08:15:48Z | |
| dc.date.available | 2023-11-18T12:45:25Z | |
| dc.date.issued | 2005-01 | |
| dc.description.abstract | The application of data mining methods and tools that can help to explore large quantities of data generated by the Call Detail Record (CDR) of telecommunication switch machine will be the art of the day to address the serious problems of telecommunication operators, The CDR consists of a vast volume of data set about each call made and it is a major resource of data for research works to find out hidden patterns of calls made by customers in addition to the typical use for bill processing activities, More importantly, data mining techno logy has enabled telecommunication companies to utilize this generic source of data for different kinds of customer relation ship management and marketing activities including fraud detection and prevention Strategies The methodology used for this research had three basis steps these were collection of data, data preparation and model building and testing, The required data set was selected and extracted from the billing data set of Ethio- mobile, Neural network data mining technology were employed to build and test the model s, The c1ata mining mcthod used in this research work have proved to yield comparably suJ'fi cient results for practical u s~ as far as supportive mechanisms are employed fo r the misclass ification of fraudulent customers as non-fraudulent and vice versa, Due to this fa ct the telecornmunication operators should take serious and intensive follow ups for unusually high frequency and long duration of international calls made by some possibly fraudulent customers customers However these lection of representative sample data for the whle database of the problem under consideration needs a series consideration and care to proply sdcct the training data set. Otherwise due to the rare occurrences of possibly fraudulent calk the training of representation data set will be a tedious activity In this research work, the researcher has proved that the CDR is a major resource of or crucial knowledge about customers of Telecomm citation Corporation, Beside that the number of calls made and the duration of each call should be traced to proper Know unhealthy customers of the corporation, Finally it is a rich field for research for other interested researchers to make further and in-depth research to this area , | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21472 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Information Science | en_US |
| dc.title | Data Mining Application in Supporting Fraud Detection on Mobile Communication: The Case of Ethio-Mobile | en_US |
| dc.type | Thesis | en_US |