A Comparative Analysis of Machine Learning Algorithms for Subscription fraud Detection: The case of ethio telecom
No Thumbnail Available
Date
2020-02-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
In these days due to the development of affordable technologies, the number
of subscribers and revenue-generating increased over the past few years in the
telecommunication industry. However, advancements of the telecom industry provides
certain appearances that stimulate fraudsters. One of the common and predominant
fraud types is subscription fraud. It is usually the precursor to other
fraud types. Since 2013
subscription fraud is listed as a top-five predominant
fraud type. Subscription fraud alone causes billions of dollar losses of telecomm
companies.
This thesis is conducted on comparative performance of three supervised machine
learning algorithms Artificial Neural Network (ANN), Support Vector Machine
(SVM) and J48
, done using two classification techniques. Before analyzing
and comparing the algorithms Call Detail Record (CDR) data were collected, relevant
features were selected and various preprocessing techniques such as feature
selection, data cleaning, shaping of data frame and feature types were performed.
As a result, J48
algorithm using Cross Validation (CV) options is found to be the
best classifier algorithm by scoring 99
.3
% accuracy followed by the two algorithms
highest scores of ANN ( CV ) and SVM (ST) with 97
.51
% and 96
.0
% respectively.
This result happens because of J48
’s capable of learning disjunctive expressions in
addition to it reduced error pruning. Pruning decreases the complexity in the final
classifier, so that improves predictive accuracy from the decrease of over fitting.
Description
Keywords
telecommunications, CDR, fraud detection, ANN, SVM, J48, Machine learning, accuracy, CV, Supplied Test (ST)