Detection of Wangiri Telecommunication Fraud Using Ensemble Learning

Fraudsters can manipulate telecom regulatory systems to their advantage, and to the disadvantage of the telecom operator, in ways that are difficult to detect, trace, and prosecute. A subscriber whose network has been compromised will often refuse to pay large fraudulent charges, leaving the operator to cover the bill resulting in revenue losses. Moreover, attacks frequently happen over holidays and weekends, when networks are often less monitored closely. This calls for an intelligent automated solution for telecom fraud detection. Wangiri (Japanese term) telecom fraud also referred to as “one ring and cut fraud” relies on this single ring method for a quick way to make money. Missed calls from unknown callers entice subscribers to call back unknowingly premium numbers where they are deceived to stay on the line for as long as possible in an effort to inflate their bill. This paper proposes the use of varied ensemble classifiers to overcome the highly biased dataset and help make a more precise classification in an efficient and effective manner. Extreme Gradient Boosting algorithm was found to have the best results in terms of correctness and performance.