Telecom traffic pumping analytics via explainable data science

María Elisa Irarrázaval, Sebastián Maldonado, Juan Eduardo Pérez, Carla Marina Vairetti

Research output: Contribution to journalArticlepeer-review

Abstract

Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for performing supervised learning, and the scarce literature on the topic. We propose a decision support system for fraud detection via clustering and decision trees. After data collection and feature engineering, we group the potential fraud cases into various clusters via an unsupervised learning approach. Then, we constructed a decision tree by using the cluster memberships as labels, evolving into the rules of a given variable and a certain label required for filing lawsuits against the suspicious cases. Telecommunication experts validate these rules to seek a legal resource against alleged perpetrators. We present the results of a case study from a Chilean telecommunication provider. All the lawsuits taken by the legal department were granted, confirming our success in dramatically reducing current and future fraud losses for the company.

Original languageEnglish
Article number113559
JournalDecision Support Systems
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
The authors gratefully acknowledge financial support from CONICYT PIA-BASAL AFB180003 and FONDECYT -Chile, grants 1200221 and 11200007 . The authors are grateful to the anonymous referees for their careful reading and helpful suggestions that improved the paper greatly.

Funding Information:
The authors gratefully acknowledge financial support from CONICYT PIA-BASAL AFB180003 and FONDECYT-Chile, grants 1200221 and 11200007. The authors are grateful to the anonymous referees for their careful reading and helpful suggestions that improved the paper greatly.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • EXplainable AI (XAI)
  • Fraud prediction
  • Interpretable machine learning
  • Telecommunications
  • Unsupervised learning

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