Detecting fraudulent bookings of online travel agencies with unsupervised machine learning

Caleb Mensah, Jan Klein*, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Online fraud poses a relatively new threat to the revenues of companies. A way to detect and prevent fraudulent behavior is with the use of specific machine learning (ML) techniques. These anomaly detection techniques have been thoroughly studied, but the level of employment is not as high. The airline industry suffers from fraud by parties such as online travel agencies (OTAs). These agencies are commissioned by an airline carrier to sell its travel tickets. Through policy violations, they can illegitimately claim some of the airline’s revenue by offering cheaper fares to customers. This research applies several anomaly detection techniques to detect fraudulent behavior by OTAs and assesses their strengths and weaknesses. Since the data is not labeled, it is not known whether fraud has actually occurred. Therefore, unsupervised ML is used. The contributions of this paper are, firstly, to show how to shape the online booking data and how to engineer new and relevant features. Secondly, this research includes a case study in which domain experts evaluate the detection performance of the considered ML methods by classifying a set of 75 bookings. According to the experts’ analysis, the techniques are able to discover previously unknown fraudulent bookings, which will not have been found otherwise. This demonstrates that anomaly detection is a valuable tool for the airline industry to discover fraudulent behavior.

Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence
Subtitle of host publicationFrom Theory to Practice : 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Graz, Austria, July 9–11, 2019, Proceedings
EditorsFranz Wotawa, Ingo Pill, Roxane Koitz-Hristov, Gerhard Friedrich, Moonis Ali
PublisherSpringer Verlag
Pages334-346
Number of pages13
ISBN (Electronic)9783030229993
ISBN (Print)9783030229986
DOIs
Publication statusPublished - 2019
Event32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019 - Graz, Austria
Duration: 9 Jul 201911 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11606 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019
CountryAustria
CityGraz
Period9/07/1911/07/19

Keywords

  • Airline
  • Anomaly detection
  • Fraud
  • Online travel agent
  • Unsupervised learning

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