Detecting organized eCommerce fraud using scalable categorical clustering

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

Researchers

Research units

Abstract

Online retail, eCommerce, frequently falls victim to fraud conducted
by malicious customers (fraudsters) who obtain goods or services
through deception. Fraud coordinated by groups of professional
fraudsters that place several fraudulent orders to maximize their
gain is referred to as organized fraud. Existing approaches to fraud
detection typically analyze orders in isolation and they are not
effective at identifying groups of fraudulent orders linked to organized
fraud. These also wrongly identify many legitimate orders as
fraud, which hinders their usage for automated fraud cancellation.
We introduce a novel solution to detect organized fraud by analyzing
orders in bulk. Our approach is based on clustering and aims
to group together fraudulent orders placed by the same group of
fraudsters. It selectively uses two existing techniques, agglomerative
clustering and sampling to recursively group orders into small
clusters in a reasonable amount of time. We assess our clustering
technique on real-world orders placed on the Zalando website, the
largest online apparel retailer in Europe1. Our clustering processes
100,000s of orders in a few hours and groups 35-45% of fraudulent
orders together. We propose a simple technique built on top of our
clustering that detects 26.2% of fraud while raising false alarms for
only 0.1% of legitimate orders.

Details

Original languageEnglish
Title of host publicationAnnual Computer Security Applications Conference (ACSAC ’19)
Publication statusAccepted/In press - 2019
MoE publication typeA4 Article in a conference publication
EventAnnual Computer Security Applications Conference - San Juan, Puerto Rico
Duration: 9 Dec 201913 Dec 2019

Conference

ConferenceAnnual Computer Security Applications Conference
Abbreviated titleACSAC
CountryPuerto Rico
CitySan Juan
Period09/12/201913/12/2019

ID: 36937759