Fraud detection in e commerce

Fraud Detection in E-Commerce

Mohit Kumar/Disha Makhija

Types of Fraud in E-Commerce Marketplace

● Buyer fraud○ Credit Card Fraud○ Reseller Fraud○ COD/RTO Fraud○ Product Exchange Fraud

● Seller fraud○ Reviews/Ratings Fraud○ Fake Listing○ Price Abuse (MRP abuse)○ Brand Infringement○ Seller Protection Fund Fraud

Reviews and Ratings Fraud Detection in E-Commerce

Disha MakhijaOngoing collaboration with: Prof Christos Faloutsos’s group (CMU)

Product Ratings, Reviews and Upvotes

Motivation for Product Ratings Fraud: Conversion vs Rating

Lower rated products have

conversion drop while higher

rated products have

conversion increase:

• Direct financial motivation

for sellers/publishers to

boost their products

Seller Ratings

1 product, multiple sellers,comparable prices

Who would you buy from???

Seller Rating Fraud - Simple Pattern

Seller Rating Fraud: Boosting Self Pulling Competition

Product Review Fraud Detection: Boosting products

Review & Ratings Fraud Literature

2011 2013 20142012

Ott et al., ACL

Bing et al., ICDM

Bing et al., EMNLP,

Christos et al., AAAI,

Christos et al., KDD

Bing et al., ICDM

NLP based

ML based

Network AnalysisChristos et al., WWW

Bing et al., AAAI

Bing et al., KDD

A technique based on synchronous and abnormal behaviour : CatchSync

● Idea : users intending fraudulent activities tend to behave synchronously, which is not quite normal

CatchSync- Catching Synchronized Behavior in Large Directed Graphs, Faloutsos et al,KDD 2014

One user, different sellers but competition!!

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A technique based on synchronous and abnormal behaviour : CatchSync

● Idea : users intending fraudulent activities tend to behave synchronously, which is not quite normal

CatchSync- Catching Synchronized Behavior in Large Directed Graphs, Faloutsos et al,KDD 2014

Precision for Top K Users: 48%

12

Review graph based online store spammer detection

Review graph based online store spammer detection, Bing et al, ICDM 2011 13

Review graph based online store spammer detection

Precision for Top K Users: 39%

Review graph based online store spammer detection, Bing et al, ICDM 2011 14

● Precision @ top 100 Users reviewed - 63%○ Using ensemble of state-of-art techniques○ Best state of the art method individually gives 48

Current Detection Performance

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Summary

Summary - Sold my kidney!

Thanks!