how to identify reshipping scams with neo4j
DESCRIPTION
This tutorial explains how to use a graph database and graph visualization to fight reshipping scams, a common ecommerce fraud.TRANSCRIPT
SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
How to identify reshipping scams with Neo4j.
Introduction.
CMO
>5 years in consulting
MSc Political sciences and Competitive
Intelligence
JeanVilledieu
SébastienHeymann
CEO
Gephi Founder
Phd in Computer Science and Complex
Systems
Linkurious is a French startup founded in 2013.
Father Of
Father Of
Siblings
What is a graph ?
This is a graph.
Father Of
Father Of
Siblings
This is a node
This is a relationship
What is a graph ? / Nodes & relationshipsWhat is a graph : nodes and relationships.
A graph is a set of nodes linked by relationships.
Some of the domains in which our customers use graphs.
People, objects, movies, restaurants, music…
Suggest new contacts, help discover new music
Antennas, servers, phones, people…
Diminish network outages
Supplier, roads, warehouses, products…
Diminish transportation cost, optimize delivery
Supply chains Social networks Communications
Differents domains where graphs are important.
The accomplice reships the goods to the fraudster. The fraudster sells the goods for $.
He uses the credit cards to buy goods on a website. He has the goods shipped to an accomplice.
Reshipping is used to launder money.
A scammer steals a few credit cards and wants to turn it into $.
Steal credit cards
Order goods online
Sell thegoods
What i s reshipping.
The accomplice is sometimes a victim too..
The accomplice may think he is doing a normal job.
Reshipping protects the criminals.
Why use reshipping.
It is worth taking time and efforts to setup a reshipping network if it helps launder money without getting caught.
Online fraud cost $3.5 billion per year to e-retailers.
Source : http://www.internetretailer.com/2013/03/28/online-fraud-costs-e-retailers-35-billion-2012
$3.5 billion
The cost of fraud for ecommerce 1/2.
Merchants pay $3.10 in costs (replacements and fees) for each dollar of fraud losses they incur.
The cost of fraud for ecommerce 2/2.
Source : http://www.trulioo.com/blog/2014/05/13/social-login-as-an-added-measure-for-e-commerce-fraud/
How to make sense of complex data.
Is it possible for e-retailers to identify reshipping scams?
What is a graph ? / Nodes & relationshipsA graph data model for the ecommerce orders.
date : 11/08/2014items : Laptop, gifcardamount : $878
IS_U
SED
_FO
R
Main Street(Street)
Address 1(Address)
San Francisco
(City)
USA(Country)
Order 1(Transaction)
214.77.224.225(IP_Address)
Address 2(Address)
Detroit(City)
Folsom Street(Street)
Lagos(City)
Nigeria(Country)
IS_BILLING_ADDRESS
IS_SHIPPING_ADDRESS
IS_LOCATED_IN IS_LOCATED_IN
IS_LOCATED_IN IS_LOCATED_IN
IS_LOCATED_IN IS_LOCATED_IN
IS_LOC
ATED
_IN
IS_L
OC
ATE
D_I
N
I’m particularly interested in orders where the billing
address, the shipping address and the location of the
IP address point in different directions ! Can we find
these?
A fraud expert designs a fraud detection pattern.
Designing a fraud detection pattern.
The pattern is translated in a graph language.
Looking for the pattern.
MATCH (a:Transaction)-[r*2..3]-(b:City)
WITH a, COUNT(DISTINCT b) AS group_size, COLLECT(DISTINCT b) AS cities
WHERE group_size > 2
RETURN a, cities
A graph database handles the data analysis at scale.
We use Neo4j to store and analyse the data.
ETL
Traditional databases.
Graph database.
The graph databases helps store the data from various sources and analyse it in real-time to identify potential fraud cases.
Visualization helps analyse the results.
ETL API
Traditional database.
Graph database.
Graph visualization.
Graph visualization is used to investigate the suspicious cases.
Graph visualization solutions like Linkurious help data analysts investigate graph data faster.
Visualizing suspicious fraud cases.
The countries are in dark green, the cities in blue, the IP address in red, the street in orange, the orders (with their date) in light green and the addresses in asparagus.
It seems a Nigerian scammer is using stolen credit
cards on our website. He has enrolled the help of
someone from Boston. We should :
- stop the transactions they are involved in ;
- ban these users and their IP addresses ;
- warn the credit card holders ;
- contact the authorities ;
The analyst turns the alert into actions to stop the fraud.
Turning data into action.
● This is an illustration of the potential of graphs. In the real world we’d use more advanced techniques (ex : to handle proxies).
● Address analysis has to be combined with other fraud detection mechanisms (ex : credit card verification).
● Only a subset of the fraudulent transactions can be inspected by a human.
● Graph-based fraud detection can be applied in other domains.
Graphs are great for fraud detection.
Beyond that example.
GraphGist : http://gist.neo4j.org/?6873cf244c0611533029
Blog post on reshipping : https://linkurio.us/reshipping-scams-and-network-visualization
Sample dataset : https://www.dropbox.com/s/aca6rhmz7vx2hgp/reshipping%20scam%20dataset.zip
Additional resources.