Fraud detection, whiplash for cash scheme and Neo4j
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Fraud detection and whiplash for cash schemes SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
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A brief overview of how to use graph technologies to identify insurance scams. Read to learn how to use Neo4j and graph analytics to find criminals.
Transcript of Fraud detection, whiplash for cash scheme and Neo4j
Fraud detection and whiplash for cash schemes
SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
WHAT IS A GRAPH?
Father Of
Father Of
Siblings
This is a graph
WHAT IS A GRAPH : NODES AND RELATIONSHIPS
Father Of
Father Of
Siblings
A graph is a set of nodes linked by relationships
This is a node
This is a relationship
People, objects, movies, restaurants, music
Antennas, servers, phones, people
Supplier, roads, warehouses, products
Graphs can be used to model many domains
DIFFERENT DOMAINS WHERE GRAPHS ARE IMPORTANT
Supply chains Social networks Communications
Stage fake accidents and receive real money from insurance companies
WHAT IS A WHIPLASH FOR CASH SCHEME
Stage a fake car accident
Fill insurance claims
Cash in the check
Based on the accident, they fill insurance forms to ask
their insurance companies to cover for injuries and the car
damages.
The insurance company looks at the claim and writes a check to its customers. The
fraudsters cash it.
A few fraudsters get together. They define an accident scenario and enact it.
But why is it hard to detect whiplash for cash fraud rings?
WHY FRAUD DETECTION IS HARD
The criminal keep their claims small, corroborate each other and pretend to have hard to disprove
injuries
PROBLEM 1 : CRIMINALS FLY BELOW THE RADAR
From one accident to the next, the vehicles, the persons and their roles will change : hard to see
a pattern emerge
PROBLEM 2 : HARD TO SEE THE PATTERN IN A LARGE NUMBER OF ACCIDENTS
How can graphs help?
GRAPH AND FRAUD DETECTION
A single accident doesn’t look suspicious
A GRAPH DATA MODEL FOR A SINGLE ACCIDENT
IS_LAWYERIS_DOCTOR
Udo(Person)
Monroe(Person)
Robrectch(Person)
Skyler(Person)
Euanthe(Person)
Jasmine(Person)
Chelle(Person)
Sousanna(Person)
Focus(Car)
Corolla(Car)
Accident 1(Accident)
IS_INVOLVEDIS_INVOLVE
D
PASSENGER DRIVERDRIVER PASSENGER PASSENGER PASSENGER
But representing the claim data as a graph makes it easy to spot a fraud ring
WHAT DOES A FRAUD RING LOOK LIKE
3 separate accidents (above) involve a small set of 8 persons (below) who seem to have strong relationships : suspicious?
HOW TO INVESTIGATE A WHIPLASH FOR CASH FRAUD RING : STARTING POINT
The investigation starts with a car accident...
As a fraud analyst, we’ll use a Neo4j graph database to investigate the claims data and see if we can spot something suspicious
1. Are the persons involved in the accident involved in other accidents?
2. If they are, who are they involved with? Are these people connected to other accidents?
HOW TO INVESTIGATE A WHIPLASH FOR CASH FRAUD RING : QUESTIONS
MATCH (accident)<-[]-(cars)<-[]-people-[]->(othercars)-[]->(otheraccidents:Accident)
WHERE accident.location = 'New Jersey'
RETURN DISTINCT otheraccidents.location as location, otheraccidents.date as date
QUESTION 1 : ARE THE PERSONS INVOLVED IN THE ACCIDENT INVOLVED IN OTHER ACCIDENTS
A simple Cypher query for Neo4j
location date
Florida 23/05/2014
Florida 27/05/2014
QUESTION 1 : ARE THE PERSONS INVOLVED IN THE ACCIDENT INVOLVED IN OTHER ACCIDENTS
Our suspects are involved in 2 more accidents
With a simple “*” we are expanding our search across the graph
QUESTION 2 : WHO ARE THEY INVOLVED WITH
MATCH (accident)<-[*]-(potentialfraudtser:Person)
WHERE accident.location = 'New Jersey'
RETURN DISTINCT potentialfraudtser.first_name as first_name, potentialfraudtser.last_name as last_name
first_name last_name
Udo Halstein
Robrecht Miloslav
Monroe Maksymilian
Skyler Gavril
Euanthe Rossana
Jasmine Rhea
Sousanna Pinar
Chelle Jessie
QUESTION 2 : WHO ARE THEY INVOLVED WITH
We have a group of 8 people involved in 3 accidents
What if we want to detect automatically these suspicious behaviour?
QUESTION 3 : IS IT POSSIBLE TO DETECT THE FRAUD
Looking in real time for highly connected “accidentees”
QUESTION 3 : IS IT POSSIBLE TO DETECT THE FRAUD
MATCH (person1:Person)-[*..2]->(accident1:Accident)<-[*..2]-(person2:Person)-[*..2]->(accident2:Accident)<-[*..2]-(person3:Person)-[*..2]->(accident3:Accident)
RETURN DISTINCT person1, person2, person3
QUESTION 3 : IS IT POSSIBLE TO DETECT THE FRAUD
It is possible to look for suspicious patterns at large scale
An event triggers security checks
New customer
New car registered
New accident
A Neo4j Cypher query runs to detect patterns
Identification of the fraudsters
The fraud teams acts faster and more fraud cases can be
avoided.
WHAT IS THE IMPACT OF LINKURIOUS
If something suspicious comes up, the analysts can use Linkurious to quickly assess the
situation
Linkurious allows the fraud teams to go deep in the data and build cases against fraud
rings.
Treat false positives
Investigate serious cases
Save money
Linkurious allows you to control the alerts and make sure your customers are not
treated like criminals.
TECHNOLOGY
Cloud ready and open-source based
OTHER USE CASES
Graphs are everywhere, learn to leverage them
Presentation on fraud and whiplash for cash by Philip Rathle and Gorka Sadowski (the inspiration for this presentation) : https://vimeo.com/91743128
Article on whiplash for cash :
- the article : http://linkurio.us/whiplash-for-cash-using-graphs-for-fraud-detection/
- the dataset : https://www.dropbox.com/s/6ipfn4paaggughv/Whiplash%20for%20cash.zip
GraphGist on whiplash for cash :
- the article : http://gist.neo4j.org/?6bae1e799484267e3c60
Whitepaper on fraud detection by Philip Rathle and Gorka Sadowski :
- the whitepaper : http://www.neotechnology.com/fraud-detection/
SOME ADDITIONAL RESOURCES TO CONSIDER
