Fraud and social network analysis

Fraud Detection in R

Bart Baesens

Professor Data Science at KU Leuven

Is fraud a social phenomenom?

Intuition: relationships between people
Are there effects indicating that fraud is a social phenomenon?

Is fraud a social phenomenom?

Fraudsters tend to cluster together:
- are attending the same events/activities
- are involved in the same crimes
- use the same resources
- are sometimes one and the same person (identity theft)

Homophily

Homophily in social networks (from sociology)
People have a strong tendency to associate with other whom they perceive as being similar to themselves in some way.

Homophily in fraud networks
Fraudsters are more likely to be connected to other fraudsters, and legitimate people are more likely to be connected to other legitimate people.

Homophily - social security fraud

Does the network contain statistically significant patterns of homophily?

assortativity_nominal(network, types = V(network)$isFraud, directed = FALSE)

networkhomophily

Identity theft

Before: person calls his/her frequent contacts

Identity theft

Before: person calls his/her frequent contacts

After: person calls new contacts which coincidentally overlap with another persons contacts

Money mules

Money mule = person who transfers money acquired illegally (e.g. stolen)
Beneficiary of fraudulent transaction
Transfers stolen money on behalf of other (scam operator)

V(network)$name

"ID02" "ID11" "ID04" "ID03" "ID08" ... "ID16" "ID10" "ID07"

print(list_money_mules)

"ID01" "ID02" "ID03" "ID04"

V(network)$isMoneyMule <- ifelse(V(network)$name %in% list_money_mules, TRUE, FALSE)

V(network)$color <- ifelse(V(network)$isMoneyMule, "darkorange", "lightblue")

vertex_attr(network)

$isMoneyMule
TRUE FALSE  TRUE  TRUE FALSE ... FALSE FALSE FALSE
$color
"darkorange" "lightblue" "darkorange" ... "lightblue" "lightblue"

Network with highlighted money mules

plot(network)

mule_network

Let's practice!

Fraud Detection in R