Mike Betron as written a four-part series on first party fraud that merits your attention:
First Part Fraud [Part 1]
What is First Party Fraud?
First-party fraud (FPF) is defined as when somebody enters into a relationship with a bank using either their own identity or a fictitious identity with the intent to defraud. First-party fraud is different from third-party fraud (also known as “identity fraud”) because in third-party fraud, the perpetrator uses another person’s identifying information (such as a social security number, address, phone number, etc.). FPF is often referred to as a “victimless” crime, because no consumers or individuals are directly affected. The real victim in FPF is the bank, which has to eat all of the financial losses.
First-Party Fraud: How Do We Assess and Stop the Damage? [Part 2]
Mike covers the cost of first party fraud and then why it is so hard to combat.
Why is it so hard to detect FPF?
Given the amount of financial pain incurred by bust-out fraud, you might wonder why banks haven’t developed a solution and process for detecting and stopping it.
There are three primary reasons why first-party fraud is so hard to identify and block:
1) The fraudsters look like normal customers
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2) The crime festers in multiple departments
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3) The speed of execution is very fast
Fighting First Party Fraud With Social Link Analysis (3 of 4)
And you know, those pesky criminals won’t use their universally assigned identifiers for financial transactions. (Any security system that relies on good faith isn’t a security system, it’s an opportunity.)
A Trail of Clues Left by Criminals
Although organized fraudsters are sophisticated, they often leave behind evidence that can be used to uncover networks of organized crime. Fraudsters know that due to Know Your Customer (KYC) and Customer Due Diligence (CDD) regulations, their identification will be verified when they open an account with a financial institution. To pass these checks, the criminals will either modify their own identity slightly or else create a synthetic identity, which consists of combining real identity information (e.g., a social security number) with fake identity information (names, addresses, phone numbers, etc.).
Fortunately for banks, false identity information can be expensive and inconvenient to acquire and maintain. For example, apartments must be rented out to maintain a valid address. Additionally, there are only so many cell phones a person can carry at one time and only so many aliases that can be remembered. Because of this, fraudsters recycle bits and pieces of these valuable assets.
The reuse of identity information has inspired Infoglide to begin to create new technology on top of its IRE platform called Social Link Analysis (SLA). SLA works by examining the “linkages” between the recycled identities, therefore identifying potential fraud networks. Once the networks are detected, Infoglide SLA applies advanced analytics to determine the risk level for both the network and for every individual associated with that network.
First Party Fraud (post 4 of 4) – A Use Case
As discussed in our previous blog in this series, Social Link Analysis works by identifying linkages between individuals to create a social network. Social Link Analysis can then analyze the network to identify organized crime, such as bust-out fraud and internal collusion.
During the Social Link Analysis process, every individual is connected to a single network. An analysis at a large tier 1 bank will turn up millions of networks, but the majority of individuals only belong to very small networks (such as a husband and wife, and possibly a child). However, the social linking process will certainly turn up a small percentage of larger networks of interconnected individuals. It is in these larger networks where participants of bust-out fraud are hiding.
Due to the massive number of networks within a system, the analysis is performed mathematically (e.g. without user interface) and scores and alerts are generated. However, any network can be “visualized” using the software to create a graphic display of information and connections. In this example, we’ll look at a visualization of a small network that the social link analysis tool has alerted as a possible fraud ring.
A word of caution.
To leap from the example individuals being related to each other to:
As a result, Social Link Analysis has detected four members of a network, each with various amounts of charged-off fraud.
Is quite a leap.
Having charged off loans, with re-use of telephone numbers and a mobile population, doesn’t necessarily mean anyone is guilty of “charged-off fraud.”
Could be, but you should tread carefully and with legal advice before jumping to conclusions of fraud.
For good customer relations, if not avoiding bad PR and legal liability.
PS: Topic maps can help with this type of data. Including mapping in the bank locations or even personnel who accepted particular loans.