This site is not optimized for Internet Explorer 9 and lower. Please choose another browser or upgrade your existing browser in order get the best experience of this website.

Synthetic Identities: Intro to Real-Time Detection

Synthetic Identities Factors

Typical Synthetic Identity Fraud Scenario

ONgDB Graph Database to the Rescue

Synthetic Identities: Real-Time Detection with ONgDB on GraphGridSynthetic identities are built through identity theft for use in fraud requiring individually valid identifying details. Identity criminals establish new identities through the combined use of false and actual data, or at times, independently valid information. Criminals utilize this synthetic identity to gain open deposit accounts, credits, driver’s licenses, and even passports.

Normally, identity criminals will often involve the use of an SSN (Social Security Number) and associating it with a name not related to that number. The use of real pieces of identity mixed together in new ways means that each piece of identifying information will pass a validation check on its own, which makes it hard to discover. Fraudsters know that synthetic identity theft is a simple-yet-lucrative act that can easily be carried out.

Bank fraud would typically involve identity criminals applying for loans, credit cards, unsecured bank credit lines, and overdrafts —- without any intention of making payment in return. It’s a major issue for today’s banking institutions.

Such problem can be a result of two main factors. The first factor involves first-party fraud being difficult to detect. Identity thieves appear like legitimate customers, up until the time they wipe all their accounts clean and suddenly disappear.

The second factor involves the nature of relationship between the participants involved in the fraud ring and the overall monetary value managed by the operation. Such connection is one feature frequently exploited by today’s organized criminals.

Even if the factual details for every fraud incident vary from one operation to another, the pattern indicated here shows how fraud rings function in real-time:

  • Two or more people are involved in the fraud ring.
  • The fraud ring shares factual contact details then mixing them together to falsify numerous synthetic identities.
  • Fraud ring members open accounts via synthetic identities.
  • Accounts are normally utilized involving typical payments and purchases.
  • Banks increase their revolving credit lines over a period of time.
  • The fraud ring maxes out all credit lines and suddenly disappear without a trace.
  • Fraudsters bring their balances all the way to zero via fake checks to double the damage.
  • Process of collections continue, but law enforcement fail to catch the fraud ring.
  • Uncollected debt is already written off.

The Open Native Graph Database (ONgDB) has become a valuable solution as a fraud detection tool for solving these problems. Graph query languages such as that of Geequel offer a simplified semantic for detecting fraud rings through graph patterns and exploring these connections in real-time.

The augmentation of one’s current fraud detection infrastructure to support ring detection can be accomplished by running a proper entity link analysis via a graph database and managing checks during important stages within the customer and account lifecycle. This includes:

  • The time when the account is being made.
  • During a review or investigation.
  • As soon as the credit balance threshold is hit.
  • When the check has bounced.

The good news is, real-time graph traversals tied to the right events can help financial institutions and agencies spot fraud rings and synthetic identifies during or before the criminal acts occur. The GraphGrid Connected Data Platform helps governments and global enterprises prevent millions of dollars in fraud every year. Download our freemium package and get started today or schedule a demo to learn more.