Article written by Emil Eifrem published by Banking Strategies
As the world continues its transformation to an always-on status, data breaches and, in turn, fraud, are on the rise. In fact, according to CreditCards.com, data breaches totaled 1,540 worldwide in 2014 with 12% of those breaches occurring in the financial services sector. While fraud is not completely preventable, there are approaches banks can take to significantly mitigate the growing issue, including focusing on relationships in banking data to uncover patterns of suspicious fraudster activity.
Looking at data relationships doesn’t necessarily mean gathering new or more data. The key to it is to look at data in a way that helps make explicit underlying connections through graph databases. And a growing number of financial institutions are using them to solve a variety of data problems, in particular to identify advance fraud scenarios, and in real time, too.
PayPal uses graph techniques to perform sophisticated fraud detection on eBay and StubHub transactions for just this purpose. International Data Corporation (IDC) estimates that this has already saved PayPal more than $700 million while enabling the company to perform predictive fraud analysis.
As we know, there are various types of fraud, with first-party bank fraud, insurance fraud and e-commerce fraud being some of the most troublesome.
First-party fraudinvolves criminals who apply for credit cards, loans, overdrafts and unsecured banking credit lines. Aite Group suggests that first-party fraud will be responsible for an estimated $28.9 billion in credit losses by the end of 2016. The surprisingly large size of these losses is due to the difficulty of identifying first-party fraud, where fraudsters behave in the same way as a legitimate customer until the day they cash in their inflated accounts and abscond with the money.
At the same time, there is a relationship between the number of fraud participants to the potential value of their illegal gains. In a fraud ring of just two individuals, sharing only phone number and address, this ring can create four synthetic identities with fake names and, with four to five accounts for each synthetic identity, a total of approximately 18 accounts. Assuming an average of $5,600 in credit exposure per account, the bank’s loss could be over $100,000 as a result.