By Adam Elliott
As fraudsters of the 21st century continue to look for unique ways to separate bank customers from their money, there’s an emerging trend that is costing retail banks millions: an increase in new account fraud (NAF) that is slipping past front-end fraud and risk controls.
What used to be a moderate hassle for retail bankers has become distressing, as the execution of fraud schemes has become more frequent and is having a noticeable impact on the bottom line. Consider the latest numbers from Javelin Research:
- Fraud against demand deposit accounts cost banks $2.2 billion in total losses in 2016, a significant rise over previous years.
- 16.7 million people had their identities stolen in 2017, up from 15.4 million in 2016, representing 6.64 percent of U.S. consumers. This is the highest number since Javelin began tracking in 2003.
- U.S. data breaches tracked in 2017 hit a new all-time high of 1,579, up 44.7 percent over last year’s record totals of 1,091 breaches, representing 178,955,069 individual records.
Perhaps it’s no surprise that research by the Aite Group revealed that new account fraud is one of the top fraud concerns among retail banking executives today, owing to first-hand experience with fraud at their banks and also greater awareness of accelerating fraud risks in the industry.
“New-account risk assessment is an increasingly challenging proposition for financial institutions of all sizes,” said Julie Conroy, research director for Aite Group’s Retail Banking practice. “Application fraud rates in the online channel are eight times that of accounts opened in the branch. With thousands of online DDA account opening attempts happening every day, it’s more important than ever for banks to have controls in place to screen out the criminals while also keeping customer friction to a minimum.”
Drivers of New Account Fraud in DDA
There is a “witch’s brew” causing an acceleration of new account fraud; the ingredients are being provided by the fraudsters, the external environment and the institutions themselves. These forces are interdependent and have resulted in an unfortunate situation.
Fraud activity today is far more organized and automated than in past years, and banks must respond with similarly well-organized and automated fraud controls. With a tremendous amount of data available from breaches, organized fraud rings are using technology to hit banks with stolen identity information. What used to take significant work for the fraudster is now as simple as running a computer script.
Pressure from external and regulatory forces like the Consumer Finance Protection Bureau (CFPB) has produced unintended consequences. Rulings by the CFPB have changed the way many financial institutions use historical risk data to outright deny the consumer’s request to open a checking account. As banks have relaxed risk-screening on the front end, they have unfortunately and unintentionally dropped some of the controls that had stopped fraud in the past.
Competition for business is driving banks to open more accounts as a means of growing their customer bases. To match competitors and meet the expectations of today’s consumers, banks absolutely must have the ability to establish new DDA customer relationships via online applications. This faceless channel exhibits much higher fraud losses; not only is it faceless but it is also vulnerable to rapid-fire account opening attempts from organized fraud rings. It used to take at least some courage to walk into a branch and pretend to be someone else.
How to Stop NAF at Retail Banks
How can banks solve for NAF while continuing to open accounts for legitimate customers? The short answer: a solution that works by identifying suspicious activity and risky behavior patterns and employing predictive models and user-defined business rules to uncover the many variations of identity-related fraud.
Another important aspect of the solution is new account inquiry velocity. A consortium of inquiry data – shared across many different financial institutions – ensures that banks are looped into fraud patterns, such as account opening attempts at several different banks. The effectiveness of this information increases the closer you get to capturing the total universe of events (i.e., the network effect).
Velocity is a critical factor to consider when fighting fraud, but the most effective front-end screening solutions look at multiple identity attributes and scrutinize them for characteristics that indicate fraud. For example, physical address changes can reveal important factors related to fraud, including long moving distances and previous fraud behavior at specific addresses. Other data elements and access points – such as email address, phone number and IP address – should also be analyzed to reveal velocity.
Key considerations for banks when creating strategies to fight NAF include:
- Fraud detection – leverages big data (including consortium new-account inquiry data) and predictive analytics to root out first-party, third-party and synthetic fraud
- Efficiency – weeds out false positive results more quickly and includes a case management system to centralize fraud investigation activities in one place
- Adaptability – flexible enough to track multiple risk factors and respond to current and evolving fraud schemes
- Simplicity – requires little or no technical integration by piggybacking on existing data channels or core processing platforms
Today’s ever-changing fraud landscape requires a high level of vigilance to protect the bottom line because institutions rely heavily on a trusted brand image and customer engagement to garner business. In addition to tightening up processes and risk thresholds, adaptability is equally important for banks seeking a more powerful weapon for fraud-fighting. Retail banks need to be ready not just for today’s schemes and scams, but for a now-unknown set of future fraud risks.
Adam Elliott is founder and president of ID Insight, providing next-generation verification, authentication, market research and fraud solutions to financial services companies, credit issuers, retailers and online merchants. Adam has more than 20 years of experience creating solutions for the financial services and direct marketing industries. A recognized name in data science and analytics, Adam has also held leadership positions at Deluxe, Time Life and Fingerhut. Contact him at email@example.com.