Last week the news broke that Wells Fargo had “been hit with $185 million in civil penalties for secretly opening millions of unauthorized deposit and credit card accounts that harmed customers,” and the backlash caused by the account opening has been fierce. While the ethical issues with the Wells Fargo scandal are of course much larger than just fake account opening (for starters, it was reported that they were opened by employees “transferring money from customers’ authorized accounts without permission,” and the customers were incurring those transfer fees), a lot of questions have been raised about how something like this could have happened in the first place. How does a company not notice huge numbers of fake accounts? Image Credit: Andrew Czap The sad reality is, it’s a lot easier to create and hide fake accounts than you might think. Whether it’s insiders creating new accounts to meet sales goals or outside fraudsters committing identity theft, fake account opening is a huge problem, and it’s becoming increasingly difficult to detect. As we’ve seen time and time again, massive dumps of personal information are being released from hacks daily, which means fraudsters can open accounts with seemingly real credentials. In some industries you often only need an email to create an account, and as we’ve shown before, you can register millions of fake accounts quickly and easily. The question remains, however: how do you detect them? It’s not that easy, as it turns out. Fraudsters are using advanced attack methods to go undeterred and undetected. Some common account opening attack techniques include: Registrations through VPNs and cloud hosting services to make the traffic appear distributed from different locations. Using mobile devices with OS/hardware flashing capabilities to make the account sign-ups appear to come from different legitimate users signing up from different computer devices. Faking browser info, user-agent strings, and MAC addresses to make sign-ups look like they come from hundreds of different unique users For a real-world example from our research, we recently took a look at new account openings at a financial institution, and we were able to detect huge numbers of fake accounts by looking for signature patterns in the data. There were three main patterns we observed from the attacks: Account openings using emails with similar patterns We identified hundreds of account openings that took place using emails with similar patterns (randomly generated usernames containing 10 characters): firstname.lastname@example.org email@example.com firstname.lastname@example.org email@example.com Account openings from the same devices We also detected fake accounts that were all registered with the same few devices (i.e., mobile hardware ID), with each device registering a small number of accounts. This is likely the result of the attackers “flashing” their devices to create the appearance of multiple distinct users from different devices. In addition to fake account creation, we have also observed this attack technique used to conduct fraudulent in-app purchases. Account openings using scripts We also detected accounts registered programmatically, as shown by the user-agent strings. For example, “Java/1.7.0_51” and “Apache-HttpClient/4.3 (java 1.5)” are default strings from the software library. Fake accounts offer lucrative opportunities for malicious actors. Everything from social reputation, ad impressions, promotional/reward points, and in-game virtual goods can put money in a fraudsters pocket, or act as a puzzle piece in a bigger scam with an even bigger payoff. Wells Fargo has a long road ahead in terms of cleaning out fake accounts, and dealing with their trust and ethical violations. Sadly, this likely won’t be the last time massive amounts of fake accounts hit the headlines. When it comes to detecting fake accounts, all companies have a ways to go. Fortunately, there are options. Advanced, AI-powered fraud solutions such as those offered by DataVisor offer organizations the ability to detect and prevent fraudulent account activity at the application and creation level. This degree of proactivity means attacks are stopped before the damage can happen. View posts by tags: Financial Fraud Related Content: React, or Prevent? Why Organizations Must Embrace A Proactive Approach To Fraud Management 3 Tips for Adopting a Fraud Prevention Approach for the Long Term Defeating Mass Registration with Unsupervised Machine Learning Stay up-to-date on the latest fraud insights and intelligence. Thank you for subscribing.