At A Glance
Behavioral analytics and progressive onboarding can help businesses uncover dormant fraud throughout the customer journey while applying risk-based controls that reduce onboarding friction and strengthen protection against sophisticated fraud.Dormant fraud, sleeper fraud, trojan horse fraud . . . whatever you call it, it’s an especially manipulative tactic that fraud teams often can’t detect until it’s too late. Fraudsters create accounts with stolen credentials or commit account takeover fraud (ATO) to gain access to existing accounts, operate normally under a fake identity, then lie low, waiting for an opportunity to attack.
For fraudsters, executing an attack from within a target’s ecosystem requires a strategic approach, — one that assumes fraud teams won’t have the tools in place to stop dormant accounts before they cause damage.
What is a dormant account?
Most financial institutions define a dormant account simply as one that hasn’t transacted since it was opened. A dormant bank account, in other words, is judged only by its inactivity, and that’s exactly the blind spot fraudsters exploit. Because dormant account activity (or the lack of it) looks harmless on its face, many fraud teams don’t flag these accounts as risky until they’re mobilized for an attack.
Dormant fraud uncovered: A case study
Experian has seen the dangers of dormant fraud play out in real time.
A payment processor used NeuroID, Experian’s behavioral analytics solution, to backtest their user base for potential signs of fraud. Upon analyzing their customer base’s onboarding behavioral data, Experian uncovered more than 100K accounts likely to be dormant fraud. The payment processor hadn’t considered these accounts suspicious and didn’t see any risk in letting them remain active, even though none of them had completed a transaction since onboarding.
Why did Experian flag these as risky when other fraud tools didn’t?
Low familiarity: Our testing revealed behavioral red flags, such as copying and pasting into fields or constant tab switching. These are strong indicators that the applicant is submitting personally identifiable information (PII) that isn’t their own.
Fraud clusters: Many of these accounts used the same web browser, device, and IP address at sign-up, suggesting that a single fraudster was creating multiple accounts. We found hundreds of clusters like these, many with 50 or more accounts sharing the same device and IP address within our customers’ user bases.
It was clear that this payment processor’s fraud stack had gaps, leaving it vulnerable. These dormant accounts could have caused significant damage once mobilized: receiving or transferring stolen funds, misrepresenting their financial position, or building toward a bust-out.
Dormant fraud thrives in the shadows beyond onboarding. These fraudsters keep accounts “dormant” until they’re long past onboarding detection measures. And once they’re in, they can often easily transition to a higher-risk account, after all, they’ve already confirmed they’re trustworthy. This type of attack can involve fraudulent accounts remaining inactive for months, allowing them to bypass standard fraud detection methods that focus on immediate indicators. That’s why effective identity risk management can’t stop at onboarding, it must keep watching accounts for signs of dormant activity long after the welcome email goes out.
Dormant fraud gets even more dangerous when a hijacked account has built trust just by existing. For example, some banks offer a higher credit line only to current customers, regardless of their past activity. The more accounts an identity has in good standing, the greater the chance that they’ll be mistaken for a good customer and given even more opportunities to commit higher-level fraud.
This is why we often talk to our customers about progressive onboarding as a way to overcome both dormant fraud risks and onboarding friction caused by asking for too much information too soon.
Progressive onboarding, dormant fraud, and the friction balance
Progressive onboarding shifts away from the one-size-fits-all model by gathering only the truly essential information initially and asking for more as customers engage. This is a direct counterbalance to the approach that sometimes turns customers off by asking for too much too soon and adding too much friction at initial onboarding. It also helps ensure ongoing checks that fight dormant fraud. We’ve seen this approach (already growing in popularity in payment processing) be especially useful across all types of financial businesses. Here’s how it works:
A prospect visits your site to explore options. They may just want to understand fees and get a feel for your offerings. At this stage, you might ask for minimal information — just a name and email — without requiring a full fraud check or credit score. It’s a low-commitment ask that keeps things simple for casual prospects who are just browsing, while also keeping your costs low so you don’t spend a full fraud check on an uncommitted visitor.
As the prospect becomes a true customer and begins making small transactions, such as a $50 transfer, you request additional details, such as their date of birth, physical address, or phone number. This minor step-up in information allows for a basic behavioral analytics fraud check while maintaining a low barrier of time and PII-requested for a low-risk activity.
With each new level of engagement and transaction value, the information requested increases accordingly. If the customer wants to transfer larger amounts, such as $5,000, they’ll understand the need to provide more details — this aligns with the idea of a privacy trade-off, where the customer’s willingness to share information increases as their trust and need for services grow. Meanwhile, your business allocates resources to those who are fully engaged, rather than to one-time visitors or casual sign-ups, and keeps an eye on dormant fraudsters who might have expected no barrier to additional transactions.
Progressive onboarding is not just an effective approach for dormant fraud and onboarding friction, but also in fighting fraudsters who sneak in through unseen gaps. In another case, we worked with a consumer finance platform to help identify gaps in their fraud stack. In one attack, fraudsters probed until they found the product with the lowest barrier to entry; once inside, they immediately launched a full-force bot attack on higher-value returns. The attack wasn’t based on dormancy, but on complacency. The fraudsters assumed this consumer finance platform wouldn’t realize that low-control onboarding for one solution could lead to easy access to much more. And they were right.
After closing that vulnerability, we helped this customer work to create progressive onboarding that includes behavior-based fraud controls for every single user, including those already with accounts, who had built that assumed trust, and for low-risk entry points. This weeded out any dormant fraudsters already onboarded who were trying to take advantage of that trust, as they had to go through behavioral analytics and other new controls based on the product’s risk level.
Behavioral analytics gives you confidence that every customer is trustworthy, from the moment they enter the front door to even after they’ve kicked off their shoes and are staying a while.
Behavioral analytics shines a light on shadowy corners
Behavioral analytics are proven beyond just onboarding, within any part of a user interaction, our signals detect low familiarity, high-risk behavior and likely fraud clusters. In our experience, building a progressive onboarding approach with just these two signal points alone would provide significant results, and would help stop sophisticated fraudsters from perpetrating dormant fraud, including large-scale bust-outs. If you’re new to the concept, our guide to behavioral analytics walks through how these signals work across the full customer lifecycle.
Want to find out how progressive onboarding might work for you? Contact us for a free demo and a deep dive into how behavioral analytics can help throughout your user journey.
