The Three Onboarding Fraud Pain Points That Slow Growth (And How to Solve Them) 

Updated: October 12, 2026 by Charles Hunter 4 min read October 12, 2026

At A Glance

Effective fraud prevention is critical to balancing security and customer experience during onboarding, particularly as fraud tactics continue to evolve. Behavioral analytics can provide organizations with earlier insight into risky behavior to detect fraud, reduce unnecessary friction and manual reviews, and support more efficient onboarding decisions.

31% of U.S. businesses say that operational stress from new account opening fraud has increased in 2026. Losses from new account fraud certainly have a bottom-line impact, but the repercussions extend further. High-volume attacks can overwhelm application flows, incur additional costs from downstream data calls and, most notably, necessitate friction that turns away legitimate customers.

Consumers’ reluctance to endure onboarding friction is only growing: In 2026, nearly four in 10 consumers reported considering abandoning the account-opening process, and 22% moved their business to another organization after a poor onboarding experience. Fraud attacks aren’t slowing down, though, which leaves fraud professionals in a top-of-funnel tug-of-war between onboarding efficiency and fraud-prevention effectiveness.

Looking to stop fraud without slowing good customers, two innovative financial services companies implemented Experian’s behavioral analytics solution to solve their top-of-funnel fraud problems. Here are the challenges they faced (and how behavioral insights helped solve them).

Pain point #1: Friction-filled identity verification

A buy-now-pay-later (BNPL) provider struggled to balance identity verification and customer experience needs. They sent every applicant through the same rigid, demanding onboarding process. The process took nearly 7 minutes to complete and required aspiring customers to submit documents, verify their identity with a selfie or even call the provider’s customer service team to prove their legitimacy. This strenuous process drove good customers away while still allowing sophisticated fraudsters to slip through.

Solution

Leveraging behavioral analytics upstream of identity verification checks helped the provider optimize downstream flows, reducing friction for low-risk applicants. By decisioning off Experian’s behavioral signals early, the provider was able to confidently remove step-ups for clear low-risk users while also rejecting fraudsters faster.

Result

The provider can now apply friction only to customers who truly prompt it. Their improved top-of-funnel decisioning cut verification time in half for 60% of their customers, providing good customers with a better experience while effectively eliminating third-party fraud.

“With (Experian’s behavioral analytics solution), we have enough information on good applicants sooner, so we can fast-track them and say ‘go ahead and get your loan, we don’t need anything else from you.’ And customers really love that.”

Head of Data Science, BNPL Provider

Pain point #2: Unseen fraud

Traditional identity verification and fraud prevention methods are table stakes, yet 67% of U.S. businesses say managing multiple fraud solutions creates significant or extremely significant complexity. Without the right orchestration strategy, even a robust fraud stack can still leave gaps for fraudsters to exploit.

In one digital bank’s case, fraudsters were slipping by defenses and hurting their customer service and internal overhead. Their fraud team was behind the 8-ball, reacting to attacks after they had occurred and giving fast-evolving fraudsters a leg up.

Solution

The bank added Experian’s behavioral analytics signals, bringing new, unique signals into their fraud stack. Behavioral data filled the gaps left by traditional fraud solutions to stop modern fraud tactics, including next-generation bot attacks.

Result

The bank now has a ew layer of visibility into fraudsters, and catches 4x more fraud with 97% accuracy. More importantly, Experian protects the bank against evolving fraud attacks, proactively protecting them against fraudsters’ next new trick.

“You can quantify some things very easily: if bad domains are coming through you can identify and stop it. But if you see things look odd, yet you can’t set up controls, that’s where NeuroID behavioral analytics come in. NeuroID captures the unseen fraud.”

Manager of Fraud Strategy, Digital Bank

Pain point #3: Operational inefficiencies

Relying on outdated fraud prevention tools, both the BNPL provider and digital bank highlighted above allowed fraudsters to work their way downstream, triggering expensive, unnecessary checks that affected their businesses’ bottom lines. And they weren’t alone: Half of U.S. businesses said that for every $1 lost to fraud, they typically spend an additional $3 to $5 on recovery.

Because their existing solutions couldn’t reliably stop fraud without extensive manual reviews, both fraud teams avoided potentially lucrative growth opportunities because of the associated risk.

Solution

By detecting risky behavior in real time, Experian helped both businesses accurately detected fraud earlier, forcing fraudsters out before they triggered expensive checks or landed on a manual reviewer’s desk.

Result

With reduced operational costs and fewer manual reviews, both businesses can pursue new customers and opportunities that were previously deemed too risky. For the digital bank, it lightens the work on their team and allows them to focus their time elsewhere. In the BNPL’s case, that meant meeting their 20% YoY growth goal.

“Before (Experian’s behavioral analytics), we were not automatically denying applications. They were getting an IDV check and going into a manual review. But with (Experian) at the top of our funnel, we implemented automatic denial based on the risky signal, saving us additional API calls and reviews — and we’re capturing roughly four times more fraud. Having behavioral data to reinforce our decision-making is a relief.”

Manager of Fraud Strategy, Digital Bank

How can behavioral analytics optimize your fraud stack?

More than half of U.S. businesses expect new account opening fraud to remain a challenge over the next two to three years.

Behavioral analytics can help businesses meet both demands by identifying risk earlier, improving visibility into suspicious activity and enabling more precise decisions at the top of the funnel. With the right insights, organizations can streamline onboarding for trusted customers, intervene when risk appears and reduce the operational costs of unnecessary checks and manual reviews.

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One Platform, Fewer Handoffs, Faster Decisions

The average credit union manages between 100-300 vendor relationships, and nearly a third have no dedicated staff managing them. What was supposed to drive efficiency is stunting growth and compromising the member experience. Application volume is continuing to rise, and consumers expect a loan decision at digital speed. But for credit unions, meeting that expectation isn't the only goal. They must also continue to make the right lending decisions while also prioritizing risk management. The institutions pulling ahead aren't the ones with the most tools. They're the ones that have turned credit, fraud and decisioning into a single, coordinated system. A single loan decision touches: Credit data Identity verification Fraud signals Decisioning logic Reporting Rarely does all of that live in one place. More often, it’s stitched together across separate platforms, each with its own contract and its own support line. When a credit view and a fraud view are generated separately, they must be reconciled instead of read together in the same instant. Every handoff is a place where a decision slows down and a signal gets missed. It's why a fraud pattern that would be obvious next to the credit file often surfaces only after the funds are already gone. Fragmentation also makes it harder to optimize lending strategies over time. When data, analytics and decisioning are disconnected, credit unions have less visibility into what's working, what's changing and where strategies need to be adjusted. An integrated decisioning ecosystem closes those seams by design, with credit, fraud and lending strategy running on one platform and accessed through a single Application Programming Interface (API). Identity and compliance capabilities can be an integrated decisioning flow. The data doesn't need to be reconciled after the fact because it was never separated in the first place. Stop overengineering, start removing friction When decisioning underperforms, the instinct is to build something more custom: a proprietary model, tighter segmentation, more configuration.  Custom modeling has its place for institutions with the scale and data to justify it. But often, for small or mid-sized credit unions, the bigger opportunity is a flexible decisioning strategy that uses a strong, continuously refreshed model. This shouldn't be either/or. The right approach gives credit unions a strong decisioning strategy today, with the ability to introduce more customized analytics and modeling as their needs evolve.  The same principle applies to strategy management: credit unions can gain greater control through self-service capabilities as they build a more sophisticated data science team.   That kind of flexibility is important as credit unions modernize their decisioning infrastructure. Research surveying more than 2,000 senior credit risk and fraud decision-makers across 11 countries found that organizations investing in unified platforms and modern infrastructure are moving faster and realizing the greatest Return on Investment (ROI).   As their needs evolve, credit unions can adjust decisioning strategies without introducing another layer of technology or relying on third parties to make every change. Until self-service is the right fit, choose a partner that understands what makes your credit union’s membership and risk appetite different from others.  Same member, same afternoon, faster answer Consider a credit union growing its auto and personal loan originations.  A member applies for an auto loan on Saturday afternoon. Credit data, identity and fraud signals are read together in a single real-time pass instead of separate reviews:  Most applications resolve immediately, approved or declined based on a more comprehensive view of the applicant. A smaller number are flagged for quick step-up verification and cleared within minutes. Only genuinely ambiguous cases reach an underwriter, who is working from one complete picture instead of reconciling multiple signals. The member has an answer before the weekend is over. The credit union books the loan before competitors even open on Monday. Built to scale This kind of process holds up today. However, the better test is whether it holds up eighteen months from now when application volume has grown, new products are live and the data flowing through the system looks nothing like it does right now. Future-proofing isn't only about handling more volume. It's about having room to be nimbler and make adjustments as quickly as the market changes. A credit union that relies on standard models within a decisioning strategy may want more customized analytics and greater control over its strategies tomorrow. Connect with us With more than three decades of credit expertise and one of the industry's largest data footprints, Experian helps credit unions do more than solve this year's friction. We're helping to build a decisioning foundation that scales, so growth becomes something the system absorbs, rather than something it must be rebuilt around. Explore More

October 21, 2026 by Zohreen Ismail

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