Fraud spikes demand attention, but focusing too heavily on one visible surge can leave other areas exposed. As defenses strengthen in one part of the journey, attackers often shift tactics, concentrating risk where oversight is lighter. Benchmarking helps teams see beyond immediate spikes so hidden exposure doesn’t quietly grow elsewhere.
The Q1 2026 Digital Trust Index and the latest benchmarks from our Fraud Industry Benchmarking Resource (FIBR) reveal a pattern many fraud teams are already seeing firsthand: the number of fraud attacks are reducing, but the cost of the fraud that does get through is rising.
Across the Sift Global Data Network:
- The payment fraud attack rate decreased from 3.3% in Q1 2025 to 2.8% in Q1 2026—a 14% decline
- The manual review rate decreased from 3% to 2.5%—a 17% reduction in review volume
- Meanwhile, the overall chargeback rate increased from 0.2% to 0.3%—a 56% increase
- The fraudulent chargeback rate increased from 0.098% to 0.102%—a 4% increase
These shifts suggest fraud is becoming more concentrated. Fewer attacks are getting through, but when they do, they are more likely to result in measurable loss.
Fraud is Becoming More Targeted
At a high level, payment fraud pressure appears relatively stable. Attack rates declined year over year, and fewer transactions are being routed to manual review. But stability in top-line metrics can mask important underlying changes.
A 14% decrease in attack rate alongside a 56% increase in chargebacks suggests fraudsters are becoming more precise and more sophisticated. Rather than relying on volume alone, attackers are targeting accounts, payment methods, and behaviors that increase the likelihood of meaningful financial gain, so when fraud does get through, it is more likely to result in a significant loss.
Rather than broad card testing activity, many fraud attempts now focus on trusted accounts, stored credentials, and accumulated balances. When fraud succeeds in these environments, it is more likely to result in disputes and financial recovery costs. Increasingly, attackers are willing to invest time in building credibility before monetizing access, allowing accounts to age, establish normal behavior patterns, and generate positive transaction history before executing a high-value fraudulent action weeks or even months later. This “good user gone bad” pattern extends the timeline for fraud detection and makes performance harder to evaluate in the short term, reinforcing the need to measure risk longitudinally rather than relying solely on immediate transaction outcomes.
Account Takeover Continues to Drive Downstream Fraud
The Q1 2026 Digital Trust Index reinforces that accounts remain a primary entry point for modern fraud. 21% of consumers report experiencing account takeover in the past year, often alongside payment fraud.
FIBR benchmarks show the ATO attempt rate declined from 1.3% in Q1 2025 to 0.95% in Q1 2026, representing a 28% decrease. This decline suggests improvements in authentication and detection strategies. However, ATO remains a persistent risk because of its downstream impact.
Once an attacker gains access to a legitimate account, they can:
- Lock customers out of their accounts, causing reputational harm
- Add stolen financials to monetize
- Monetize the account by selling it to another fraudster
Even relatively small shifts in ATO success rates can create ripple effects far beyond individual transactions. When accounts are compromised, the impact extends to brand perception, eroding trust, increasing customer churn, and reducing lifetime value. Customers rarely distinguish between attacker behavior and platform responsibility; if accounts are not protected, the brand is often seen as untrustworthy or unsafe. Over time, this reputational risk can affect not only individual companies, but confidence in entire digital ecosystems, reinforcing that fraud prevention is as much a trust and growth strategy as it is a security function.
Industry Insight: Fraud Pressure Varies by Business Model
FIBR benchmarks show that fraud exposure is not evenly distributed across industries. Different business models create different incentives for attackers, influencing both attack frequency and loss patterns.
For example:
- Food & Delivery saw a 3.7% payment fraud attack rate paired with extremely low manual review volume (0.012%), reflecting highly automated decisioning environments where speed is critical.
- Internet & Software showed elevated fraudulent chargeback exposure (0.104%), illustrating how subscription models and stored credentials can create attractive targets for account compromise.
- Digital Commerce maintained a relatively lower 1.7% attack rate, but still experienced measurable downstream dispute pressure (0.19% chargeback rate), reinforcing the importance of balancing approval rates with risk tolerance.
- Online Gambling demonstrated relatively low attack volume (0.4%) paired with comparatively higher fraudulent chargeback exposure (0.16%), highlighting how high-value transactions can concentrate dispute risk even when overall attack rates appear manageable.
- Finance & Fintech maintained relatively controlled exposure, with a 2.7% attack rate and 0.14% chargeback rate, reflecting tighter identity verification where accounts connect directly to funds.
These variations reinforce why benchmarking against peers is critical. A fraud rate that appears high in isolation may be typical for a specific business model, while a seemingly low rate could signal under-detection.
Efficiency Gains Can Mask Emerging Risk
Manual review rates declined 17% year over year, reflecting stronger automation, more precise decision thresholds, and scalable fraud operations that support sustainable growth.
Reducing manual review improves operational efficiency and customer experience. Faster decisions create less friction for legitimate users and allow analysts to focus on more complex investigations. However, benchmarking data shows that efficiency gains do not always translate directly into lower loss rates.
When review volume decreases while chargebacks rise, it can indicate that decision thresholds are optimized for speed but not fully calibrated to evolving attack patterns. Benchmarks help teams prioritize where human review delivers the most impact. Emerging and complex attack patterns warrant analyst attention, while repetitive, easily identifiable fraud should be automated, allowing teams to focus expertise where it meaningfully improves outcomes.
Certain Payment Types Continue to Attract Disproportionate Fraud
Attackers consistently prioritize payment methods and account features that enable rapid monetization. Stored value, loyalty balances, and alternative payment methods often present attractive opportunities because protections are still maturing relative to traditional card networks.
As digital commerce expands into subscriptions, wallets, and hybrid payment models, fraud risk increasingly reflects coordinated activity across accounts, devices, and payment types rather than isolated transaction anomalies. Understanding how performance compares to industry benchmarks helps teams determine whether exposure is increasing faster than controls are evolving.
Fraud’s Impact Extends Beyond Loss Rates
Fraud directly influences customer confidence and long-term retention.
According to the Digital Trust Index:
- 73% of consumers say they have abandoned a purchase due to concerns about payment security
- 52% say they would stop using a platform after experiencing fraud
- 26% of consumers report experiencing online payment fraud in the past year
These behaviors reinforce that fraud prevention and customer experience are interconnected. Too much friction can reduce conversion. Too little protection can erode trust. Benchmarks provide the context needed to balance these priorities.
Turning Benchmarks Into Decisions
The Q1 2026 Digital Trust Index provides a macro view of how fraud patterns are evolving across the digital economy. FIBR complements that perspective with operational benchmarks that allow teams to evaluate performance relative to peers.
Together, they help answer key questions:
- Are payment fraud controls aligned to current attack patterns?
- Is manual review volume delivering measurable risk reduction?
- Is account protection strong enough to prevent losses?
- Is your fraud strategy optimized for the lowest chargeback rate, or the highest revenue protection?
Fraud rarely increases uniformly across the funnel. It concentrates where incentives are highest and defenses lag behind attacker innovation. Benchmarking helps teams identify those gaps earlier and adjust controls before losses accumulate.
The goal is not simply to match a benchmark. It is to understand where to increase scrutiny, where to automate with confidence, and where reducing friction can improve both conversion and customer trust.
