Agentic AI, Synthetic Consumers, and Trust Dilution : About Fraud

An Era of Quiet Complexity

The days when fraud revealed itself through noisy spikes and easily recognized breaches are receding. Today’s fraud landscape is defined by interactions that look “normal,” that blend into the fabric of commerce and engagement, and that do not announce themselves through outliers alone. Synthetic consumers are an archetype of this subtlety: identities composed of real, fabricated, or recycled data that persist across time and context. They behave in ways that fundamentally challenge the assumptions underpinning conventional fraud systems.

Consider the scale. Synthetic identities now account for a substantial share of fraud cases, with one industry analysis noting that up to 80% of new account fraud originates from synthetic profiles, and that these personas are involved in roughly 21% of first-party fraud incidents. Traditional onboarding and static checks struggle to distinguish these constructed identities from real ones, leaving vast swaths of risk unexamined until it surfaces elsewhere. 

This is not the background noise of yesterday’s fraud. It is a structural complexity, quietly shifting the baseline upon which detection models and risk scores are built. When the majority of new accounts could be synthetic, but possibly never transact, then the problem is no longer simply catching fraud, but understanding whether the engagement we observe truly reflects real human intent.

Artificial Agents in the Marketplace

Agentic AI is simultaneously a boon and a disruption to digital ecosystems. On the one hand, AI assistants help consumers complete tasks, discover products, and navigate services more efficiently than ever before. These agents accelerate growth and broaden accessibility. On the other hand, the very capabilities that make AI useful—adaptivity, contextual awareness, proficiency in task completion—are indistinguishable from the behavior of automated attacks at the surface level.

Fraud specialists today face a paradox of automated behaviors that were once clear markers of malicious bots now resembling legitimate interaction. Agents making purchases, filling carts, or submitting forms can be doing so either to facilitate commerce or to probe weaknesses. According to a forward-looking threat forecast, 75% of fraud professionals surveyed expect fraud to become increasingly AI-driven, reflective of a broad shift in tactics and velocity. 

Beyond this, recent industry projections warn that the acceleration of machine-to-machine interactions may soon trigger conversations about liability and accountability, illustrating that the challenge is not merely detection, but interpretation of intent and ownership. 

Trust Distortion Through Data

Synthetic identities and adaptive automation distort the very signals fraud detection systems rely on. Machine learning models, for all their sophistication, are fundamentally pattern recognizers. They infer what is typical based on historical examples. When data is infused with synthetic behavior that mimics real activity, the models adapt. They begin to treat that activity as legitimate. Over time, this shift leads to model drift: a slow recalibration of “normal” that includes the very behaviors we hope to catch.

Real world evidence unfortunately supports this. In 2024 and 2025, synthetic and AI-assisted fraud contributed to a measurable increase in identity fraud rates, even as surface metrics like overall fraud didn’t reflect this. In one identity fraud report encompassing hundreds of millions of transactions, the global fraud rate rose to 2.1% of transactions, with synthetic profiles and AI-generated deepfakes representing a significant portion of those incidents. 

The implications extend well beyond fraud teams. Marketing attribution, customer lifetime value models, and demand forecasting all rely on the integrity of data. When foundational signals lose clarity, every downstream decision inherits that uncertainty. The real risk is not simply that fraud occurs. It is that we begin to make strategic decisions based on data that is partly synthetic, partly automated, and increasingly ambiguous in its meaning.

When Detection Is Not Enough

Conventional fraud defenses focus on bad actors that behave differently from good actors. But modern fraud blurs that distinction. Automated agents and synthetic consumers can behave in ways that are statistically indistinguishable from genuine users, especially when velocity and interaction patterns alone are used to judge risk. Recent analysis of AI-optimized attacks found that malicious agents are systematically exploiting promotions and identity loops in ways that evade legacy indicators and confound detection systems built on old assumptions. 

What is required instead is a shift in perspective: from detecting observable anomalies to interpreting intent within context. That means systems and teams must integrate richer signals that persist across time and channels, and that ground interactions in stable identity references rather than ephemeral point-in-time checks. Only by anchoring insights in continuity can subtle distinctions become visible.

Continuity as a Lens

The organizations that navigate this environment effectively treat identity not as a momentary credential, but as a longitudinal signal. Context rooted in historical patterns, cross-channel linkage, and observed behavior enables teams to see beyond the superficial, revealing when a sequence of actions reflects enduring intent or simply transient noise. This approach reframes risk assessment from reactive blocking to proactive interpretation.

Importantly, this perspective aligns fraud mitigation with broader business goals. Marketing, customer experience, and product analytics all benefit from clearer understanding of who is genuinely contributing value versus who is merely inflating metrics. When a segment appears to engage but its underlying identities carry synthetic or automated traits, interpretation of that data changes. Decision-makers can respond with nuance rather than blunt force.

A Question of Intent

In practice, distinguishing intent requires rigor and precision. It requires systems that can weave historical signals together, understand adaptive behaviors, and integrate insights across disparate sources. It also requires cultural adaptation within organizations, where fraud teams are not isolated sentries but interpreters of risk and trust.

Collaborative efforts have shown the benefits of shared signal networks and contextual exchanges. Financial institutions that participated in data consortia significantly boosted detection capability, sometimes by more than an order of magnitude, illustrating the value of shared insight over isolated observation. 

This shift toward interpretation over detection reflects a broader understanding: fraud is not merely about isolating bad events. It is about understanding the arc of behavior and the meaning embedded within it. Synthetic activity and agentic AI make this task more complex, but also more revealing. The challenge ahead is to sharpen our view, not just widen our nets.

Clarity in a Blurred Landscape

Fraud teams have long been defined by their ability to identify and respond to risk. That expertise is still necessary, but it is no longer sufficient. As synthetic consumers and AI agents become pervasive, the central question transforms. It is not who is acting outside the rules. It is what each action signifies.

Understanding intent in a world where human behavior, automation, and synthetic constructs coexist demands that we rethink both measurement and interpretation. It requires anchoring to persistent identity signals, integrating cross-temporal context, and elevating insight over alert fatigue. Those who succeed will be the teams that make sense of complexity, rather than merely cataloging it.

The future of fraud management is about more than catching the bad actors. It is about understanding every intent and preserving trust in the signals upon which all informed decision-making depends.

Where to Deepen Insight

This evolution in fraud management challenges every organization to rethink what meaningful signals look like. AtData is identifying the implications of synthetic identity, agentic automation, and trust distortion across sectors. For professionals seeking to deepen their understanding and stay ahead of emerging threats with evidence-based strategies, AtData can provide valuable perspective grounded in real-world behavioral data.

Explore AtData.com/Fraud-Prevention and continue the conversation about how to better interpret intent in an increasingly complex identity landscape.