Combating synthetic fraud in financial services with generative AI and human expertise

Synthetic fraud is getting smarter. Detection must too.

Synthetic identity fraud has become one of the most persistent and costly threats facing financial services organizations. 

Unlike traditional identity theft, synthetic fraud blends real and fabricated data to create entirely new identities that can pass initial checks, build credibility over time and trigger losses before detection systems catch up. 

As digital onboarding expands and transaction volumes grow, the scale and speed of fraud schemes continue to increase.

Criminal networks are now using automation and AI tools to generate more convincing identities, documents and interaction patterns. That shift is putting pressure on legacy banking fraud detection solutions that have a heavy reliance on static rules and known fraud signatures.

It’s a big reason banks and financial institutions are exploring more adaptive approaches to fraud, including generative AI (GenAI) models that can surface subtle anomalies and emerging risk patterns faster. These technologies are advancing how fraud risk is identified and prioritized — and they’re most effective when paired with expert human investigation and response. 

In a fraud landscape that’s constantly shifting and adapting, the most resilient defenses against synthetic fraud combine AI-driven detection with expert human oversight and response.

What is synthetic fraud — and why is it harder to catch?

In financial services, synthetic fraud most often refers to synthetic identity fraud — which differs from traditional identity theft in a critical way: instead of taking over a real person’s or business’s identity, fraudsters construct a new one. 

They combine valid data elements — such as a legitimate Social Security number or address — with fabricated names, dates of birth or contact details. The result is a “synthetic” identity that can appear legitimate across multiple verification checkpoints.

Synthetic identities are often introduced through credit applications or digital onboarding flows, where automated checks validate individual data points but may not detect that an overall identity is manufactured. Fraudsters nurture these accounts, build transaction history and increase credit limits before cashing out. And because the manufactured identity doesn’t map cleanly to a real victim, the fraud can go undetected longer and recovery is more complex.

Preventing synthetic identity fraud is more challenging because its signals are fragmented. One system may see valid credentials, another, normal behavior. Without cross-signal analysis and adaptive modeling, the risk pattern stays hidden. 

Most traditional banking fraud detection solutions weren’t designed for identities that are partly real and partly fabricated. Preventing synthetic identity fraud requires more dynamic, intelligence-driven approaches coupled with human-in-the-loop expertise.

Why traditional fraud prevention tools are no longer enough 

For years, many banking fraud detection solutions have relied on rules-based systems built around known fraud patterns. While still valuable, these controls weren’t designed for the synthetic identity schemes or AI-assisted fraud tactics being used today. Static thresholds, predefined scenarios and checklist-style verification can only catch what earlier fraud technologies were originally trained to recognize.

Synthetic identities are intentionally constructed to pass standard validation steps. Each individual data element may appear legitimate, even when the overall identity is not. Rules-based systems often evaluate signals in isolation, making blended identities especially difficult to flag.

Rules-based fraud systems evaluate known patterns. Synthetic identity fraud is designed to look legitimate at each individual checkpoint — making it easier to evade detection.

Legacy fraud detection environments tend to struggle in several key areas:

• Pattern dependency – reliance on known fraud signatures and predefined scenarios
• Signal isolation – evaluating individual attributes instead of cross-signal behavior
• Speed gaps – segmented or delayed workflows while fraud moves in near real time
• False positive pressure – tighter rules increase friction, manual reviews and customer experience risk

This growing mismatch is one reason banks and financial services firms are investing more heavily in AI fraud prevention approaches that can learn and adapt instead of relying solely on fixed rules.

How generative AI enhances fraud detection and prevention

Artificial intelligence is known for its ability to handle complex analytical and creative tasks at lightning speed. In fraud detection, AI’s value increasingly lies in generative AI techniques such as simulation, modeling and pattern discovery — going beyond classification of known behaviors to expose weaknesses and emerging risk signals.

GenAI is increasingly being applied in fraud prevention programs for:

• Scenario simulation — generating synthetic fraud patterns to test controls
• Adversarial modeling — stress-testing defenses against likely attack strategies
• Pattern discovery — surfacing hidden cross-signal relationships
• Control gap analysis — identifying where existing detection logic may fail

This helps shift fraud operations from reactive detection toward more proactive risk anticipation.

Generative AI doesn’t just detect fraud patterns — it can simulate them, stress-test defenses and expose gaps before attackers do.

Synthetic identity fraud rarely produces a single obvious red flag. Instead, it creates small inconsistencies across identity attributes such as demographic and contact data, device behavior, usage timing and transaction flows. 

Generative models are especially useful for surfacing subtle relationships across these fragmented signals — even within large, messy datasets. Another advantage is safer model training. High-quality fraud data is limited and sensitive, but generative techniques can create realistic synthetic datasets that expand coverage, strengthen fraud detection and support privacy and compliance requirements.

Many generative AI fraud detection capabilities are still emerging and being deployed in controlled or hybrid environments. And while they’re advancing real-time fraud detection, they work best as part of a layered fraud strategy that includes human oversight, verification and guidance versus a standalone control.

Modernizing identity verification and behavioral analysis

Identity verification has moved beyond document checks and static proofing. Current systems continuously analyze behavioral, biometric and contextual signals across the customer lifecycle. 

Behavioral analytics establishes baselines for legitimate user interactions, including: 

• Typing rhythm and navigation patterns
• Device posture and session behavior
• Timing and transaction habits

When activity deviates, risk scores adjust dynamically, exposing synthetic identities that might pass initial onboarding checks. AI-driven monitoring strengthens digital onboarding by cross-checking identity attributes, device intelligence and behavioral signals simultaneously, enabling real-time rather than months-later detection.

Biometric and liveness technologies add another layer of verification, though they aren’t foolproof as deepfakes improve. That’s why multi-signal analysis — not any single method — is critical to detecting synthetic identity fraud.

AI-identified fraud signals need expert interpretation and follow-up to turn detection into effective fraud mitigation.

Why human expertise matters in AI-powered fraud mitigation

Even the most advanced AI detection doesn’t stop fraud on its own. Effective outcomes depend on how alerts are investigated, interpreted and resolved — where experienced fraud analysts add critical value.

GenAI models excel at surfacing anomalies and prioritizing risk. Human experts evaluate context and intent, assess patterns, reconcile conflicts, apply judgment in ambiguous cases and facilitate resolution. 

Human-led review also strengthens customer handling and regulatory compliance. Decisions involving account restrictions, escalations or outreach benefit from expert oversight and documented rationale. Feedback from investigators — whether validating or overturning AI-generated alerts — improves future model performance. 

 Over time, this human-in-the-loop cycle boosts detection accuracy, accelerates resolution and optimizes efficiency — to maximize AI's effectiveness in fraud prevention. 

Building a future-ready fraud defense strategy

Fraud tactics will continue to evolve — and AI will remain part of both the threat and defense landscape. Banks and financial organizations that treat AI as a capability multiplier versus a standalone solution will be better positioned to keep pace.

Generative AI fraud detection and broader AI-driven fraud prevention tools are advancing how risk is surfaced, scored and prioritized. They improve speed, pattern recognition and scalability. But durable fraud prevention still depends on expert interpretation, investigation discipline and effective response execution.

A future-ready strategy brings three key elements together: 

• Adaptive AI-driven detection
• Strong governance
• Experienced human oversight, analysis and response

This layered approach helps reduce loss exposure, control false positives and protect customer trust even as fraud tactics shift.

Organizations that align advanced technology with expert-led fraud operations gain more than better alerts — they achieve faster resolution and stronger outcomes. 

Conduent supports financial institutions with expert-driven fraud detection and rapid response services that work alongside existing technology investments, helping turn risk signals into resolved cases and stronger defenses.

Learn more on our website about Conduent’s range of Bank and Lending Solutions.

 
Frequently asked questions (FAQs)

What is synthetic identity fraud in financial services?

Synthetic identity fraud happens when criminals create new identities by mixing real and fake information, such as valid Social Security numbers with fabricated names. These identities can pass checks, build credibility and cause losses before detection.

How does generative AI help detect financial fraud?

Generative AI simulates fraud patterns, uncovers subtle anomalies and tests defenses against likely attacks. It reveals hidden relationships in data, helping institutions detect and respond to emerging fraud faster.

What are the limitations of traditional fraud detection methods?

Rules-based systems rely on known patterns and isolated data points. They often miss synthetic identities or AI-assisted fraud because they lack adaptive modeling and cross-signal analysis.

Why is human expertise important in AI-driven fraud prevention?

AI flags suspicious signals, but humans evaluate context and apply judgment and investigative insight — validating alerts, assessing intent and resolving cases to ensure effective, compliant fraud mitigation.

How can financial institutions combine AI and human oversight for stronger defenses?

The most effective approach routes AI alerts into structured human review workflows. Trained fraud analysts investigate, validate and resolve cases, improving accuracy, reducing false positives and accelerating resolution.