Synthetic Fraud is Here: How Compliance Teams Can Build Resilience Against AI-Generated Deepfake Scams

Synthetic fraud is a prominent present-day threat because last year, deepfake attacks occurred every five minutes, while digital document forgeries surged by 244%. Fraud enabled by generative AI is creating serious compliance problems for financial institutions, particularly since many existing detection systems are falling behind.

Today’s deepfakes go far beyond fake videos. They include realistic audio clones, forged government IDs, and synthetic identities that bypass outdated systems. These attacks damage reputations, drain resources, and shake public trust. A recent report revealed that 53% of finance professionals were targeted by deepfake fraud schemes in the past year, and 43% of those targets fell victim.

As AI becomes easier to access, malicious actors are deploying it faster than most institutions can adapt. This creates constant pressure for compliance teams. They must detect these advanced fraud attempts while preserving operational transparency, auditability, and efficiency.

In this blog, we explore what synthetic fraud looks like in 2025 and what compliance leaders can do to build resilience. The compliance challenges are significant, but the right strategies and technologies can help overcome them.

The Scope of Synthetic Fraud and Deepfake Scams in 2024–2025

Synthetic fraud is escalating at an unprecedented pace. These attacks are not isolated. They reflect a growing pattern of fraud that impacts businesses globally and increases compliance challenges for financial institutions relying on outdated controls.

Surge in AI-Powered Fraud Cases Across Industries

Between May 2024 and April 2025, AI-enabled fraud schemes rose by 456% across sectors such as banking, insurance, and fintech. Fraudsters now create hyper-realistic synthetic identities, clone voices, and build fake companies that look completely authentic. These tactics are designed to bypass traditional rule-based systems and human-led reviews.

Synthetic identities are frequently used to open bank accounts or apply for credit, often remaining undetected for long periods. These accounts are then exploited in larger fraud schemes, which makes early detection essential.

Financial Impact on Enterprises and Institutions

In the last year, the average financial loss per deepfake-related fraud case reached $450,000, while financial institutions reported losses as high as $600,000 per incident.

These disrupt internal operations and damage brand reputation. Together, these issues amplify the compliance challenges institutions must face when balancing fraud prevention, risk management, and customer trust.

Executive and Contact Center Targeting

Deepfakes are now used to impersonate senior executives. These impersonations are often associated with urgent financial requests that circumvent standard approval procedures.

Contact centers face similar threats. AI-generated voice clones can deceive voice-verification systems that were once considered reliable. This makes it easier for fraudsters to access customer accounts or manipulate internal processes.

Regional Trends of the U.S., India, and EU Insights

In the United States, incidents of business email compromise and crypto-related deepfake fraud have grown steadily. In the European Union, regulators are responding with stricter governance focused on synthetic identity fraud, and new AI regulations are expected by early 2026.

These regional trends show that synthetic fraud is a global issue. Institutions must prepare for varying threat types and regulatory environments, all while managing internal compliance challenges.

What’s Fueling Synthetic Fraud and AI‑Generated Deepfake Scams

Synthetic fraud is the result of specific conditions that make financial systems more vulnerable. Understanding these conditions is essential for institutions that are looking to improve controls and reduce their compliance challenges.

One of the biggest drivers is the rapid availability of generative AI tools. What was once complicated and expensive is now simple and free to use. With tools that can mimic voices, forge identities, and generate realistic videos, fraudsters don’t need advanced technical backgrounds. 

Pre-trained AI models and templates are widely accessible, allowing anyone to create synthetic identities or clone executive voices in a matter of minutes.

Another contributing factor is the rise of “fraud-as-a-service.” Criminal networks are commercializing synthetic fraud techniques, offering deepfake production, fake ID kits, and voice cloning tools through the dark web. These services lower the barrier for entry, helping organized groups and individuals alike launch convincing attacks at scale.

Cryptocurrencies have added another layer of difficulty. As crypto remains less regulated in many regions, it offers an anonymous and fast way to move illicit funds. Deepfake-enabled scams involving fake investment schemes and AI-generated crypto influencers have already caused billions in losses, creating new compliance challenges for institutions tasked with tracking high-risk transactions.

Finally, many institutions are still dependent on manual reviews or static rules. These older systems have difficulty upgrading to understand the changing fraud techniques. Without real-time risk detection and behavior-based analysis, even well-structured compliance programs can miss key warning signs. This gap between how fraud is executed and how it is monitored continues to widen.

Together, these factors create an environment where synthetic fraud evolves. For compliance teams, adapting to this reality is not just about new tools but also about reevaluating processes, data use, and overall fraud strategy.

How Compliance Teams Can Build Resilience Against Synthetic Fraud

Institutions face increasing pressure to detect and respond to synthetic fraud before it causes operational or financial damage. To effectively address these risks and meet rising compliance challenges, teams must adopt proactive and adaptive methods rather than rely on legacy models. The following areas are central to building genuine resilience against deepfake-driven threats.

Modern Detection Tools and Real-Time Alerts

Conventional detection methods built on fixed rules frequently fail to capture signs of fraud. Modern tools now use machine learning and real-time behavior analysis to catch anomalies that suggest synthetic manipulation. 

Generative AI models are also being used to detect signs of other AI-generated content. These systems can scan documents, voice files, and videos for inconsistencies or manipulated metadata.

Institutions are also deploying biometric validation, device intelligence, and identity verification technologies to verify users more accurately. For example, AI tools can now detect unusual typing patterns, synthetic speech markers, and inconsistencies in photo IDs that human analysts may overlook.

AI Explainability and Automation in Fincrime Ops

Many financial institutions are adopting AI to automate parts of their fraud investigations, but few focus on explainability. Without clear reasoning behind automated decisions, compliance teams face audit risks and regulatory pushback.

Explainable AI platforms provide transparency by documenting how risk decisions are made, which sources were referenced, and what patterns triggered alerts. Incorporating automation into case investigations also frees up time and resources. 

Tasks like compiling customer transaction histories or summarizing fraud patterns can now be handled by AI, making operations faster, more scalable, and more accurate. This helps reduce repetitive workloads and boosts the consistency needed to meet compliance challenges.

Workforce Training and Governance Enforcement

Even with advanced systems, human error remains a risk. Many deepfake scams succeed not because of weak tech, but because employees fail to recognize synthetic content. This is especially common in contact centers or operational teams handling urgent requests.

Institutions should invest in continuous training programs that update staff on fraud trends, especially deepfake threats. Training should include voice impersonation exercises, phishing detection, and simulated scenarios that mimic AI-driven fraud attempts.

Clear escalation procedures, internal reporting mechanisms, and role-based access also reduce exposure. When governance policies are enforced through technology, such as mandatory dual verification for fund releases, it becomes harder for fraud to succeed.

Data Security, Policy Audits, and Ethical AI Usage

Building resilience also requires strengthening internal safeguards. Institutions must ensure that sensitive data, such as audio files of executives or internal documentation, is stored securely and not used in ways that could be exploited to train malicious AI.

Policy audits should be regular and detailed. They must evaluate how data is shared internally and externally, how authentication is performed, and whether controls are updated for emerging threats. Ethical AI use should also be codified, ensuring that internal AI systems are not introducing their own risks or compliance violations.

Institutions reduce both their risk exposure and their regulatory liabilities by setting clear boundaries on data access and investing in explainable, auditable technologies. This is an important step in reducing long-term compliance challenges while maintaining operational trust.

How Lucinity Helps Compliance Teams Respond to Synthetic Fraud and Deepfake Scams

When synthetic identities, forged documents, and deepfake audio or video are used to bypass traditional systems, Lucinity’s platform enables fast, explainable, and auditable investigation workflows. Each tool directly supports resilience against deepfake scams and the mounting compliance challenges they bring.

Luci Agent: The Luci agent serves as an AI co-investigator, helping teams quickly process and analyze suspicious cases involving potential synthetic fraud. In scenarios where time and clarity are important, Luci can transform large volumes of transaction data and internal documentation into concise summaries.

Luci also visualizes money flows in a way that helps detect unusual or artificially constructed behavior, which is one of the most common signals of synthetic financial activity. With explainability built into every interaction, Luci supports trust and transparency throughout the case investigation process.

Case Manager: Synthetic fraud often cuts across multiple departments and data sources, which can overwhelm traditional systems. Lucinity’s Case Manager unifies all relevant signals, including fraud alerts, KYC inconsistencies, or suspicious communications, into a single, organized view. 

This platform allows compliance teams to investigate synthetic identity fraud, deepfake impersonation attempts, or AI-generated scam behavior without juggling multiple tools or missing context.

Luci Plug-in: In high-pressure environments, compliance analysts cannot afford to switch between multiple platforms when dealing with potential fraud. The Luci plug-in brings AI support directly into the systems teams already use, whether it is a CRM interface, spreadsheet, or case notes tool. 

For example, a fraud analyst working inside a spreadsheet can trigger Luci to instantly summarize transactional trends or flag patterns that resemble synthetic fraud. Similarly, if a suspicious case appears during CRM use, Luci can assist in verifying customer data or generating an on-the-fly report without disrupting the analyst’s workflow. 

Customer 360: Synthetic identities tend to behave differently from real customers. The challenge for compliance teams lies in detecting those subtle differences early enough. Lucinity’s Customer 360 compiles information from transaction platforms, third-party records, and activity patterns to build a continually updated profile of each customer.

If a user suddenly shifts transaction locations, changes counterparties, or shows signs of coordination with other flagged profiles, Customer 360 surfaces those signals for review. It provides a comparative analysis that helps compliance teams spot outlier behavior, which often accompanies synthetic scams.

Wrapping Up

As synthetic fraud grows more advanced, compliance teams are under increasing pressure to protect their organizations using methods that can match the threats they face. Scams created with automation, fabricated identities, and impersonations using manipulated media have become part of daily operations. 

Responding effectively means moving beyond minor fixes. It takes organized responses, stronger tools, and a new approach to how financial crime is examined and handled.

Here are four essential takeaways for compliance leaders:

1. Synthetic fraud has become frequent and impactful, with deepfake scams now contributing to significant financial losses and operational risks.
2. Compliance challenges continue to intensify, driven by the modernity of generative AI tools and the speed with which fraud tactics evolve.
3. Real-time, explainable AI technologies are now essential, providing compliance teams with actionable insights, transparent investigations, and consistent oversight.
4. Resilience depends on coordinated efforts, where governance frameworks, staff training, and smart automation work together.

To learn more about synthetic fraud and support long-term compliance, visit Lucinity today!

FAQs

Q1. What are the top compliance challenges linked to synthetic fraud?Detecting deepfake scams, verifying synthetic identities, and ensuring steady oversight have become significant compliance hurdles today.

Q2. How can compliance teams detect AI-generated fraud?Using explainable AI tools, real-time alerts, and behavior-based analytics helps teams respond faster to synthetic threats and reduce compliance challenges.

Q3. Why are deepfakes difficult for compliance teams to manage?Deepfakes mimic real people with accuracy, making traditional detection ineffective and creating urgent compliance challenges.

Q4. How does Lucinity address compliance challenges from synthetic fraud?Lucinity combines AI-powered investigation tools, automation, and real-time validation to reduce risk and simplify compliance challenges.