FINRA Warns of AI-Powered Deepfake Scams Targeting Investors: What Financial Firms Must Do Now

What would you do if your “CFO” popped onto a video call and urgently approved a large wire? What if the voice on your voicemail sounded exactly like your portfolio manager, asking to move funds before market close? In 2025, those aren’t hypothetical scare stories—they’re the new normal. FINRA just put AI-powered fraud front and center in its latest annual regulatory report, and the message is unmistakable: deepfakes and generative AI are reshaping investor risk, compliance expectations, and the cybersecurity agenda.

In this deep dive, we unpack what FINRA’s warning means in practice, how criminals are operationalizing deepfakes against investors and firms, and what leaders must do—right now—to defend people, portfolios, and reputations without stifling innovation.

Why FINRA Is Sounding the Alarm—And Why It Matters Now

FINRA’s annual oversight priorities carry weight because they inform how examiners will test your controls and where penalties are most likely to land. This year, AI isn’t a side note—it’s a headline risk. As summarized by Comply’s latest roundup, FINRA’s report prioritizes AI and cybersecurity in response to a surge in sophisticated scams, including deepfake audio and video designed to impersonate trusted financial experts and executives.

Criminals are exploiting generative models to create hyper-realistic voices and faces that bypass traditional detection.
Firms are adopting AI cautiously—mostly for summarization and transaction verification—due to supervision, privacy, and recordkeeping obligations.
Preparedness is not optional: FINRA expects risk assessments, staff training on deepfake red flags, resilient architectures like Zero Trust, stronger identity controls (including biometrics), and robust incident reporting.

This warning lands alongside ongoing FBI alerts on AI-enabled phishing, voice cloning, and social engineering that dramatically increase response rates and losses. See the FBI’s Internet Crime Complaint Center for current advisories and reporting guidance: ic3.gov.

Read the Comply summary: News You Should Know: Top Five Compliance Articles (Feb. 14, 2025)
FINRA’s annual oversight materials: Annual Regulatory Oversight Report
FBI crime reporting and PSAs: IC3

Bottom line: AI is a double-edged sword. It can improve supervision and detection, but it’s also supercharging fraud. Regulators expect you to manage both sides of that blade.

How AI Deepfakes Are Targeting Investors and Financial Firms

Sophisticated adversaries don’t need prolonged access to your systems if they can access your people. That’s where deepfakes shine: they excel at creating just enough urgency and trust to manipulate actions.

Common attack patterns we’re seeing

Voice-cloned executives authorizing wires
The attacker scrapes public speeches, earnings calls, or podcasts to clone a C-level voice.
They call finance or operations, referencing current initiatives to sound credible.
They demand “confidential” and urgent payments outside normal processes.
Video imposters on “pop-up” meetings and webinars
A fake “portfolio manager” or “research head” appears in a short-notice video call with investors to discuss a hot opportunity.
Lip-sync and expressions look convincing enough for a quick pitch; Q&A is deflected.
Follow-up emails contain payment instructions to a controlled account.
Hyper-personalized phishing and inbound requests
Emails reference details scraped from LinkedIn, press releases, deal tombstones, and regulatory filings.
Voice notes or voicemails with cloned voices reinforce the request.
Attachments are AI-crafted to mimic internal memos or term sheets.
Synthetic identities for KYC and account takeover
Deepfaked selfies and documents pass weak liveness checks.
Fraudsters open or take over accounts, then launder funds through rapid trades or crypto rails.
Investor-facing impostor sites and social accounts
Lookalike domains and accounts host deepfake CEO videos “announcing” special raises or airdrops.
Paid ads amplify reach; comments are bot-filled social proof.

The common thread: the con targets human trust in familiar voices, faces, and formats—and it often works faster than traditional defenses.

Why Traditional Controls Are Failing Against Deepfakes

Caller ID and email domain checks are easily spoofed.
“Dual control” can be bypassed when both approvers are socially engineered using the same fake authority.
Employees over-index on “I know that voice/face,” especially under time pressure.
Content scanning tools miss bespoke spear-phish that contain minimal malware.
Legacy knowledge-based authentication (e.g., mother’s maiden name) is trivial to defeat with data leaks and OSINT.

The solution set isn’t one silver bullet. It’s a layered, identity-centric strategy that strips implicit trust, validates requests out-of-band, and flags anomalies in behavior—not just content.

What FINRA’s Priorities Signal for Compliance Teams

While you should consult the full report and your legal counsel, several themes are clear across regulators:

Risk assessments: Update enterprise risk assessments (ERAs) and business impact analyses (BIAs) to include deepfake-enabled fraud scenarios, especially for payments, trading, onboarding, and investor communications.
Incident reporting: Ensure you can detect, triage, and report incidents within required timeframes. Map obligations across FINRA rules, SEC requirements, and state breach laws.
Zero Trust architecture: Move away from implicit trust on networks and devices. Enforce least privilege, continuous verification, and segmentation.
Third-party risk: Evaluate vendors for deepfake and AI-related risks (e.g., voice biometrics, identity proofing, AI tooling). Require clear controls, audit rights, and breach notification.
Examinations: Expect more probing on staff training, supervisory systems, communications surveillance, change management for AI tools, and evidence you actually test your controls.

Helpful frameworks and guidance: – NIST Zero Trust architecture: SP 800-207 – CISA Zero Trust Maturity Model: cisa.gov/zero-trust-maturity-model – NIST AI Risk Management Framework: nist.gov/itl/ai-risk-management-framework

A Pragmatic Defense Roadmap: 0–90 Days and Beyond

You don’t need perfection tomorrow. You do need visible momentum. Here’s a staged approach that balances speed and rigor.

Quick wins (next 30 days)

Freeze “informal” approvals
Reaffirm that no voice note, text, or ad-hoc video call can authorize cash movements, changes to instructions, or credentials—ever.
Implement mandatory call-backs
For any payment, trade, or client instruction change, require verification to a phone number on file (not provided in the request). Document this in policies.
One-page playbooks
Issue concise runbooks for finance, trading, investor relations, and client service: red flags, escalation paths, and “stop-the-bleed” steps.
Micro-train high-risk roles
15-minute scenario-based training for assistants, wire desks, traders, wealth advisors, and executives. Include audio/video examples of deepfake artifacts.
Tighten external comms hygiene
Standardize executive video backgrounds and on-screen watermarks; catalog official social accounts and publish them on your website.

Foundation moves (30–90 days)

Phishing-resistant MFA everywhere
Deploy FIDO2/passkeys for critical systems and privileged access. See: FIDO Alliance – Passkeys
Liveness checks for identity workflows
Use biometric verification with robust liveness detection for onboarding and high-risk changes. Tune for false positives; add manual review for exceptions.
Out-of-band secure channels
Stand up a secure, logged communications channel for sensitive approvals (e.g., verified portal or app with push approvals and biometrics).
Payment controls
Enforce positive pay, velocity limits, and beneficiary whitelisting. For “first payment to a new beneficiary,” add a time delay and two independent verifications.
Vendor assurance
Assess identity proofing, biometrics, and call center vendors for deepfake resilience (liveness, challenge-response, synthetic detection). Require SOC 2 and clear SLAs. Learn more: AICPA SOC Reports

Scale and hardening (3–12 months)

Zero Trust pilots
Micro-segment trading, treasury, and advisor systems; enforce continuous device and user risk scoring; block anomalous flows.
Behavior analytics and anomaly detection
Baseline normal user behavior (UBA) and transaction patterns (fraud analytics). Flag unusual timing, geography, counterparties, and approval routes.
Media provenance and authenticity
Explore Content Credentials and C2PA standards to cryptographically sign your official media. Educate clients to look for authenticity signals.
Resources: C2PA and Content Authenticity Initiative
AI-native detection
Consider model-based detectors for synthetic audio/video, but don’t over-rely on them. Use as one signal among many, with human-in-the-loop review.
Incident response and tabletop tests
Simulate a deepfake-initiated wire fraud, a fake-CEO video on social media, and a synthetic KYC breach. Capture lessons and improve playbooks.

Technology Controls That Actually Move the Needle

Identity verification and liveness

Multi-factor authentication using hardware-backed credentials (FIDO2) reduces phishing and session hijacking.
Biometric verification with active liveness challenges (blink/turn/prompt-based) helps against spoofing—but combine with device telemetry and document checks.
For call centers, deploy challenge-response phrases and dynamic knowledge questions that aren’t publicly available.

Communications integrity and provenance

Approvals via verified apps/portals, not email, SMS, or ad-hoc video.
Digitally sign sensitive communications where feasible; avoid sharing credentials or payment details in channels you can’t control.
Publish and maintain a “How we communicate” page to help clients verify instructions, plus your official domains and social accounts.

Detection and monitoring

Train analytics on behaviors (who approves what, when, from where) rather than only content.
Track brand impersonation: monitor for lookalike domains and fake social profiles; issue takedowns quickly.
Use voice biometrics cautiously; enforce replay and synthesis detection and include opt-outs for privacy laws.

Data protection and segmentation

Least privilege access tied to identity posture (user + device).
Segregate treasury, trading, and client data; enforce just-in-time access and step-up verification for sensitive actions.
Encrypt data at rest and in transit; restrict external sharing by default.

Threat intelligence and collaboration

Subscribe to sector ISACs and law enforcement bulletins; integrate threat intel into detection rules.
Share indicators of compromise (IOCs) from incidents where lawful and safe to do so.

Train People to Outsmart AI

Tools help, but people stop fraud. Your training must go beyond “phishing 101.”

Red flags staff should memorize

Time pressure + secrecy: “This must be done now, and don’t tell X.”
Channel switch: “Reply to this new number” or “use this link.”
Unusual requests: first-time vendors, changed wiring details, after-hours approvals.
Inconsistencies: lip-sync off by a beat, odd lighting artifacts, robotic cadence, or a voice that speaks without interruptions.
Context gaps: exec lacks recent project details or mispronounces known names.

Role-based simulations

Wire desk and AP: simulate a voice-cloned CFO asking to bypass controls.
Advisors and IR: run a fake “client” change request with altered bank details.
Executives: drill on responding when their likeness is abused on social platforms.

Executive media hygiene

Limit high-quality, clean audio/video recordings in uncontrolled environments.
Use consistent, distinctive visual elements in official videos; embed Content Credentials where feasible.
Establish a rapid-response plan for fake videos: legal, comms, and platform takedown flows.

Guidance for Investors and Clients: A Quick Protection Checklist

You don’t need to be a security pro to avoid most deepfake scams. Use this simple playbook:

Never move money based on a call, voicemail, or video alone. Verify with a known number or your firm’s secure portal.
Treat any “changed wiring instructions” as suspicious. Confirm via a second channel.
Look for authenticity signals: firm domain, secure portals, and consistent contact info listed on the official website.
Hang up and call back using the number on your statement or card—not the number sent in the message.
Check advisors and firms on FINRA BrokerCheck: brokercheck.finra.org
Report suspicious activity to your firm and the FBI IC3: ic3.gov

Responsible AI Adoption: Innovate Without Inviting Trouble

Regulators aren’t anti-AI; they’re anti-unmanaged risk. You can unlock value while honoring obligations.

Govern use cases
Approve low-risk, high-value pilots (e.g., call summarization, anomaly triage, coding assist) with guardrails.
Model risk management
Inventory models and providers; document intended use, data inputs, risks, and mitigations. Leverage established guidance like the Federal Reserve’s SR 11-7 on model risk principles: SR 11-7
Data handling and privacy
Prohibit inputting PII, MNPI, or client data into public models. Use private, logged instances with DLP and access controls.
Supervision and recordkeeping
Ensure AI-assisted communications, investment analysis, and client interactions are captured, supervised, and compliant with books-and-records rules.
Vendor diligence
Evaluate AI vendors for security, privacy, IP protections, and auditability. Require incident notice and cooperation terms.

Helpful frameworks: – NIST AI RMF: nist.gov/itl/ai-risk-management-framework

Preparing for Examinations: What Examiners Will Ask For

Expect targeted questions and evidence requests:

Governance and risk
Your latest cyber risk assessment with AI/deepfake scenarios.
Board/committee oversight materials and metrics.
Policies and procedures
Written standards for identity verification, approvals, and incident handling.
AI usage policies, data restrictions, and supervision controls.
Training and testing
Completion rates for role-based training; results of phishing/deepfake simulations.
Tabletop exercise reports and remediation actions.
Technology and monitoring
MFA deployment scope; identity proofing methods; liveness detection.
Fraud analytics rules, alert volumes, and tuning history.
Incident management
Incident tickets, root causes, notifications, and restitution outcomes.
Lessons learned and control changes.

Keep documentation concise, current, and consistent. If you’re still implementing, show a realistic roadmap with owners, budgets, and milestones.

Three Realistic Scenarios to Pressure-Test Your Controls

The “CEO on a plane” wire
A voice clone calls AP requesting a confidential $450,000 prepayment to a “strategic partner.” The email follow-up uses a lookalike domain. Would your call-back and beneficiary controls stop it?
The “pop-up investor webinar”
A fake PM hosts a 12-minute video session about a timely private placement. Attendees receive “instructions” via a spoofed IR email. Can your brand monitoring and takedown playbook contain it? Do clients know your official process?
The “synthetic client”
A high-net-worth “client” completes selfie verification and requests updated bank details. Liveness is barely passed. Will your layered verification and manual review flag the anomaly before funds move?

Collaboration Is Your Force Multiplier

No single firm can solve AI-enabled crime alone.

Work with regulators
Share patterns and emerging threats; seek pre-clearance on novel controls or communications methods when appropriate.
Partner with tech providers
Co-develop liveness challenges tailored to your risk; integrate provenance signals into your workflows.
Engage industry groups
Join sector ISACs, standards bodies like C2PA, and security communities to exchange IOCs and best practices.

The Takeaway

Deepfakes and AI-driven scams aren’t edge cases anymore—they’re a material, exam-priority risk that strikes at the heart of investor trust. The firms that will win this moment are moving fast on three fronts:

People: training high-risk roles with realistic scenarios and clear “stop the bleed” playbooks.
Process: codifying out-of-band verification, payment rigor, and incident reporting without exceptions.
Technology: deploying phishing-resistant MFA, liveness and provenance checks, behavior analytics, and Zero Trust.

Do those three well, and you’ll protect clients, satisfy regulators, and still harness AI where it truly helps. Wait, and you may find your brand speaking words you never said—while losses mount.

Resources to get started: – FINRA Annual Regulatory Oversight: finra.org/rules-guidance/guidance/annual-regulatory-oversight-report – Comply’s summary of the 2025 priorities: comply.com – FBI IC3 reporting: ic3.gov – NIST Zero Trust (SP 800-207): csrc.nist.gov – NIST AI RMF: nist.gov – C2PA and Content Credentials: c2pa.org, contentauthenticity.org – FINRA BrokerCheck: brokercheck.finra.org

FAQ

Q: What exactly is a “deepfake” and why is it so dangerous in finance? A: A deepfake is synthetic audio, video, or imagery generated by AI to convincingly mimic a real person. In finance, deepfakes are dangerous because they exploit trust and urgency to bypass standard controls—especially for payments, trading, and sensitive client instructions.

Q: Can I reliably spot a deepfake with the naked eye or ear? A: Sometimes you’ll notice glitches (odd blinking, mismatched lip-sync, unnatural cadence), but high-quality deepfakes can be nearly flawless. That’s why process controls—like out-of-band verification and secure approval channels—are more reliable than “gut feel.”

Q: Are AI deepfake detectors enough to protect my firm? A: No. Detectors can help as one signal, but they’re not foolproof and can be evaded. Build layered defenses that validate identity, verify intent, and analyze behavior, with human-in-the-loop reviews for high-risk actions.

Q: What does “Zero Trust” actually mean for a broker-dealer or adviser? A: Zero Trust assumes no implicit trust based on network location. Practically, it means continuous verification of users and devices, least-privilege access, segmentation of critical systems like trading and treasury, and strong identity-centered controls. See NIST SP 800-207: Zero Trust Architecture

Q: Should we roll out voice biometrics to stop voice clones? A: Voice biometrics can add friction for attackers, but they must include anti-spoofing and synthesis detection and be combined with other factors. Consider privacy and consent obligations, offer alternatives, and test for false accepts/rejects.

Q: Can we use generative AI safely in operations or compliance? A: Yes—if governed. Favor low-risk use cases (summarization, triaging alerts) on private, logged instances with DLP. Block PII and MNPI from public models, supervise outputs, and document models in your risk inventory. Use the NIST AI RMF for structure: AI RMF

Q: What should an investor do if they think they followed a fake instruction? A: Contact your firm immediately using a known phone number, notify your bank to attempt a recall, change affected credentials, and file a report with the FBI IC3: ic3.gov. Document everything (emails, numbers, timestamps).

Q: Are there regulatory reporting requirements after a deepfake-driven incident? A: Potentially. Depending on the incident’s nature (fraud, client data exposure, operational impact), you may have obligations to notify clients, regulators (e.g., FINRA, SEC), and possibly law enforcement within defined timelines. Work with counsel and confirm requirements in your jurisdiction and registration category.

Q: How can clients verify they’re dealing with a legitimate advisor or firm? A: Use FINRA’s BrokerCheck to verify registrations and disclosures: brokercheck.finra.org. Cross-check contact details with the firm’s official website, and only use secure, authenticated portals for instructions and approvals.

Q: Does content provenance (C2PA/Content Credentials) solve deepfakes? A: It helps by allowing publishers to cryptographically sign media so recipients can verify authenticity, but adoption isn’t universal and attackers can still create unsigned fakes. Treat provenance as one part of a broader validation strategy.

The race is on: criminals are using AI to sound and look like us. With disciplined processes, focused training, and identity-first controls, you can make sure only the real you can move real money.

Discover more at InnoVirtuoso.com

I would love some feedback on my writing so if you have any, please don’t hesitate to leave a comment around here or in any platforms that is convenient for you.

For more on tech and other topics, explore InnoVirtuoso.com anytime. Subscribe to my newsletter and join our growing community—we’ll create something magical together. I promise, it’ll never be boring!

Stay updated with the latest news—subscribe to our newsletter today!

Thank you all—wishing you an amazing day ahead!

FINRA Warns of AI-Powered Deepfake Scams Targeting Investors: What Financial Firms Must Do Now

Why FINRA Is Sounding the Alarm—And Why It Matters Now