Deepfake fraud isn’t just coming; it’s already here, and it’s costing billions. Using AI to clone voices and faces, scammers are successfully impersonating CEOs, tricking biometric systems, and siphoning sensitive data.
The impact on India has been particularly severe:
- Economic Toll: Predicted losses of over ₹70,000 crore (Pi-Labs, 2025).
- Widespread Reach: 47% of Indians have encountered AI scams, twice the global norm.
- Universal Risk: From CEO fraud in the boardroom to synthetic identity theft in the banking sector, the attack surface is massive.
Generic security protocols are no longer a defense.
TL;DR: This guide outlines the essential 2026 roadmap for deepfake prevention. We break down the mechanics of the attack, the tech stack required for detection, and the vital steps your business must take to stay secure in the age of synthetic media.
How Deepfake Scams Work
First of all, let us break down the system that fraudsters use. Creating deepfakes is no longer something that requires large amounts of technical skill. Today, fraudsters use platforms and treat the creation of deepfakes as if it is a scalable business. As such, they follow a structured attack plan consisting of the following steps.
1. Target Selection
Fraudsters first perform large amounts of research to find their victims, and then unearth their vulnerabilities. They dig through websites like LinkedIn or corporate directories and social media to understand reporting structures. They also make full use of executives who have public video or audio available, including podcast interviews or keynote speeches. They can also target consumers, building profiles using data breaches or public posts.
2. Content Synthesis
Once they have acquired enough source material, these fraudsters use generative AI models to create digital clones. They can use just a few seconds of clean audio to clone voices well enough to fool people. They can also use face-swapping algorithms to place a target’s face onto an actor’s body. The more advanced fraudsters can perform fine-tuned adjustments, like lighting or matching vocal inflections, to make sure that the synthetic persona is maximally convincing.
3. Delivery Mechanisms
Once the synthetic persona has been created, it needs to be deployed against the targets. This is done via trusted communication channels. Fraudsters use methods like:
- Urgent WhatsApp Voice Notes
- Compromised Email Threads
- Fake Social Media Profiles
They can also use Virtual Camera Software to inject deep-faked video feeds directly into live Zoom calls or bank video KYC sessions.
4. Exploitation
Once they have managed this level of access, they need to rely on social engineering to complete the fraud. These scammers often create a sense of extreme urgency, either via fear or excitement. For example, one may impersonate high-ranking executives and demand immediate wire transfers in secret, or they can use a synthetic identity to acquire a high-limit credit card from a bank. Either way, the end result is financial theft, identity fraud, or severe damage to the reputation.
Real-World Deepfake Scam Case Studies
In 2024, a Hong Kong-based employee of a UK engineering firm authorized a $25 million transfer after a video conference where every participant, including the CFO, was a deepfake. Relying on the visual “authenticity” of his colleagues, the employee inadvertently funneled massive sums into offshore accounts.
This same technology is now being weaponized against India’s digital infrastructure. With Indian institutions performing approximately 11 lakh video KYC calls daily, cybercriminals have found a massive attack surface. By combining stolen PAN card details with AI-generated faces, they easily bypass weak camera checks to create “mule accounts.” These fabricated identities serve as the primary engine for laundering stolen funds across the country.
Beyond institutional theft, scammers are also exploiting public trust. In early 2026, a deepfake video of BSE CEO Sundara Raman Ramamurthy went viral, offering fraudulent stock tips to unsuspecting investors. The incident forced the Bombay Stock Exchange to issue urgent nationwide warnings, highlighting how easily AI can turn a trusted reputation into a tool for mass financial exploitation.
10 Steps to Prevent Deepfake Scams
As deep fake scams get more and more dangerous, upgrading your security infrastructure becomes increasingly important. Here are ten steps you should take to stop AI-driven fraud.
1. Verify Identity Using Multi-Factor Biometric Checks
Passwords and OTPs, while useful, are no longer enough. You need to implement multi-factor authentication that includes biometrics. This way, even if an attacker steals a password, they still cannot access and replicate the authorized user’s physical biometrics.
2. Implement Liveness Detection in All Video-Based Onboarding
Liveness detection is your best weapon against spoofing. When performing a remote onboarding process, you should mandate active or passive liveness checks. As such, your system needs to verify that a real, living human is in front of the camera, making sure to detect workarounds like printed masks, pre-recorded videos, and AI avatars.
3. Use Deepfake Detection Software at KYC Touchpoints
In case your liveness detection setup is spoofed, and attackers use camera injection techniques to push synthetic identities into your system, you need another layer of defences. These defenses come in the form of specialized deepfake detection tools that analyze pixel-level data. These tools can catch micro artifacts, unnatural blending, as well as algorithmic signatures that cannot be caught by the human eye.
4. Train Staff to Recognize Deepfake Red Flags
Given how much scamming relies on social engineering, it is important to ensure that your employees are properly trained. You should conduct regular training exercises simulating deepfake phishing or voice phishing. There are several tells that they should be able to spot, like robotic audio, lighting mismatches, or unusual behavioral requests.
5. Establish Out-of-Band Verification for High-Value Transactions
You should never authorize large financial transfers based on a single communication channel. If, for example, an executive demands an urgent payment via a video call, it is the employee’s responsibility to verify the request through a secondary “out of band” method. What this means is that they should call the executive back on a known secure internal phone number to confirm the instruction, or email them, or use any other method of communication that is hard to spoof.
6. Audit Video Conferencing Systems for Injection Vulnerabilities
Scammers often use virtual cameras like OBS Studio to push deepfake feeds into platforms like Zoom or Microsoft Teams. As such, you should set up a security team to audit all video conferencing setups.
7. Monitor for Synthetic Identity Fraud Signals
Synthetic identities often have specific fingerprints. Scammers usually create synthetic identities by mixing real stolen data, like valid Aadhaar numbers, with AI-generated faces or other data. The best defence against this is to deploy fraud monitoring systems that analyze cross-channel signals. When you spot anomalies like:
- multiple accounts using the same device ID
- rapid and successive onboarding attempts
- mismatched credit histories
You can take it as a sign of a synthetic identity.
8. Implement RBI-Mandated Video KYC Safeguards
If you run a regulated financial entity in India, strict compliance with RBI mandates is the best way to protect your company. Ideally speaking, you should follow the 2025 RBI Master Direction on KYC. You should mandate actions like:
- geo-tagging
- live timestamps
- concurrent auditing
- End-to-end encryption for all Video-based Customer Identification Processes
9. Deploy Real-Time Deepfake Detection APIs at Digital Entry Points
Digital entry points are your biggest vulnerabilities, and as such you should integrate powerful real-time deep fake detection APIs directly into your application’s backend. These APIs are used to analyze incoming audio and video streams within milliseconds. As and when the AI detects a high probability of synthetic identities, the system should automatically flag or halt the session before any damage is done.
10. Report Suspected Deepfake Fraud to CERT-In and Authorities
As and when you encounter a deep fake scam, you need to report it immediately. The Indian Computer Emergency Response Team actively tracks AI threats, and the National Cyber Crime Reporting Portal helps map networks of attackers and issue advisories as needed.
Deepfake Scam Types & Prevention by Category
Different scams require defenses that are adjusted to match. Here, we break down some of the most common threats as well as how to best neutralize them.
Video KYC Deepfake Bypass Attacks
Fraudsters use camera injection to pipe a deepfake into a bank’s app. The AI lipsyncs the answers to a live agent’s questions.
Target: Direct Onboarding.
Prevention: Use passive liveness checks and environment scanners to block virtual cameras.
CEO / CFO Audio Deepfake Fraud
Attackers clone an executive’s voice. They call a junior employee and demand a massive, secret wire transfer.
Target: Corporate Finance.
Prevention: Enforce out-of-band verification and strict multi-person authorization protocols.
Romance & Social Engineering Deepfakes
Scammers generate fake photos to build a persona. They engage victims for months before asking for money.
Target: Consumers.
Prevention: Educate users on reverse-image search and common behavioral warning signs.
Political & Reputation Deepfakes
Bad actors generate fake videos of public figures or business leaders saying controversial things to manipulate stock prices or damage brands.
Target: Consumers
Prevention: Proactively monitor social media using brand-protection software and issue swift, authenticated public denials
Synthetic Identity KYC Fraud
Criminals blend a stolen ID number with an AI-generated face to apply for high-limit credit cards.
Target: Lending Institutions.
Prevention: Cross-reference identity claims with government databases and credit bureaus.
H4: Summary Table
| Scam Type | Attack Vector | Target | Detection Method | Prevention Tool |
| Video KYC Bypass | Camera Injection | Banks/Fintech | Pixel/Artifact Analysis | Liveness Detection |
| CEO Audio Fraud | Voice Cloning | Enterprises | Audio Frequency Checks | Out of band verification |
| Romance Scam | Synthetic Media | Consumers | Reverse Image Search | User education |
| Reputation Deepfake | AI Video Generation | Public Figures | Media Forensics | Brand monitoring |
| Synthetic Identity | Mixed Data | Lenders | Cross-database matching | API Database checks |
Deepfake Prevention Compliance for Banks & Fintechs in India
Here are the four requirements that you should fulfill to keep your defences up.
RBI Video KYC Compliance Requirements
Recently, the Reserve Bank of India updated its KYC framework in order to defend against digital fraud. For maximum protection, your system should capture live coordinates, i.e. geotagging, and date-time stamps, as well as high-accuracy face matchign and liveness detection. Plus, you need to make sure that all your verified V-CIP data is synchronised in real time with the Central KYC registry.
SEBI & IT Act Regulatory Considerations
Given the heavy risk that mule accounts pose, the SEBI and IT Act have mandated strict on-boarding protocols for retail investors in particular. The Information Technology rules require intermediaries to take down unlawful content like deepfakes before a deadline of 36 hours. If you fail to secure your platform in this way, you can suffer penalties under the Digital Personal Data Protection Act.
CERT-In Reporting Requirements
CERT-In has mandated the reporting of severe cyber breaches, like successful deepfake fraud. They also advise the use of MFA and AI detection tools.
Recommended Technology Stack for BFSI
If you want to maximise compliance and stop fraudsters, you need to build the right technology stack. No one tool can catch everything, so it is important to build a layered defence system.
- Liveness Detection blocks spoofed faces at onboarding, using tools from iProov, Facephi, or IDmission
- Deepfake Detection API catches injected synthetic video/audio, using tools from Sensity AI, Reality Defender, or Attestiv
- Document Verification validates physical document authenticity, using tools from Onfido, Jumio, or HyperVerge
- KYC Orchestration manages end-to-end V-CIP workflows, using Digilocker integration and CKYC sync.
- Fraud monitoring detects anomalous behaviour across multiple channels, using tools from Feedzai, ThreatMetrix, or SAS Fraud
- Employee training builds capabilities among your employees.
Enterprise Implementation Checklist
Use this checklist to identify what gaps still exist in your defenses.
Risk Assessment
- Threat modeling and surface mapping
- Current stack audit
- Compliance review
- Friction vs Security tolerances
Vendor Evaluation
- Red Team Deepfake Testing
- Select and implement video liveness, deepfake detection, voice anti-spoofing, and biometrics
- Note Orchestration Capabilities
Technical Integration
- Deploy Device Intelligence
- Integrate multi-modal APIs
- Roll out digital signatures
- Use hardware keys
Human-In-The-Loop
- Establish out-of-band approvals
- Update response plans
Tools & Technology for Deepfake Prevention
Now that we have looked at the overview, let’s take a closer look at how these tools work.
AI-Based Liveness Detection (Passive vs Active)
Active Liveness tools require the users to move their heads around, blink, or even make specific expressions, while passive detection tools simply capture short videos or images.
Deepfake Detection APIs & SDKs
Deepfake detection APIs and SDKs don’t just analyze images or videos on a surface level. They scan for algorithmic signatures, like edge blurring or unnatural colour gradients.
Document Verification with NFC & Hologram Checks
Passports and ID cards come equipped with NFC chips and holograms. While AI generated images can work to fool low-level security setups, it is much harder to fake NFC chips and holograms.
Video Integrity Analysis Tools
These tools observe metadata and network packets of a video stream, and are capable of detecting if any tampering is going on. For example, if the video originates from a software like OBS Studio, it can be marked as a red flag and potentially fraudulent.
Employee Awareness & Training Platforms
These tools work to train employees to detect and defend against a wide variety of attacks, social engineering in particular.
How to Detect a Deepfake in a Video Call
Video calls are extremely susceptible to being deepfaked. As such, you need to know how to identify these kinds of calls.
Key Red Flags to Look For
There are five main red flags to look out for when trying to figure out if a video call is deepfaked. However, as video generation AIs improve, these markers grow subtler and harder to detect.
- Unnatural blinking: Note if the person rarely blinks, or blinks strangely.
- Lip-sync lag: Note if the audio doesn’t perfectly match the mouth movement, and watch for blurriness around the teeth and lips.
- Lighting inconsistencies: Deepfaked videos are bad at handling lighting. Look for shadows that don’t match light sources.
- Facial edge artifacts: AIs find it difficult to maintain continuity when rendering glasses, hair, or jewellery. Note any changes that occur.
- Unusual behavioral prompts: These deepfaked videos are relatively limited, and asking them to make specific movements can break the illusion, like asking them to turn their heads or make complex movements.
Wrapping Up
As deepfakes become easier to make and cheaper to use, fraud numbers are going to climb higher than they have ever been. Dealing with fraud at this scale is not possible without automation and setting up institution-level systems.
At HyperVerge, we fight AI with AI. Our industry-leading deepfake detection tools and RBI-compliant video KYC solutions stop injection attacks, synthetic identities, and presentation spoofing in real time. Take a look at HyperVerge’s Deepfake Detection Solutions today!
