Research On Ai-Enabled Identity Fraud Using Biometric Spoofing Technologies
1. Case 1: Fingerprint Forgery for Bank Fraud – India (2024)
Overview:
Two individuals in West Bengal, India, exploited the Aadhaar Enabled Payment System (AEPS) by forging fingerprints to withdraw money from victims’ bank accounts.
Technical Method:
The criminals obtained the victims’ fingerprints from public records and personal items.
They created polymer-based fake fingerprints that could be scanned by biometric devices.
These forged fingerprints were used to authenticate bank withdrawals without the victims’ consent.
Legal Action:
Prosecuted under Indian Penal Code sections relating to cheating by personation, forgery, and criminal breach of trust.
Evidence included bank transaction logs, the forged fingerprint devices, and witness statements.
Outcome:
Both defendants were sentenced to 5 years imprisonment and fines.
This case highlighted the vulnerability of fingerprint-based authentication systems and set a precedent for prosecuting biometric spoofing in India.
2. Case 2: AI-Generated Deepfake for Bank Authentication – Vietnam (2023)
Overview:
A criminal group used AI-generated face videos (deepfakes) to bypass facial recognition authentication on banking apps.
Technical Method:
The group harvested facial images and short videos of the account owners from social media.
Using deepfake AI technology, they created synthetic facial videos that mimicked the account owners’ movements and expressions.
These videos were submitted to the bank’s facial recognition system, allowing unauthorized transactions.
Legal Action:
Prosecuted under fraud and money-laundering statutes in Vietnam.
Digital forensic experts traced the deepfake videos, reconstructed AI usage, and linked the perpetrators to illicit transactions.
Outcome:
The defendants were convicted, and their illicit gains were confiscated.
This case became an early example of AI-enabled biometric spoofing in financial fraud, demonstrating the emerging threat of synthetic identities in banking.
3. Case 3: Fingerprint Cloning for Rural Banking Fraud – Telangana, India (2022)
Overview:
A gang exploited fingerprint biometric systems used in rural banking services to withdraw funds from farmers’ accounts.
Technical Method:
Gang members obtained access to fingerprint scanners used in local banks.
They cloned fingerprints onto rubber stamps and polymers.
The cloned fingerprints were used to conduct withdrawals and account manipulation.
Legal Action:
Prosecuted under bank fraud, forgery, and identity theft laws.
Forensic evidence included fingerprint duplication devices and transaction logs showing unauthorized withdrawals.
Outcome:
Seven individuals were arrested; all devices used for biometric spoofing were confiscated.
The case highlighted systemic vulnerabilities in rural banking biometric authentication systems.
4. Case 4: Biometric Fraud via Customer Service Center – Delhi, India (2025)
Overview:
A man running a “bank customer service center” collected victims’ fingerprints and bank details under false pretenses.
Technical Method:
Victims were convinced to provide fingerprints for deposits or services.
The fraudster used these fingerprints to authenticate withdrawals from multiple accounts.
The scheme resulted in losses of approximately ₹18–20 lakh.
Legal Action:
Case filed under criminal breach of trust, cheating, and fraud provisions.
Police collected fingerprint devices, victim testimonies, and bank transaction records.
Outcome:
The perpetrator was arrested, prosecuted, and ordered to repay the defrauded amount.
This case demonstrated how collection of legitimate biometric data can be misused when AI or digital authentication systems are involved, even without deepfake technology.
5. Case 5: Welfare Fraud Highlighting Biometric System Vulnerabilities – UK (2024)
Overview:
A gang of individuals carried out large-scale welfare fraud, taking advantage of weak or absent biometric verification in the benefit application process.
Technical Method:
The criminals impersonated legitimate applicants using forged documents and, where minimal biometric checks existed, exploited them to claim benefits.
Although AI was not directly used, the case illustrated that insufficient biometric anti-spoofing systems can enable identity fraud at scale.
Legal Action:
Prosecuted under fraud, money-laundering, and identity theft laws in the UK.
Investigations relied on document verification, interviews, and analysis of claim patterns.
Outcome:
Convictions included prison sentences and mandatory restitution.
The case prompted authorities to explore AI-based biometric verification and anti-spoofing measures in welfare systems.
Analysis of Key Trends from These Cases
Techniques Used:
Fingerprint cloning (Cases 1, 3, 4)
AI-generated facial deepfakes (Case 2)
Exploiting weak biometric verification or process loopholes (Case 5)
Legal Approach:
Traditional fraud and identity theft statutes are adapted for biometric spoofing.
Evidence includes digital logs, forensic reconstruction of biometrics, and physical devices used in spoofing.
Impact:
Courts recognize biometric spoofing as a form of fraud.
These cases establish precedent for prosecuting AI-assisted or digitally enabled identity theft.
Highlight the need for multi-factor authentication, liveness detection, and AI-based spoof detection.
These five cases collectively illustrate how AI-enabled and traditional biometric spoofing are being exploited, how legal systems respond, and the technical and evidentiary challenges faced in prosecution.
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