AI-Generated Identity Fraud: Bringing Real-Time Identity Intelligence for Fraud Prevention
Author: admin | 07 Mar 2024In This Post
According to the Identity Theft Resource Center, Synthetic Identity Fraud and Impersonation attacks will witness a rise in 2024 statistics as compared to last year, showing a staggering rise in these malpractices too. At the beginning of the 4th quarter of 2023, US News reported that victims of identity fraud took from weeks to a few months to regain control of their identity.
With these alarming statistics, where do the anti-fraud actors stand today? What is the best strategy that Identity Verification solutions can utilize to reduce identity theft? Is facial biometrics enough for identity fraud detection? To answer these questions, let us look into a clearer picture explaining Identity Fraud and the preventive measures.
Key Takeaways
- Identity Fraud is an illicit activity that has developed different shapes over the past years.
- There is a difference between facial verification and facial recognition
- Facial biometrics can help detect and prevent identity fraud through a two-way approach.
- Facial comparison systems need to implement real-time identity intelligence for fraud prevention
What is Identity Fraud?
Identity Fraud, or Identity Theft is an illicit practice in which a person uses the personally identifiable information of someone else for illicit gains such as monetary benefits or gaining access to a prohibited area. The information stolen in identity theft can be:
- Name, Address, or Date of Birth of anyone
- Social Security Number (SSN), password, or passport number
Identity theft is also done by stealing Biometric data for biometric verification such as stolen fingerprints, retina or Iris scan data, or using AI deepfakes for spoofing attacks to carry out cyberattacks, hack databases, steal virtual assets, and even carry out terrorist attacks.
Types of Identity Fraud
Identity theft can manifest different forms in the following three aspects of identity verification:
Document Identity Fraud
In document identity fraud, the documents used for KYC (Know Your Customer) of a customer are manipulated for impersonation. These documents mostly include Identity Cards, Driver’s License and Passports. The most common type of identity document fraud is National ID Card which accounts for 46.8% of all document fraud.
Biometrics Identity Fraud
This type of identity theft is an advanced threat vector in which the unique biometric identifiers of a user are stolen through sophisticated tactics. Fingerprint forgery was discussed in 1937 by William Harper in his journal. But the claim seemed too early. Today after 87 years, digitally equipped cybercriminals are confident and successful in breaking the barriers and using stolen biometrics to create new identities or use the existing credentials to commit further crimes.
Similar to fingerprint theft, facial biometric patterns, voice cloning, and retina eye patterns can now easily be forged with the latest technological advancements. This has made identity fraud detection far more difficult as the credentials belong to an actual identity.
Generative AI Identity Fraud
Generative AI using the Deep Learning technique is the most trending concept and a rising threat to the digital identities of users. It creates different threat vectors like Deekfakes create highly realistic yet fake digital identities. Gen-AI also enables highly realistic identity document forgery that can spoof and counter multiple Identity Verification solutions. Generative AI Fraud extrapolated the impact of both Biometric and Document Identity Fraud.
Read more on How to Prevent Deepfakes in The Age of Generative AI- Facia
Synthetic Identity Fraud
It is one of the most common types of identity fraud in which an actual user’s social security number, date of birth, or any other information is stolen and combined with another forged identity information to create a new identity.
How Facial Biometrics Help in Preventing Identity Fraud?
Firstly, we need to understand that Facial Biometrics alone are a single-factor authentication protocol which still leaves out loopholes that can act as walkthroughs for fraudsters. That is why multi-factor authentication is a robust practice. Facial recognition is usually combined with fingerprints, passwords, and other credentials to add as many protective layers as possible.
Secondly, we must know that there is a difference between facial identity and facial facial recognition. According to ITRC, the two similar yet different terms are often confused and pose threats to the beneficial uses and seamless implementation of facial verification. Let’s have a look and understand the difference:
In the illustration above it is clear that facial biometrics is a two-way tool to prevent identity fraud especially when it comes to digital identities. Let’s look deeper into how Facial Biometrics help in detecting identity fraud:
| Identity Fraud Prevention through Facial Biometrics | |
| Facial Verification | Facial Recognition |
|
|
|
|
How Facial Comparison Systems can bring in Real-Time Identity Intelligence?
Facial Identity Verification stands as the foundation of Identity Verification Solutions, Any digitally constructed IDV system largely hinges on the efficiency and accuracy of facial verification or facial comparison.
Here are the main features of real-time identity intelligence that an IDV solution should possess:
- Real-time Analysis: It should process facial biometric data instantly to identify an individual user.
- Identity Verification: It should be integrated with the eKYC process to verify a person’s provided information with a stored database.
- Intelligence Integration: The IDV solution should also have capabilities like behavioral analysis and contextual data to enhance facial verification accuracy.
- Continuous Monitoring: It should employ ongoing facial recognition for continuous identity validation in dynamic environments.
- Adaptive Algorithms: It should advance through algorithms that adapt to varying conditions, such as changes in lighting or facial expressions, for reliable verification.
- Security Enhancement: Strengthening identity authentication processes by integrating real-time facial biometrics.
Generative AI Identity fraud attempts are advancing with the creation of deepfakes tactics such as face swaps. It also employs voice cloning, body swaps, and text-based deepfake attacks to commit serious crimes. Thus, to mitigate this, Facia is a unique biometric identity verification suite that provides various identity verification services. From Liveness Detection to Age Verification, Facia is equipped to combat the latest threat vectors and to serve as your handy tool in identity fraud detection.
Frequently Asked Questions
Synthetic identity fraud occurs when a fraudster combines real and fake information to create a new identity. For example, they might use a real Social Security number with fake details like a name, birthday, and address to apply for credit or open bank accounts. They then take loans, leaving the victim with a damaged credit score and significant debt.
Identity fraud generally occurs in four stages: Acquisition, Assembly, Exploitation, and Abuse.
- Acquisition: Fraudsters acquire personal information through phishing, data breaches, or social engineering.
- Assembly: They combine this data to create a synthetic or stolen identity, which they then use to exploit financial systems.
- Exploitation: Fraudsters use the synthetic identity to open accounts, take out loans, or make purchases.
- Abuse: Fraudsters may nurture the synthetic identity over time to build a credible credit history before making a large-scale cash-out, leaving the victim to deal with the financial fallout.
Artificial intelligence (AI) helps prevent fraud in several ways:
- Pattern Recognition: AI algorithms analyze vast amounts of data to identify suspicious patterns.
- Anomaly Detection: AI detects deviations from normal behaviour, like sudden changes in spending patterns or access attempts from unusual locations.
- Link Analysis: AI identifies connections between seemingly unrelated data points, uncovering potential fraud rings or networks.
- Predictive Modeling: AI models predict the likelihood of fraud based on historical data and behavioural patterns.
Generative AI in fraud detection creates models that simulate normal and fraudulent behaviours. These models help train detection systems to identify and distinguish between legitimate and suspicious activities. Generative AI also generates synthetic data to improve the robustness and accuracy of fraud detection algorithms, making it harder for fraudsters to bypass security measures.
Here are some red flags that might indicate synthetic identity fraud:
- Inconsistent information: Discrepancies between application data and public records (e.g., address mismatch).
- Lack of credit history: A new applicant with no credit history or a very short credit file.
- Suspicious employment information: Fake or unverifiable employment details.
- Rapid account opening: Multiple accounts opened within a short period, especially across different institutions.
- Unusual activity: High-value transactions or purchases shortly after account creation.
Being aware of these red flags can help financial institutions and individuals detect and prevent synthetic identity fraud.
