How Fraudsters Leverage AI and Deepfakes for Identity Fraud

To understand this, let’s first understand the concept of identity fraud – how is it performed and why.

Understanding Identity Fraud

What is Identity fraud?

Unauthorized access to someone’s sensitive information and then use that info to perpetrate a crime, mislead, or swindle that person or a third party to profit from the pleasures associated with the false identity.

Why is it performed?

For monetary benefits.

Such as getting access to the target’s loan, bank, or credit card accounts.

False or counterfeit identity documents are generally used in illegal activities (for example, breaking into restricted areas) and in communicating with government authorities like immigration.

The use of false names, ID cards, falsified or forged documents, and age fraud to just “conceal” one’s true identity are some definitions of identity fraud.

The Role of AI in Identity Fraud

Deepfake technology is increasingly being used in fraudulent operations.

Deepfakes are a tool that cybercriminals are using more and more to get beyond biometric security measures, pose as people, and steal identities.

Due to the possibility that these advanced counterfeits would go undetected by conventional identity verification techniques, this presents a serious threat to both people and organisations.

AI and biometric analysis

Biometric authentication enables users to log in using built-in biometrics on their device.

Such as facial features, fingerprint recognition, or iris scanning.

What’s the purpose?

Biometric authentication is known as one of the safest login methods since it is undoubtedly multifactor and is not dependent on knowledge-based questions that criminals may readily get through data dumps, social engineering, and other techniques.

Because of its improved security, enhanced user experience, and the introduction of new technologies like passkeys, businesses are adopting biometric authentication at an increasing rate.

However, as more companies use biometric identification, fraudsters are also becoming more interested in it, and deepfakes give them a way around this safe login mechanism.

With the rise of deepfakes and generative AI, criminals may now fabricate synthetic biometric data, including face characteristics, to fool biometric systems. 

This might jeopardize the reliability of biometric-based identity verification by allowing unwanted access to sensitive data, equipment, or protected locations.

Below mentioned are deepfakes tactics to circumvent biometric security measures are-

• Tampering with face recognition software
• Making use of voice cloning technologies

Machine learning in detecting irregularities

Machine Learning (ML) is a series of AI algorithms – developed on the basis of historical data to provide risk protection. Additionally, Massive amounts of data are analyzed using data mining in order to identify trends and predict future events.

Subsequently, AI algorithms identify trends that point to fraud, such as anomalous volumes of transactions, irregular account activity, or odd access hours.

Training determines how good an AI model will be! Using past information, predictive programs proactively detect patterns of fraudulent behavior.

In addition, real-time fraud detection tools can notify your business of any suspicious conduct.

Identity theft may be effectively prevented via machine learning and data analysis, which analyze and interpret data to identify trends in behavior. Financial institutions have the ability to halt illicit usage by terminating idle sessions or alerting account holders about sign-ins from unfamiliar devices whenever abnormal activity is detected.

Continuous learning and adaptation

In the data science world, continuous learning refers to a technique whereby a machine learning model continuously evolves and becomes better with time as it is exposed to fresh data. This is comparable to how, over the ages, humans have acquired (or rejected) information and developed abilities.

Data is essential to continuous learning.

Why?

Because it gives the model the knowledge it needs to grow and change. Optimization is impossible without fresh data; without it, a model is unable to function better or adjust to changes.

To guarantee that the model is picking up the appropriate lessons along its workflow, the data must be correct, dependable, and pertinent to the issue the model is attempting to address. Data is not just necessary once, but continuously in the context of continuous learning.

Mitigating the Threat to Businesses

Businesses must implement stringent identity security measures to combat the increasing threat posed by deep fakes and safeguard individuals throughout their entire journey as a customer.

However, the complexity arises from consolidating risk signals from various tools and solutions like multiple fraud solutions, Intelligent Document Processors, as well as customer databases.

This enables businesses to enhance their defense against deepfakes and unify identity security measures by incorporating the following steps:

•       Authentication Services to enhance biometric authentication and address vulnerabilities in secure passkeys,
•       Multimethod Detection and Response Services utilizing machine learning to detect user anomalies,
•       Identity Verification to identify forgeries with sophisticated IDs and video deep fakes, and
•       Identity Management for a comprehensive view of the user and storage of user data.

The future of AI in identity verification

Ethical use of AI

Innovative AI-powered identity verification and biometric verification solutions with liveness checks are becoming popular among innovative companies.

Reason?

These methods ensure that the identity being confirmed of the prospective clients is both physically present and authentic, adding an additional layer of protection.

Liveness checks verify that biometric data is being collected from a live subject and not from a pre-recorded or controlled source by using dynamic interactions, such as gestures or facial movements.

Deepfake detection enabled by AI can guarantee that you are onboarding genuine people each and every time, protecting you from losing money or reputation due to deepfake fraud.

Signzy’s Identity Verification Solutions

The majority of corporate scams occur as a result of human collaboration.

With the infusion of AI, Signzy’s onboarding system gives documents and data intelligence comparable to that of a person, yet it is unhackable.

Tools and solutions at Signzy is a whole end-to-end client onboarding process since it has capabilities including picture extraction, categorization, object recognition, validation, fabrication check, video-based identity verification, and liveness check.

With only a few clicks, Signzy’s Identity Authentication expedites the verification process, increases conversion rates, complies with AML and KYC regulations, and improves fraud detection for organisations.