Conducting authentication and identity verification procedures is a legal obligation in many industries. Compliance with these regulations is paramount for the prevention of economic and financial crimes such as fraud, identity theft, and money laundering, in addition to both personal and economic security. Traditionally, authentication and verification procedures were carried out manually, which was a time-consuming process requiring tedious attention to detail.

As technology has progressed, new digital methods have been developed to systematize authentication and verification procedures and make them more reliable than their humanly conducted counterparts. Introducing Artificial Intelligence (AI) to these procedures as a next step has improved their accuracy, efficiency, and user-friendliness all the more. In this article, we will discuss AI and its role in authentication.

Basics of Artificial Intelligence

Artificial Intelligence, commonly known as AI, is a broad term representing several types of experience-based data processing techniques designed to mimic human development, reasoning, and decision-making. Through analysis, experience, and data input, AI can learn new skills, detect fraud, transcend language barriers, and much more. Artificial intelligence has not only proven itself in numerous industries, but has even become the standard for data authentication, identity verification, object recognition in medical imaging, personal assistance technology, and other processes.

To date, AI’s reach can generally be classified into four techniques: machine learning, Natural Language Processing (NLP), automation and robotics, and machine vision.

Machine Learning: Machine learning is the foundation of AI. This technique is programmed to develop and refine skills based on experience. Deep Learning, a subset of machine learning, is a more advanced form of machine learning, utilizing several layers of neural networks to process, analyze, and learn from data, based on which it can then make human-like decisions, acquire new skills, and operate independently of human supervision.

Natural Language Processing: Natural Language Processing (NLP) is an AI technique used to process written and spoken language. This includes, among others, translating languages both in written and audio formats, editing texts, composing written content, and generating audio responses. NLP is used in Interactive Voice Response (IVR), translation applications, grammar tools, word processors, and more.

Automation and Robotics: Automation and Robotics are two separate forms of AI that are typically paired together in applications. As the name suggests, automation is responsible for improving efficiency and accuracy through the automation of repetitive, monochromatic, and often time-consuming tasks. For example, automation is used for fraud detection in financial institutions or data entry and management in everyday business. While automation is generally designed to perform computer-based tasks, robotics, on the other hand, are engineered to perform physical human tasks, such as assembly line work in a manufacturing facility or certain high-precision surgical operations.

Machine Vision: Machine vision, also known as Computer Vision, is an AI technique used to convert images into processable data that can then be automatically analyzed and assessed for discrepancies. Machine vision is the basis for common functions such as document authentication, identity verification, object recognition, and facial recognition. Though useful in many fields, this technology has become an integral tool in both the healthcare and financial industries.

The Origin of Machine Learning and its Role in AI

The concept of artificial intelligence was first conceived in the 1920s and popularized through science fiction. However, by the 1950s, the idea of artificial intelligence had transformed from a mere concept into an official subject of research and development. At that time, computers already existed, but their functionality was limited to the simple execution of commands. With computers lacking the capability of memory or storage, everything that was done would be lost as soon as the command was completed. This is where machine learning first came into play.

Since its creation, machine learning technology has undergone leaps and bounds of advancement and refinement. From its simple origins of chess-playing programs and the first robots, machine learning has greatly expanded and become the foundation of most AI techniques and algorithms. Algorithms are coded solutions to solve individual technical problems. Depending on their purpose, algorithms range from simple to highly complex and can process or compute many types of data to produce the desired output. With machine learning being the pillar of AI and technology in general, its employment can be seen anywhere technology is used.

AI in Authentication

Unlike verification, which is the initial validation process of matching one's identity to their identification documents, authentication is the subsequent process of verifying that the person attempting to gain access to an account or sensitive information is in fact the same person. There are three types of authentication:

  1. Something you know: password, PIN, Personally Identifiable Information (PII), or an answer to a security question.
  2. Something you have: objects, such as key cards or ID badges.
  3. Something you are: facial or iris recognition, fingerprints, or biometric data.

The first type of authenticator has traditionally been the most common. However, due to a high rate of hacking and data breach occurrences, multifactor authentication - an instance of the second type of authenticator - is now preferred to ensure data security. Among the three types of authentication, the most secure is the third type. This authenticator might also use AI technology to detect whether the person is actually present, also referred to as Liveness Detection.

During the verification process, biometric data may be collected as an additional resource for authentication. There are two types of biometrics in authentication: biometric authentication and behavioral biometric authentication. An example of biometric authentication would be the use of a thumbprint or facial recognition to access an account. Behavioral biometric authentication tracks behavioral patterns such as moving a mouse, scrolling and clicking with a trackpad, or typing on a keyboard. These behavioral indicators are carefully analyzed to determine whether the actions are being performed by a human or a computer.

Incorporating AI into authentication has successfully enhanced security and reduced the number of malicious cyberattacks and data breaches. AI has also opened the door for businesses to scale up, accept more customers, and efficiently process their information, while simultaneously offering a seamless and user-friendly authentication experience.

Biometric Authentication Woman

Challenges and Concerns

AI in authentication is faster, more reliable, and more efficacious than manual authentication procedures that rely on human attention to detail and accuracy. However, the widespread use of AI in authentication and other applications that involve personal information or public safety has also raised concerns about its compliance with data privacy regulations, the ethics of using AI in the military, and the safety of emerging technology such as self-driving vehicles.

Although AI in authentication has a positive track record of fraud detection and data security, the reliability, accuracy, and potential risks associated with it are still brought into question. As with every development, there is always room for improvement. AI algorithms are constantly under the scrutiny of their developers and public opinion. Updating algorithms and creating new ones is certainly a challenge; however, the outcome usually results in the progression of data security, fraud prevention, and user convenience. Despite the inevitable challenges and concerns that AI in authentication will face, the outlook for the future is promising.

Use Cases: Real-world Applications

AI-driven Authentication in Finance:

Financial institutions are responsible for the protection of their customers' personal information and monetary assets, fraud prevention, and economic stability. These responsibilities come with great risks and serious consequences if a breach of security, fraudulent activity, or any other form of financial crime were to occur. To combat illicit activities, financial institutions must comply with strict KYC (Know Your Customer) regulations. To reliably detect suspicious activity and prevent crime, authentication and verification procedures must continually evolve.

As a result of significant technological advancements, financial institutions now offer user-friendly digital services through online portals and smartphone apps. However, because traditional authentication procedures relied on unreliable human capacity and attention to detail, AI in authentication has now become commonplace. AI-driven authentication tools, such as PXL Vision's PXL Ident, offer up-to-date AI algorithms and biometric technology to ensure regulatory compliance, consistent fraud detection, and user convenience.


Healthcare and Patient Data Security

Implementing AI in the healthcare industry has been met with mixed emotions. While its direct use in medical practice and medical research has caused concern for patient data security, its use in authentication has made online healthcare portals and telemedicine more secure and user-friendly.

To access digital healthcare portals, patients use AI-driven multifactor authentication. This method balances advanced security with user-friendliness and convenience. Additionally, AI-driven authentication may be used to protect patient information in hospital databases and prevent security breaches. Due to the sensitivity of patient information, a breach of security could be detrimental to both the patient and the institution. AI in authentication plays a significant role in the healthcare sector, providing the most advanced form of security currently in circulation.                                                                                         


The deployment of AI in authentication has made it possible for companies to stay one step ahead of cybercriminals and defrauders. With its current standing, AI in authentication has a positive outlook for the future, with plenty of room for further growth and advancement.

As AI technology continues to develop and adapt to evolving regulations and international standards, it may either retain its superior position in the realm of computer science or be surpassed by a similar, yet more powerful, emerging technology called quantum computing. Regardless of what the future may hold, the use of AI in authentication has greatly improved data security and user convenience, making it a truly indispensable technology.


What is AI verification?

AI Verification is a type of mechanism used to regulate and validate the compliance of AI technology with relevant regulations.

What are the 3 types of authentication?

The three types of authentication are (1) something you know, (2) something you have, and (3) something you are. The first type is knowledge-based, requiring information such as a PIN, password, or answer to a security question. The second type is something tangible, such as an ID badge or access key card. Finally, the third type of authentication requires something unique to each person, such as a fingerprint, voice recognition, or facial scan.

What is AI biometrics?

AI biometrics refers to the integration of AI with biometric systems. In combination, they are used for identifying and verifying individuals based on their physical or behavioral characteristics. This technology includes features like facial recognition, fingerprint identification, and voice recognition.

A key application of AI in biometrics is Liveness Detection. This process involves AI algorithms verifying the real-time presence of a person, ensuring that the individual is physically present during the verification process. This helps in preventing fraudulent activities, such as using photos, videos, or masks to impersonate someone.