The booming cyberspace has allowed fraudsters and financial criminals to engage in cybercrimes. Identity and data theft, impersonation, phishing, fraud, money laundering, and terrorist financing are some of them. It is critical for all organizations, including financial institutions, to protect their customer data for ensured KYC/AML compliance. The best means to achieve data protection is by enhancing your organization’s security. It is specifically essential for financial institutions because they are more prone to illicit financial practices and crimes. Moreover, AML/CFT compliance failures lead to hefty penalties on the organizations, resulting in financial losses and reputational damage. Therefore, they must integrate machine-learning facial recognition into their system.
Facial Recognition Machine Learning for Biometric Verification
Facial detection or recognition is an artificial intelligence-backed computer technology that recognizes human faces and is used for tracking humans in real time. It uses neural network technology, an AI method that trains computers to imitate the human brain in data processing. Machine learning facial recognition carries out the following functions:
- Face tracking, in which Facial recognition machine learning identifies and tracks human faces in digital images or videos.
- Face analysis uses facial expressions to detect certain parts of an image or video for determining gender, age, and even emotions.
- Facial recognition system that requires detection data to produce a faceprint and match it with stored ones.
Machine-learning facial recognition is a biometric verification procedure that identifies a person’s face for identity verification through facial data. This technology operates by collecting unique biometric data associated with a person’s facial expressions and features to identify and authenticate a person. Considering its significance for the finance sector in today’s age, your financial organization must integrate biometric facial recognition technology into your KYC mechanism to deter fraud and comply with AML regulations.
Value of Facial Identification Technology in the Digital Era
According to an estimate, the facial identification market was roughly USD 5 billion in 2021 and is anticipated to reach USD 12.67 billion by 2028. These astonishing statistics display the significance of machine learning facial recognition in the 21st-century digital era. As per the above-cited figures, more investment in biometric machine-learning facial recognition is expected to thrive rapidly. This is solely due to the essential role it plays in elevating the security of businesses, especially financial institutions such as banks and digital asset exchanges. It is an effective solution to prevent fraud and identity theft while securing customer data from fraudsters and hackers. Furthermore, facial identification is valuable for efficient customer onboarding, transparent transaction monitoring, and AML/CFT compliance because of its top-notch accuracy.
Advantages of Facial Recognition Technology for Various Businesses/Organizations
Being the owner of any business, specifically a financial organization, you must be aware of the numerous advantages of facial recognition for your organization. Some of them are listed as follows:
- Machine-learning facial recognition ensures a unique, prompt, and swift identity verification process, particularly for remote users, without consuming much time and effort. Thus, it increases your credibility and customers’ trust in your organization.
- It can be easily integrated into various sectors such as transport, aviation, commerce, etc.
- AI-based facial recognition ensures security through unique facial recognition algorithms that protect your customer’s identity and recognize any suspicious identity.
- Machine-learning facial recognition guarantees seamless customer onboarding and aids your business or financial institution in achieving regulatory compliance via transparent means.
Use of Facial Recognition Technology in Law Enforcement
Law enforcement is also one of the essential sectors; thus, facial recognition technology in this industry holds great significance, like others. The law enforcement agencies such as the police and security organizations have widely integrated machine-learning facial recognition into their investigation apparatus. It compares the images or videos of a person from different sources with those in the database. Therefore, it enables the LEAs to identify, trace and track suspects and criminals in real-time, accelerating investigations to a great extent.
Deep Learning Facial Recognition
As mentioned above, machine-learning facial recognition uses neural network technology, a type of machine learning that uses interconnected neurons or nodes in a layered structure resembling the human brain. Face recognition deep learning allows the detection and verification of facial features of human beings. The algorithms first identify human eyes, followed by other features such as eyebrows, mouth, and nostrils. This high-tech innovation is a revolution in digital technologies, serving the purpose of security assurance and prevention of multiple crimes.
SaaS Solution Providers for Facial Recognition Online
SaaS or “software as a solution” providers are the companies that offer solutions such as KYC, KYB, AML, KYT, KYI, and biometric verification to various businesses and financial institutions. Most importantly, they provide solutions for facial recognition online through biometric verification for legitimate customer onboarding for fraud prevention and identification of high-risk customer profiles such as politically exposed persons (PEPs), sanctioned individuals or corporations, and individuals from high-risk countries. The multi-layer authentication processes ensure data protection from fraudsters through machine-learning facial recognition.
To Conclude
Machine-learning facial recognition is highly significant for every organization in any sector, specifically financial institutions. This technology uses deep learning to detect, identify and verify human faces to ensure security and prevent financial crimes.
