Healthcare insurance fraud, with significant financial implications, is a major issue in the United States. The healthcare fraud costs the U.S. approximately $80 billion annually. This includes fraudulent activities such as billing for services not rendered, upcoding, and submitting false claims.

In 2023, the Department of Justice (DOJ) recovered nearly $1.9 billion from healthcare fraud schemes. Significant enforcement actions, such as charging 78 individuals for a $2.5 billion fraud scheme, highlight the ongoing efforts to combat this issue. Thus, machine learning facial recognition is essential in combatting these issues with regular premises.

This article will explore the potential uses of AI-driven face recognition technology and how it will discourage dishonest, fraudulent claims. 

Key Insights of the Article 

• Understanding AI face recognition

• How does a facial recognition scanner work?

• The types of healthcare frauds that can be prevented by biometric face recognition

• Dissuade identity fraud with a face recognition solution

• Face recognition services for healthcare insurance

Comprehending AI Face Recognition

Face Recognition Technology (FRT) is an advanced technical innovation that is used to verify the faces to authenticate the legitimate user. The need for robust security has also increased in the ever-evolving digital realm, where technology is exasperated with continuous revolutions. It has created the need for security and surveillance to verify the users in different sectors.

AI face recognition integrated with machine learning (ML) and convolutional neural networks (CNNs) employs intelligent algorithms to accurately determine the users' identities. This whole process transpires with the help of a physical device, that is a machine learning facial recognition scanner. For healthcare insurance, these devices are integrated with the different places of the hospitals where the patients and insured can verify themselves into the system.

The Functionality of Facial Recognition Scanner

The facial recognition scanner is an optimal device for efficiently verifying the user. It can be of two types, either it is placed in the walls of the hospitals or it can be integrated into the mobile phones with the help of the software. However, the facial recognition scanner works the same in the prior types, that is: 

Image Capture: A camera captures a digital image of the face, which serves as the raw input for the facial recognition system.

Face Detection: The system recognizes and separates the face within the captured image, detaching it from the background and other objects.

Preprocessing: The isolated face image is enhanced and normalized to standardize lighting, scale, and orientation for better recognition accuracy.

Feature Extraction: Key facial characteristics, such as the space between the eyes, nose shape, and cheekbone structure, are extracted to create a unique facial code.

Feature Comparison: The extracted elements correspond to a database of known faces to find a match based on similarities.

Decision Making: The system decides if the face matches any in the database by evaluating the similarity score against a predefined threshold.

Result Output: The system provides the result, indicating whether a match was found, and may trigger actions like granting access or logging the attempt.

Continuous Learning: The system may update its database and improve algorithms based on new data to enhance accuracy and adapt to changes in appearance.

Type of Healthcare Frauds Prevent By Biometric Face Recognition

Several types of healthcare fraud can be prevented with the help of biometric face recognition. For instance:

Unbundling or Double Billing: This includes submitting multiple claims for the same service or to different sources. 

Phantom Billing: It involves billing for a service visit or supplies the patient never acquired.

Identity Theft or Identity Swapping: This is the most common type that involves using another person’s health insurance or allowing another person to use your insurance.

Impersonating Professionals: It involves providing or billing for health services or equipment without a license issued by an accredited institution.

Forgery: This involves creating or using forged prescriptions for unnecessary claims. 

Face Recognition Solution Dissuade Identity Fraud

In the insurance sector, dishonest claims are so common that people exploit the advantages of this facility. Insurance companies tend to take robust security measures to verify the user and their credentials to ascertain them. Thus, they opt for face recognition solutions to deter identity fraud, especially when people disguise their identities to obtain invalid benefits. This technology is the epitome of advanced artificial intelligence that is incorporated with machine learning facial recognition to dissuade this insurance scam. 

Face Recognition Services In Healthcare Insurance

Facial recognition services are optimal for the healthcare sector as the identities of the patients can be determined before giving them sensitive treatments. There is a case in the U.S. where there were two patients of the same first and last name, and one of them was a cancer patient who was going to get chemotherapy. The nurse verified the name and put the drip on the wrong patient as she did not check the IDs and face. Thus, the patient's carer sued the hospital, and they had to pay heavy penalties to the patient and his family.

Final Statement

Incorporating machine learning facial recognition technology in the insurance sector is pivotal to combating identity fraud and reducing fraudulent claims. Insurance companies can enhance security with AI-driven face recognition technology that ensures accurate patient verification and mitigates financial losses associated with healthcare fraud. This technology simplifies the verification process, maintaining the integrity of healthcare services to create a more reliable and efficient system. Adopting such innovative solutions will protect against deceitful practices and promote ethical standards within assiduousness.