Publication Date: 2024/02/05
Abstract: Insurance companies continue to struggle to deliver effective customer service and automated client interactions in the modern digital world. This problem becomes evident when serving customers who have lost their physical documents or when fraud occurs. In response to these challenges, a face recognition system was created to enhance customer identification and mitigate fraud risks within insurance companies. The system matches customer faces with the information stored in three databases, thereby enhancing customer service and security measures. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) and VGGface models were used for face detection and recognition, while SQLite databases were employed for storing customer information. The streamlit Python package was used to input the uploaded image or camera image and the visualization of system output. The developed face recognition system offers insurance companies a means to identify customers through image recognition to improve customer service, increase their efficiency in their activities and avoid frauds that may lead to these insurance companies’ bankruptcy. This system can be seamlessly integrated with existing systems and applied in multiple areas, including customer service, security, and access control. Overall, it provides a practical solution for insurance companies to improve performance and organizational culture while reducing the risk of fraud. The accuracy of the developed recognition approach is 96% which is higher when compared to other existing approaches.
Keywords: Convolution Neural Network, Face Recognition, Machine Learning, Feature Extraction, Deep Learning, Face Matching.
DOI: https://doi.org/10.5281/zenodo.10618701
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN286.pdf
REFERENCES