ABSTRACT
Fraud complexity decreases insurance sector efficiency and trust. Traditional fraud detection technologies cannot detect dynamic and complicated fraud behaviours. CNNs offer cutting-edge pattern recognition to solve this problem. The project will use a state-of-the-art CNN-based prediction framework to reduce false positives, boost customer confidence, and improve fraud detection accuracy. Analysis of large transaction anomaly and consumer behaviour datasets using CNN layers. Preprocessing, pooling, and optimisation in these models allow step-wise accuracy and scalability with high outcome. This CNN-based model outperformed all previous techniques with impressive precision, recall, and F1-score increases. It achieved an accuracy of 92%, 8% better. It efficiently identifies fraud while decreasing operational inefficiencies. Scalability, precision, and adaptability of CNNs in fraud detection clearly open the gates to revolutionary potential. The concept thus greatly enhances customer confidence in the insurance industry, and operational excellence is bound to follow.
