Insurance claims estimation and fraud detection with optimized deep learning techniques

Estimation and fraud detection in the case of insurance claims play a cardinal role in the insurance sector. With accurate estimation of insurance claims, insurers can have good risk perceptions and disburse compensation within proper time, while fraud prevention helps deter massive monetary loss from fraudulent activities. Financial fraud has done significant damage to the global economy, thus threatening the stability and efficiency of capital markets. Deep learning techniques have proven highly effective in addressing these challenges to analyse complex patterns and relationships in extensive datasets. Unlike traditional statistical methods, which often struggle with the intricate nature of insurance claims data, deep learning models performs well in handling diverse variables and factors influencing claim outcomes. To this extent, it explores the deep learning models like VGG 16 & 19, ResNet 50, and a custom 12 & 15-layer Convolutional Neural Network for accurate estimation of insurance claims and detection of fraud. The proposed work enhanced with Enhanced Hippopotamus Optimization Algorithm (EHOA) combined with a custom 12-layer CNN to optimize the hyperparameters and enhance the performance of the model. Overcoming challenges such as local minima and slow convergence, dynamic population adjustment, momentum-based updates, and hybrid fine-tuning are used with the EHOA. The experimental results reveal that the newly proposed EHOA-CNN-12 attains excellent accuracy (92%) and efficiency in comparison to other state-of-the-art approaches in claims estimation and fraud detection tasks.