Insurance claims estimation and fraud detection with optimized deep learning techniques.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning in Finance

Background:

• Accurate insurance claims estimation and fraud detection are crucial for financial stability and risk management.
• Traditional methods struggle with complex insurance data, necessitating advanced analytical techniques.
• Financial fraud poses a significant threat to capital markets and economic stability.

Purpose of the Study:

• To explore deep learning models for enhanced insurance claims estimation and fraud detection.
• To develop and evaluate a novel hybrid model combining deep learning with optimization algorithms.
• To improve the accuracy and efficiency of fraud detection and claims processing in the insurance sector.

Main Methods:

• Utilized deep learning models including VGG 16 & 19, ResNet 50, and custom Convolutional Neural Networks (CNNs) of 12 and 15 layers.
• Introduced the Enhanced Hippopotamus Optimization Algorithm (EHOA) to optimize hyperparameters for a custom 12-layer CNN (EHOA-CNN-12).
• Implemented techniques like dynamic population adjustment and momentum-based updates within EHOA to address optimization challenges.

Main Results:

• The proposed EHOA-CNN-12 model demonstrated superior performance in insurance claims estimation and fraud detection.
• Achieved an excellent accuracy rate of 92% with the EHOA-CNN-12 model.
• The hybrid approach significantly improved model efficiency and accuracy compared to other state-of-the-art methods.

Conclusions:

• Deep learning models, particularly the EHOA-CNN-12, offer a powerful solution for complex insurance data analysis.
• The integration of EHOA effectively optimizes deep learning models, enhancing their performance in fraud detection and claims estimation.
• This research provides a robust framework for improving financial security and operational efficiency within the insurance industry.