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

P Anand Kumar¹, S Sountharrajan²

¹Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamilnadu, India.

Scientific Reports

|July 27, 2025

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This study introduces a novel deep learning approach for insurance claims estimation and fraud detection. The Enhanced Hippopotamus Optimization Algorithm combined with a custom 12-layer Convolutional Neural Network achieved 92% accuracy, outperforming existing methods.

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.

Keywords:

Deep learning Fraud detection Insurance claims estimation Residual network (ResNet) and convolutional neural network (CNN)Visual geometry group network (VGG net)

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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.