Insurance claims estimation and fraud detection with optimized deep learning techniques
Abstract 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 monetar...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12848-0 |
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| author | P. Anand Kumar S. Sountharrajan |
| author_facet | P. Anand Kumar S. Sountharrajan |
| author_sort | P. Anand Kumar |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-47653418538a42aaa4fde162fa1276d7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-47653418538a42aaa4fde162fa1276d72025-08-20T03:42:22ZengNature PortfolioScientific Reports2045-23222025-07-0115112410.1038/s41598-025-12848-0Insurance claims estimation and fraud detection with optimized deep learning techniquesP. Anand Kumar0S. Sountharrajan1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamAbstract 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.https://doi.org/10.1038/s41598-025-12848-0Insurance claims estimationFraud detectionDeep learningVisual geometry group network (VGG net)Residual network (ResNet) and convolutional neural network (CNN) |
| spellingShingle | P. Anand Kumar S. Sountharrajan Insurance claims estimation and fraud detection with optimized deep learning techniques Scientific Reports Insurance claims estimation Fraud detection Deep learning Visual geometry group network (VGG net) Residual network (ResNet) and convolutional neural network (CNN) |
| title | Insurance claims estimation and fraud detection with optimized deep learning techniques |
| title_full | Insurance claims estimation and fraud detection with optimized deep learning techniques |
| title_fullStr | Insurance claims estimation and fraud detection with optimized deep learning techniques |
| title_full_unstemmed | Insurance claims estimation and fraud detection with optimized deep learning techniques |
| title_short | Insurance claims estimation and fraud detection with optimized deep learning techniques |
| title_sort | insurance claims estimation and fraud detection with optimized deep learning techniques |
| topic | Insurance claims estimation Fraud detection Deep learning Visual geometry group network (VGG net) Residual network (ResNet) and convolutional neural network (CNN) |
| url | https://doi.org/10.1038/s41598-025-12848-0 |
| work_keys_str_mv | AT panandkumar insuranceclaimsestimationandfrauddetectionwithoptimizeddeeplearningtechniques AT ssountharrajan insuranceclaimsestimationandfrauddetectionwithoptimizeddeeplearningtechniques |