Prediction of automobile insurance fraud claims using machine learning
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.29Keywords:
Prediction, Automobile, Insurance, Fraud claims, Machine learning, Fraud detection.Dimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. This study aims to create a predictive model for accurately identifying potential vehicle insurance fraud claims. Understanding fraud detection processes and operationalizing information communication technology is crucial for implementing corrective actions, but personally reviewing insurance claims is time-consuming and costly. This study explored machine learning algorithms to detect fraudulent vehicle insurance claims. The research evaluated AdaBoost, XGboostNB, SVM, LR, DT, ANN, and RF. AdaBoost and XGBoost classifiers outperformed other models with 84.5% classification accuracy, while LR classifiers performed poorly with balanced and unbalanced data. The ANN classifier performed better with unbalanced data. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the models. The results demonstrate the effectiveness of machine learning in distinguishing between genuine and fraudulent claims, providing insurance companies with a powerful tool to proactively combat fraud and improve their overall risk management strategies. The findings of this research contribute to the insurance industry’s efforts to enhance fraud detection systems, reduce financial losses, and offer more competitive insurance premiums to honest policyholders.Abstract
How to Cite
Downloads
Similar Articles
- Rupesh Mandal, Bobby Sharma, Dibyajyoti Chutia , Smart flood monitoring in Guwahati city: A LoRa-based AIoT and edge computing sensor framework , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- B. S. E. Zoraida, J. Jasmine Christina Magdalene, Smart grid precision: Evaluating machine learning models for forecasting of energy consumption from a smart grid , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. Rukmani, C. Jayanthi, Trust and security in wireless sensor networks: A literature review of approaches for malicious node detection , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Sowmiya M, Banu Rekha B, Malar E, Assessment of transfer learning models for grading of diabetic retinopathy , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Isaac Asampana, Henry M. Akwetey, Ben Ocra, Jones Y. Nyame, Albert A. Akanferi, Hannah A. Tanye, Factors motivating the adoption of virtual learning environments in higher education. Is gender relevant? , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Pritee Rajaram Ray, Bijal Zaveri, Inclusive education for children with learning difficulties in Mauritius: An analytical study among select stakeholders , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Rajalakshmi, V. Sulochana, Enhancing deep learning model performance in air quality classification through probabilistic hyperparameter tuning with tree-structured Parzen estimators , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Ashish Nagila, Abhishek K Mishra, The effectiveness of machine learning and image processing in detecting plant leaf disease , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
<< < 4 5 6 7 8 9 10 11 12 13 > >>
You may also start an advanced similarity search for this article.