International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Vehicle Insurance Claims Fraud Detection using Machine Learning |
|---|---|
| ABSTRACT | Fraudulent practices in vehicle insurance have emerged as a significant concern, leading to considerable financial losses for insurers globally. Such fraud occurs when false or misleading information is provided to obtain benefits that are not legitimately entitled. Traditional detection approaches, often reliant on manual review or rule-based systems, struggle to process and analyse the large, complex datasets associated with modern claim records. To address this limitation, the present study applies machine learning techniques to classify claims as either genuine or fraudulent. Several algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbours, Support Vector Machine, and Gaussian Naïve Bayes—are trained on a historical dataset of insurance claims. Model performance is assessed using precision, recall, and F1-score metrics. Results indicate that machine learning methods can significantly enhance both the accuracy and efficiency of fraud detection, enabling insurers to make faster, more informed decisions with increased confidence. |
| AUTHOR | APOORVA S M, DR. P DEVAKI Dept. of ISE, National Institute of Engineering, Mysore, Karnataka, India Professor, Dept. of ISE, National Institute of Engineering, Mysore, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401066 |
| pdf/66_Vehicle Insurance Claims Fraud Detection using Machine Learning.pdf | |
| KEYWORDS | |
| References | [1] “Detecting insurance claims fraud using machine learning techniques” published by Riya Roy and Thomas George K in 2017 [2] “Fraud Detection and Analysis for Insurance Claim using Machine Learning” published by Abhijeet Urunkar, Rashmi Bhat and Nandinee Mudegol in 2022 [3] “Enhancing Auto Insurance Fraud Detection Using Convolutional Neural Networks” published by Ratchanon Wongpanti and Sirion Vittayakorn in 2024 [4] “The Identification of Insurance Fraud – an Empirical Analysis Working papers on Risk Management and Insurance” published by Kajia muller in 2013 [5] “A Model for the Detection of Insurance Fraud, Geneva Papers on Risk and Insurance Theory” published by Belhadji, E., G. Dionne, and F. Tarkhani in 2012 [6] “A Survey on Fraud Analytics Using Predictive Model in Insurance Claims” published by K. Ulaga Priya and S. Pushpa in 2017 [7] “A Model for the Detection of Insurance Fraud” published by E. B. Belhadji, G. Dionne, and F. Tarkhani [8] “Performance comparative study of machine learning algorithms for automobile insurance fraud detection” published by B. Itri, Y. Mohamed, Q. Mohammed, and B. Omar in 2019 [9] “Detecting Auto Insurance Fraud by Data Mining Techniques” published by Bhowmik R in 2019 [10] Kaggle, “Insurance Claim Fraud Detection Dataset,” Kaggle.com, [Online]. Available: https://www.kaggle.com/ [Accessed: 02-Aug-2025]. |
