Abstract
In current era, people are influenced with various types of insurance such as health insurance, automobile insurance, property insurance and travel insurance, due to the availability of extensive knowledge related to insurance. People are trending to invest in such kinds of insurance, which helps the scam artist to cheat them. Insurance fraud is a prohibited act either by the client or vendor of the insurance contract. Insurance fraud from the client side is encountered in the form of overestimated claims and post-dated policies etc. Although, insurance fraud from the vendor side is experienced in the form of policies from non-existent companies and failuew to submit premiums and so on. In this paper, we perform a comparative analysis on various classification algorithms, namely Support Vector Machine (SVM), Random-Forest (RF), Decision-Tree (DT), Adaboost, K-Nearest Neighbor (KNN), Linear Regression (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP) to detect the insurance fraud. The effectiveness of the algorithms are observed on the basis of performance metrics: Precision, Recall and F1-Score. The comparative results of classification algorithms conclude that DT gives the highest accuracy of 79% as compared to the other techniques. In addition to this, Adaboost shows the accuracy of 78% which is closer to the DT
Highlights
The major issue faced by insurance companies is a fraud that causes immense loss to insurance companies sometimes beyond repair
It from training data but in unsupervised learning, we was claimed that the proposed approach produced the cannot infer which one is a fraud case and which one highest accuracy
Data mining approaches like grouping, classification, and variance detection could be used in insurance fraud detection, and based on the previous data we can predict future fraud claims using these techniques
Summary
The major issue faced by insurance companies is a fraud that causes immense loss to insurance companies sometimes beyond repair. Different machine learning and data mining techniques have proven to be promising in detecting frauds. In a fraud detection scenario in a supervised algorithms i.e. Decision Trees, Support Vector learning method we can find out fraud and legal cases. It from training data but in unsupervised learning, we was claimed that the proposed approach produced the cannot infer which one is a fraud case and which one highest accuracy. Data mining approaches like grouping, classification, and variance detection could be used in insurance fraud detection, and based on the previous data we can predict future fraud claims using these techniques. Machine learning approaches used in this research were Bayesian Networks, Decision Trees, and rule-based algorithms. The models used for insurance fraud detection were Support Vector Machines, Decision Trees, and artificial neural networks.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Mehran University Research Journal of Engineering and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
