Abstract
Fraud claims of automobile insurance are great loss for insurance companies as well as client with insurance policy. The main motive of this work is to devise a mechanism first as predictive model to classify whether a policy claim is classified as genuine or not and secondly what different kinds of parameters should be goaled to find fraud claims. To accomplish this motive, greatly precise prediction models are made by finding key features set through feature selection methods that are necessary for avoiding loss in future. The study of parametric and non-parametric algorithms that are statistical learning in nature reflects on to diminish unpredictability and escalates the possibilities of finding the accurate claims. The required feature set that is important for a framework is investigated by calculating feature relevant formed on the perceived specifics of a policy claim through a cross-validation. This is further tested for improvement of the efficiency through which claims of automobile fraudulent are precisely distinguished using adaptive neuro-fuzzy inference system. The accuracy of the model based on adaptive neuro-fuzzy inference system can reach more than 98% with an exact feature set chosen through a cross-validation. The endeavour attempted here would surely benefit not only insurance sector but also other fraud detection in other sectors like credit card, financial communication and many more in a way to get rid of increasing fraud in financial sector.
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Panda, G., Dhal, S.K., Satpathy, R., Pani, S.K. (2022). ANFIS for Fraud Automobile Insurance Detection System. In: Borah, S., Mishra, S.K., Mishra, B.K., Balas, V.E., Polkowski, Z. (eds) Advances in Data Science and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-16-5685-9_50
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