• Sudhir Lawand
• Dr. Umesh Kulkarni

this research is carried out to propose a model on prediction of fraud in insurance. In the worldwide time, Insurance frameworks had quickly made a considerable measure of gigantic advancement in our general public. Because of the expanded worry in everyday life, the development of interest of protection expanded. Information mining causes protection firms to disclosure helpful examples from the client database. Insurance companies are uncovered different sorts of misrepresentation and every year endure generous misfortunes. These days, information mining strategies are utilized to find and concentrate learning from dataset. The use of these techniques can be help to misrepresentation location in insurance industry. The essential objective is extortion location strategies for insurance utilizing information mining approach amid the previous years. Discoveries in this examination show that information mining strategies like SVM, Naïve Bayesian and Random Forest decision tree have been connected most generally to give preparatory answers for the troubles inborn in the recognizable proof and arrange of fake accident protection information. The project presents fraud detection method to predict and analyse fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, SVM and Random Forest decision tree algorithms. This enables to build a more robust model with increased accuracy.

aggregation, Regression, Data Mining

"survey on fraud prediction for an application using data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.209-212, June-2019, Available :http://www.jetir.org/papers/JETIR1908971.pdf

"survey on fraud prediction for an application using data mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp209-212, June-2019, Available at : http://www.jetir.org/papers/JETIR1908971.pdf