Done by
Mr. Thanuj Kumar S – 20171CSE0726
Mr. Utsav Deep – 20171CSE0734
Mr. Syed Atif - 20171CSE0702
Mr. Syed Shoaib - 20171CSE0709
Mr. Tejas Bhatnagar – 20171CSE0721

Under the guidance of
Prof. T. Ramesh - Asst Prof CSE

Every year, the insurance industry losing billions of dollars due to fraud. The act when a person makes fake insurance claims to gain benefits, compensation & other advantages to which they are not entitled is known as Insurance Fraud. Nowadays insurance fraud detection is becoming a tedious problem for insurance companies to deal with as they need more investment and workforces to keep track of every transaction. In this project, we are focusing on the major issue faced by insurance companies that is insurance fraud. we use the machine learning technique to detect insurance fraud based on the transactional data given by the insurance company. We build predictive models and compared their performance by calculation of confusion matrix then it is evaluated on various performance measuring parameters like accuracy, precision, recall, F1 score, and also on AUC curve. SVM (Support Vector Machine) and XG Boost (Extreme Gradient Boosting) are the machine learning algorithms used. After model evaluation, we select the best model for prediction. In this project using this dataset we found XG Boost performing better than SVM. Best Model gave the expected output in terms of Fraud or Not Fraud. Our application is deployed in cloud platform as API, using flask Web framework.

Research Paper "INSURANCE FRAUD DETECTION USING MACHINE LEARNING" Published in https://www.ijaict.com/journals/ijaict/ijaict_abstract/2021_volume08/2021_v8i1/v8i1_1.html (IJAICT India Publication)