Insurance Company working as commercial enterprise from last few years has been experiencing fraud cases for all type ofclaims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, differentorganization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity suchas insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So,we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implements machinelearning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms usedfor classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learningmodel is built using PySpark Python Library.Keywords: Insurance fraud detection, Machine learning, Predictive modeling, Supervised learning, Unsupervised learning, Decisiontrees, Random forest, Logistic regression, Fraud prevention, Support vector machines (SVM), Claim pattern analysis, Model evaluationmetrics, Data driven fraud detection, Insurance claim patterns, Financial losses reduction