Fraud Detection And Analysis For Insurance Claims Using Machine Learning

• Author(s):

  Dr. M. Kishore Kumar | Golla Madhu | Guguloth Arun Kumar | Bhukya Dilip Kumar

• Keywords:

  Insurance Fraud Detection, Machine Learning, Classification Algorithms, Django Framework, Predictive Analytics, Supervised Learning, Voting Classifier, Data Preprocessing.

• Abstract:

  Insurance Fraud, Particularly In Claim Processing, Results In Billions Of Dollars In Losses Annually For Companies Worldwide. Manual And Rule-based Detection Mechanisms Are Often Inefficient In Detecting Sophisticated Fraud Schemes. This Study Proposes An Automated System That Leverages Machine Learning (ML) Algorithms To Classify Insurance Claims As Genuine Or Fraudulent. Supervised Learning Models, Such As Logistic Regression, Support Vector Machine (SVM), Decision Tree, Naïve Bayes, And SGD, Were Trained And Evaluated. An Ensemble Model Using A Voting Classifier Outperformed The Individual Classifiers. The System Was Deployed Using Django On A WAMP Server, Which Integrated Real-time Prediction And User Access Control.

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Other Details

• Paper id:

  IJSARTV11I7103847

• Published in:

  Volume: 11 Issue: 7 July 2025

• Publication Date:

  2025-07-01

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