Online Fraud Transaction Detection Using Xgboost, Pca, And Cnn1d

• Author(s):

  P.Ashwini | Dr. K Murali Kranthi | Kolpula Archana | Nakka Poojitha | Samrat Rohith | Badisa Naga Phiani Kumar

• Keywords:

• Abstract:

  The Online Payment Platforms And Financial Services Are Growing Fast. This Means That Online Fraud Transactions Are Also Increasing. Old Methods Of Detecting Fraud Do Not Work Well. They Cannot Find The Changing Fraud Schemes. We Propose A System That Uses Machine Learning To Detect Online Fraudulenttransactions. This System Uses Extreme Gradient Boosting, Principal Component Analysis, And One-dimensional Convolutional Neural Network. Our System Is Designed To Detect Online Fraudulent Transactions Efficiently. It Uses A Combination Of Machine Learning Models To Identifypatterns In Transaction Data. The System Also Uses Data Balancing Methods To Address The Class Imbalance Problem. We Tested Our System. It Performed Better Than Traditional Methods. The Results Show That Our System Is Better At Detecting Fraudulent Transactions. It Also Reduces The Number Of Positive Results. Our System A Solution For Securing Online Financial Transactions.

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Other Details

• Paper id:

  IJSARTV12I4104872

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-05

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