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FRAUD DETECTION IN BANKING USING MACHINE LEARNING ALGORITHM

  • Unique Paper ID: 167769
  • Volume: 11
  • Issue: 4
  • PageNo: 298-304
  • Abstract:
  • The number of financial transactions has grown dramatically in the last few years due to the growth of financial institutions as well as the acceptance of web-based e-commerce. In internet banking, fraudulent transactions are becoming an increasingly common issue, and detecting fraudulent activity has never been easy. In banking, detection of fraud is observed as a binary machine learning problem where input is either categorized as fraud or not. In this proposed methodology, we have proposed a banking fraud detection using three steps: Pre-processing, feature extraction as well as classification. Pre-processing has done for data cleaning process, and then the feature extraction used for detecting transaction time and finally classification hybrid (Decision Tree-Random Forest) for detecting the fraud in banking. Results showed that the hybrid algorithm gives 95.8% accuracy and least error compared with single machine learning algorithms
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 4
  • PageNo: 298-304

FRAUD DETECTION IN BANKING USING MACHINE LEARNING ALGORITHM

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