Anonymized Credit Card Transaction Using Machine Learning Techniques

  • B. K. Padhi
  • S. Chakravarty
  • B. N. Biswal
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


In the last few years, anonymized credit card transactions have grown more threats that have caused serious consequences in the finance and banking sectors. Due to the dramatical growth of the online payment system, now many banks and financial sectors are implementing various types of automatic fraud detection system to analyze the fraud transactions; machine learning (ML) is one of the promising approaches to find out the fraud transactions. Machine learning methodologies have proved the most promising solution for anonymized transactions. This paper comparatively analyzes the basic machine learning algorithms which include SVM, LDA, QDA, DT, and RF for fraud detection. At the same time, some of the modern open-sourced boosting machine learning algorithms which include XGBoost, LGBoost, and CatBoost are also implemented.


Fraud Fraud detection Machine learning Boosting algorithm Performance evaluation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • B. K. Padhi
    • 1
  • S. Chakravarty
    • 1
  • B. N. Biswal
    • 2
  1. 1.Department of Computer Science & EngineeringCenturion University of Technology & ManagementBhubaneswarIndia
  2. 2.Department of Computer Science & EngineeringBhubaneswar Engineering CollegesBhubaneswarIndia

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