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Monetary Transaction Fraud Detection System Based on Machine Learning Strategies

  • Lakshika Sammani ChandradevaEmail author
  • Thushara Madushanka Amarasinghe
  • Minoli De Silva
  • Achala Chathuranga Aponso
  • Naomi Krishnarajah
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1041)

Abstract

Fraud is a costly business problem which causes every organization to face huge loss. Fraud may lead to risk of financial loss and loss of the confidence of customers and stakeholders of the company. Cyber security teams and internal audit departments of most of the organizations try to monitor such fraudulent activities using traditional rule-based fraud detection systems. However, with the rapid adaptation of online financial transactions, it is more difficult to identify fraudulent activities by static methods and via data analysis. Further, as traditional rule-based fraud detection systems cannot dynamically adjust the rule set based on the behavioral changes of the fraudsters, there is a high possibility of detecting false positive alerts. The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.

Keywords

Machine learning Artificial neural network Convolutional neural network Recurrent neural network Bayesian belief network Hidden Markov model Support vector machine and decision trees K-nearest neighbor (K-NN) Hidden Markov model (HMM) 

Notes

Acknowledgements

I would like to express my sincere gratitude to everyone who were behind me from the beginning to the completion of the paper. Firstly, I am very much thankful to my supervisor Mr. Achala Chathuranga Aponso for the unwavering support, guidance, and insight throughout this period. I acknowledge with thanks for the encouragement received from Ms. Naomi Krishnarajah, Dean of the Informatics Institute of Technology. Special thanks to Mr. Thushara Madushanka Amarasinghe and Ms. Minoli De Silva for all the support and ideas. Last but not least, I would like to express my love and gratitude to all my family members, especially to my father and mother for the constant support toward my studies and for believing me. I am sure that this would have not been possible without the valuable contribution of each one of you.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Lakshika Sammani Chandradeva
    • 1
    Email author
  • Thushara Madushanka Amarasinghe
    • 1
  • Minoli De Silva
    • 1
  • Achala Chathuranga Aponso
    • 1
  • Naomi Krishnarajah
    • 1
  1. 1.Department of Computing, Informatics Institute of TechnologyUniversity of WestminsterColomboSri Lanka

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