Detect Suspicious Transactions and Identify Fraud Transactions in Banking Data Using Machine Learning

Authors

  • Geetanjali Sharma Research Scholar, JJTU, Rajasthan. Working as Assistant Professor, Computer Engineering Department, Pimpri Chinchwad College of Engineering, Nigdi,Pune, India
  • Shashi Bhushan Senior Lecturer, Department of Computer & Information Sciences (CIS), Universiti Teknologi PETRONAS , Malaysia
  • Ram Joshi Dean Academics, Department of Information Technology, RSCOE,Pune, India
  • Asmita Manna Assistant Professor,Computer Engineering Department,Pimpri Chinchwad College of Engineering,Nigdi,Pune, India
  • Madhuri Amol Suryavanshi Assistant Professor,Computer Engineering Department,Pimpri Chinchwad College of Engineering,Nigdi,Pune, India

Keywords:

Suspicious Transactions, KNN, SVM, Random Forest, Adaboost, Navies Bayes

Abstract

The identification of potentially malicious transactions is a crucial and difficult activity for the purpose of preventing cyberattacks, which may have devastating effects on a company's finances, reputation, and legal standing. This is an extremely important problem for assuring the safety of investors in investment applications. When it comes to our scenario, the goal of identifying potentially suspicious transactions is to determine the typical patterns of behavior shown by application users and to pinpoint any circumstances that deviate significantly from these norms at the appropriate moment. Changing your password, logging in from several locations, and engaging in high-volume cash flow or trading activities are all examples of events that have a high level of significance for investing applications. It is very essential to promptly identify suspicious transactions and activity and to take the appropriate steps in order to protect the users as well as the assets and reputation of the firm. The proposed method for predicting fraudulent transactions begins with the dataset, which is pre-processed in the initial phase and then passed into further steps where, test/train split was performed, dataset spliced model parameters were defined, then genetic algorithm for feature selection was initiated, which returns weights for particular features after that model is loaded and those weights are passed into it, and model training was then initiated, and once training was completed, model M was returned.

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Published

24.03.2024

How to Cite

Sharma, G. ., Bhushan, S. ., Joshi, R. ., Manna, A. ., & Suryavanshi, M. A. . (2024). Detect Suspicious Transactions and Identify Fraud Transactions in Banking Data Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 243–251. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5062

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

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