Detecting Fraudulent Patterns: Real-Time Identification using Machine Learning


  • Manoj Tarambale Associate Professor, Electrical Engineering Department, PVG’s College of Engineering and Technology & G K Pate Institute of Management, Pune - 09 (India)
  • Ketaki Naik Associate Professor, Department of Information Technology, Bharati Vidyapeeth's College of Engineering for Women, Pune
  • Rahul Manohar Patil Head, Department of Electronics and Telecommunication Engineering, NES’s Gangamai College of Engineering. Nagaon, Dhule (Maharashtra), India
  • Rajendra V. Patil Assistant Professor, Department of Computer Engineering SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (M.S.), India
  • Shailesh Shivaji Deore Associate Professor, Department of Computer Engineering SSVPS B S DEORE College of Engineering Dhule Maharashtra
  • Mahua Bhowmik Associate Professor, Department of Electronics and Telecommunication Engineering, Dr. D.Y. Patil Institute of Technology, Pimpri, Pune


Machine Learning, fraud detection, Healthcare, KNN, LR, NB


The difficulty of identifying fraudulent activity in real-time has grown in importance in the age of digital transactions and networked technologies. The comprehensive strategy presented in this work uses the strength of machine learning techniques to address this pressing problem.Our study focuses on creating and implementing a reliable, real-time fraud detection system that can change with changing fraud patterns while maintaining a high degree of accuracy. To analyse huge amounts of transactional data in real-time, we suggest a system that combines multiple machine learning approaches, such as K Nearest Neighbour, Logistic regression, Naive Bayes model.Our system's capacity for constant learning and adaptation is at its core. Anomaly detection methods are used to find out-of-the-ordinary trends in transaction data, and historical data is used to train prediction models that can predict fraudulent behaviour. In order to identify anomalies at the individual level, the system also uses user behaviour analysis, which improves accuracy and lowers false positives.The proposed machine learning method is highly accurate and quick at detecting fraudulent activity, making it appropriate for use healthcare domain. Our system offers a strong defence against the constantly changing terrain of fraudulent activities by upgrading its knowledge base and reacting to new fraud trends, protecting both businesses and customers.


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How to Cite

Tarambale, M. ., Naik, K. ., Patil, R. M. ., Patil, R. V. ., Deore, S. S. ., & Bhowmik, M. . (2024). Detecting Fraudulent Patterns: Real-Time Identification using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 650 –. Retrieved from



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