Machine Learning Perspective: Fraud Payment Transaction Detection

Authors

  • Nishant Upadhyay Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India
  • Yogesh Singh Rathore Department of Computer Science and Application, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India
  • Nidhi Bansal Department of Computer Science & Engineering, School of Engineering and Technology, Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, Haryana, India
  • Sushant Jhingran Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India
  • Gaurang Chaudhary Computer Science and Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, Karnataka, India
  • Sudhanshu Maurya Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
  • Rekha Chaturvedi Department of Data Science and Engineering, School of Information Security and Data Science, Manipal University Jaipur, Jaipur Rajasthan, India
  • Kritika Soni Department of Computer Science & Engineering, School of Engineering and Technology, Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, Haryana, India

DOI:

https://doi.org/10.13052/jmm1550-4646.213414

Keywords:

Electronic transaction, payment mode, fraud detection, probability method, machine learning algorithms

Abstract

Online banking transaction fraud occurs when fraudulent activity is initiated by a criminal. Such as seizing accounts and hacking points to execute online fund transfer mechanisms. This scenario is a main challenge for the upcoming data processing that is traveling only online. In today’s scenario, most of the work has become digital. In such cases, machine learning algorithms must be ex-traordinary to take stringent security measures to transfer such funds over a public channel. The paper analyzes some machine learning techniques such as naive Bayes and support vector machines to prevent loss. The machine has been used based on real data and can reduce losses manifold. After processing, the optimization reached 95 percent in terms of accuracy. The implementation can improve business operations to move the funds online while reducing overall risk.

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Author Biographies

Nishant Upadhyay, Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India

Nishant Upadhyay is an Assistant Professor in Sharda University, with over five years of experience in higher education, specializing in Operating Systems, Data Structures, and Machine Learning. He is currently pursuing a Ph.D. and holds an M.Tech in Data Science from Jawaharlal Nehru University (JNU). He has published research in areas such as artificial intelligence, network security, and healthcare, and holds patents for several innovations, including a smartwatch application for real-time electrical signal detection. He can be contacted at an email: nishant.upadhyay23@gmail.com.

Yogesh Singh Rathore, Department of Computer Science and Application, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh, India

Yogesh Singh Rathore, resident of Noida, Uttar Pradesh. He is a graduate of Kurukshetra University, Kurukshetra where he earned a Bachelor’s Degree in Science with Computer Science as a vocational subject. As his enthusiasm grew in computer applications, he met post-graduation in computer applications from Gurukul Kangri Vishwavidyalya, Hardwar, where he earned his first Master’s Degree in computer applications. After a short span of teaching computer science, he indulges more in computer technology and completed his Master’s in Technology from Kurukshetra University, Kurukshetra thereafter he earned his Ph.D. from Mewar University, Rajasthan After that, he became a full-fledged educator and chooses this field as his profession. He can be contacted at email: yogesh.rathore@sharda.ac.in.

Nidhi Bansal, Department of Computer Science & Engineering, School of Engineering and Technology, Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, Haryana, India

Nidhi Bansal is an associate professor at MRIIRS Faridabad Haryana, India. She received a B.Tech. from GBTU-UPTU Lucknow India in 2010, M.E. from NITTTR Panjab University Chandigarh India in 2014, and Ph.D. in Computer Science from AKTU-UPTU Lucknow India in 2023. Her research interests are in cloud computing and machine learning broadly, with applications in data science, and computer networking. She can be contacted at email: nidhi18jul@gmail.com.

Sushant Jhingran, Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India

Sushant Jhingram is an accomplished academic professional with a robust foundation in computer science and engineering. He holds a B.Tech in Computer Science from UPTU (2008). M.Tech from MDU (2014) PhD from Sharda University. Since 2016, he has been contributing as an Assistant Professor in the Computer Science and Engineering Department at Sharda University, where he is known for his expertise in cloud computing and microservices architecture. He can be contacted at email: sushantjhingran@gmail.com.

Gaurang Chaudhary, Computer Science and Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bangalore, Karnataka, India

Gaurang Chaudhary is a Computer Science and Artificial Intelligence graduate from Amrita Vishwa Vidyapeetham with expertise in AI, robotics, IoT, and data analytics. He has completed research-oriented internships at companies like Nokia and GNAP, contributing to AI-driven automation and smart solutions. His projects include a computer vision-based prosthetic, a self-driving vehicle, and smart agriculture systems. He holds multiple patents, including an automatic headlight control system and an intelligent home security system, and has published research in IEEE. Proficient in Python, Android Studio, and IoT development, he is dedicated to leveraging AI for innovative and impactful solutions. He can be contacted at email: gaurangchaudhary619@gmail.com.

Sudhanshu Maurya, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India

Sudhanshu Maurya is currently working as Associate Professor CSE & Research Head at Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), India. He has completed his post-doctoral research at the School of Computer & Communication Engineering, Universiti Malaysia Perlis (UniMAP), Malaysia. He is also associated with the Center of Artificial Intelligence & Robotics, Indian Institute of Technology (IIT) Mandi, for research on AI-based Quantum Cryptographic Techniques. Dr. Maurya has completed his Ph.D. in Computer Science and M. Tech CSE. His area of research is dedicated to Artificial Intelligence, Security, and Cloud Computing. Currently, he has two international Patents (Granted), 13 Indian patents, two edited books, and five-course books, and authored/co-authored more than 150 research papers indexed in Web of Science/SCOPUS. He can be contacted at dr.sm0302@gmail.com.

Rekha Chaturvedi, Department of Data Science and Engineering, School of Information Security and Data Science, Manipal University Jaipur, Jaipur Rajasthan, India

Rekha Chaturvedi is currently working as assistant professor at Manipal University Jaipur. She did B.E. (IT) from Rajasthan University, M. Tech (Software Engineering) from the SGVU and Ph.D. (CSE) from Amity University Rajasthan. Her research interest includes data mining, image processing, digital image watermarking, machine learning, soft computing, and nature inspired computing. She can be contacted at email: rekha.chaturvedi@jaipur.manipal.edu.

Kritika Soni, Department of Computer Science & Engineering, School of Engineering and Technology, Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, Haryana, India

Kritika Soni is an Associate Professor at Manav Rachna International Institute of Research and Studies, with over 11 years of experience in higher education. She holds a Ph.D. and M.Tech in Computer Science from the same institution. Her academic and research expertise spans cyber security, blockchain, big data, cloud computing, and network security. Dr. Soni has published extensively in these domains and holds several patents for her innovative contributions. She can be reached via email at: kritikasoni.set@mriu.edu.in.

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Published

2025-08-13

How to Cite

Upadhyay, N. ., Rathore, Y. S. ., Bansal, N. ., Jhingran, S. ., Chaudhary, G. ., Maurya, S. ., Chaturvedi, R. ., & Soni, K. . (2025). Machine Learning Perspective: Fraud Payment Transaction Detection. Journal of Mobile Multimedia, 21(3-4), 577–598. https://doi.org/10.13052/jmm1550-4646.213414

Issue

2025: Vol 21 Iss 3-4

Section

WPMC 2024