A Predictive Model for Fraud Detection in Digital Payment Transactions using ML Algorithms
DOI:
https://doi.org/10.5281/Keywords:
Predictive Analytics, Digital Payment Fraud Detection, Machine Learning, Financial Transaction Security, Credit Card Fraud DetectionAbstract
The rapid evolution of online payment technologies has greatly simplified access to capital. But instead, the possibility of fraudulent transactions is on the rise. Fraud of a more complex kind may occasionally go undetected by conventional rule-based systems. Combining LightGBM and Random Forest (RF), two machine learning approaches, this study presents a robust fraud detection tool to combat fraudulent financial transactions. The method consists of applying data preprocessing methods which include handling missing data, deleting duplicate data, data normalization, applying one hot encoder method, and applying data balancing using SMOTE technique. Moreover, exploratory data analysis includes using correlation matrix and analyzing transactions in order to discover any fraud patterns. In addition, dividing the processed dataset into a test and train dataset for implementing the model. The LightGBM model surpasses the other strategies with a 98.80% accuracy (ACC), 99.37% precision (PRE), 99.37% recall (REC), 98.80% F1-Score (F1), and an AUC-ROC of 0.99%.
References
[1] Sonam, I. Mazhar, and A. Sulthana, “Online Payment Fraud Detection Using Machine Learning Techniques,” Iconic Res. Eng. Journals, vol. 9, no. 6, Dec. 2025, doi: 10.64388/IREV9I6-1713148.
[2] D. Patel, “Explainable Risk Decision Systems Using Artificial Intelligence Models for Payment Fraud Identification with Mitigation,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA: IEEE, 2026, pp. 01–06, April. doi: 10.1109/ISDFS69419.2026.11459006.
[3] A. K. Padhy, C. Medicherla, B. Vulugundam, C. Kulkarni, T. P. Patel, and S. Shivam, “Latency-Optimized Microservices Orchestration for Real-Time E-Commerce in Multi-Cloud Environments,” in 2025 International Conference on Computer and Applications (ICCA), 2025, pp. 1–6. doi: 10.1109/ICCA66035.2025.11430930.
[4] T. P. Patel and P. Agarwal, “Dynamic Multi-Agent Reinforcement Learning for Competitive E-Commerce Price Optimization: Curriculum-Driven SAC, Equilibrium Analysis and Ethical Safeguards,” in 2026 International Seminar on Intelligent Business and Edge-Computing Research (ISIBER), 2026, pp. 214-219,February. doi: 10.1109/ISIBER68248.2026.11470206.
[5] D. Patel, “Integrating Price Elasticity and Reinforcement Learning: A Data-Driven Framework for Strategic E-commerce Pricing,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA: IEEE, 2026, pp. 1–6, February. doi: 10.1109/ICAIC67076.2026.11395747.
[6] M. E. Lokanan, “Predicting Mobile Money Transaction Fraud using Machine Learning Algorithms,” Appl. AI Lett., vol. 4, no. 2, Apr. 2023, doi: 10.1002/ail2.85.
[7] G. Ahmed, A. Ahmed, M. Ahmed, J. Latha, and P. Kumar, “Indian Banking Precision Marketing: A Comparative Analysis of Machine Learning Customer Segmentation Algorithms,” in 2024 2nd International Conference on Cyber Resilience (ICCR), 2024, pp. 1–6. doi: 10.1109/ICCR61006.2024.10532917.
[8] C. Patel, “Generative AI for Personalized Marketing and Customer Experience in E-Commerce,” Int. J. Emerg. Res. Eng. Technol., vol. 7, no. 1, pp. 12–19, 2026, doi: 10.63282/3050-922X.IJERET-V7I1P103.
[9] S. Irfan, “AI-Driven Credit Scoring: A Study of Machine Learning Algorithms for Enhanced Risk Assessment,” in 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/AIEI69164.2026.11496824.
[10] S. A. Pushkala, “Financial Fraud Identification Using Graph Neural Network And LSTM With Autoencoder-Based Data Refinement,” J. Int. Cris. Risk Commun. Res., vol. 9, no. 1, pp. 198–213, Jan, 2026, doi: 10.63278/jicrcr.vi.3615.
[11] N. R. Panguluri and M. S. Jasti, “Fraud Detection in Digital payments Using Artificial Intelligence,” Int. J. Manag. IT Eng., vol. 14, no. 08, pp. 12–23, 2024.
[12] J. Mishra, S. Rai, B. Moharana, V. K. Singh, S. Dey, and T. Sarkar, “Revolutionizing Financial Fraud Detection in Healthcare Insurance Through Blockchain & AI-Driven Predictive Analysis,” in 2025 International Conference on Technology Enabled Economic Changes (InTech), Tashkent, Uzbekistan: IEEE, 2025, pp. 1516–1522, February. doi: 10.1109/InTech64186.2025.11198190.
[13] L. S. Jattan and R. R. Prajapati, “Digital Payment Fraud Detection System Using Machine Learning,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 07, no. 06, 2025, doi: 10.56726/IRJMETS79983.
[14] B. Mohan, V. R. Surasani, and R. Kumar, “Autonomous Data Stewardship: Multi-Agent AI for Real-Time Master Data Management in Financial Services,” in 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA: IEEE, 2026, pp. 0689–0698, February. doi: 10.1109/CCWC67433.2026.11393832.
[15] P. Parida, “Language Translation Model by leveraging AI and its impact on banking expansion,” Int. J. Comput. Appl., vol. 187, no. 63, pp. 55–59, Dec. 2025, doi: 10.5120/ijca2025926048.
[16] A. Nerella, P. Badri, K. Sundravadivelu, and R. Murugesan, “Navigating Regulatory Hurdles in AI-Driven Credit Card Approvals: Balancing Innovation and Compliance,” J. Inf. Syst. Eng. Manag., vol. 8, no. 4, pp. 1–9, Nov. 2023.
[17] D. S. L. Anusha Nerella, Pratik Badri, Dr. P. Preethi and Sheeba, “Unified Finance : Balancing Mobile Wallets and Traditional Payment Methods,” J. Electr. Syst., vol. 17, no. 4, pp. 157–165, April, 2021.
[18] A. B. Chatterjee, “Designing Zero-Downtime, Cloud-Native Transaction Processing Architectures for 24×7 Global Payment Networks,” Int. J. Emerg. Trends Comput. Sci. Inf. Technol., vol. 7, no. 1, pp. 137–145, Feb, 2026, doi: 10.63282/3050-9246.IJETCSIT-V7I1P120.
[19] J. B. Ambedkar, K. H. Devi, D. M. Chandra, A. H. S. Vardhan, and C. B. V. Yashwanth, “Real-Time Bank Transaction Fraud Detection using Apache Kafka and Machine Learning,” in 2026 International Conference on Smart Electronic Devices and Intelligent Systems (ICSEDIS), IEEE, Apr. 2026, pp. 1246–1253. doi: 10.1109/ICSEDIS68157.2026.11518536.
[20] R. R, S. C. H, and G. R, “Leveraging Machine Learning Techniques of Real Time Detection of UPI Fraud,” in 2025 7th International Conference on Intelligent Sustainable Systems (ICISS), IEEE, Mar. 2025, pp. 1506–1510. doi: 10.1109/ICISS63372.2025.11076419.
[21] A. S. Mandlik, M. S. Ganeshpure, C. N. Kadadas, L. Korra, J. U. Kidav, and M. A. Lavadkar, “AI-Driven Real-Time Threat Detection for UPI Transactions,” in 2025 International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA), IEEE, Aug. 2025, pp. 1–3. doi: 10.1109/ICAMIDA64673.2025.11209290.
[22] E. Prayitno, R. Kartadie, U. Lestari, and M. S. Bunga, “Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment Systems,” in 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2024, pp. 85–89. doi: 10.1109/ISRITI64779.2024.10963478.
[23] D. Dahiphale et al., “Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach,” in 2024 IEEE International Conference on Big Data (BigData), IEEE, Dec. 2024, pp. 4854–4863. doi: 10.1109/BigData62323.2024.10825105.
[24] Y. R. M. R, K. A, R. D, R. Reshma, D. R. Santhosh, and N. Mekala, “An Analytical Approach to Fraudulent Credit Card Transaction Detection using Various Machine Learning Algorithms,” in 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), 2023, pp. 1400–1404. doi: 10.1109/ICEARS56392.2023.10085157.
[25] S. Irfan, “Identification of Financial Fraud Transactions: A Cybersecurity via Machine Learning Methods,” in 2026 IEEE International Conference on AI Engineering and Innovations (AIEI), NIT Jamshedpur, India: IEEE, 2026, pp. 1–6, May. doi: 10.1109/AIEI69164.2026.11497127.
[26] A. A. Almazroi and N. Ayub, “Online Payment Fraud Detection Model Using Machine Learning Techniques,” IEEE Access, vol. 11, pp. 137188–137203, 2023, doi: 10.1109/ACCESS.2023.3339226.
[27] R. Udayakumar, A. Joshi, S. S. Boomiga, and R. Sugumar, “Deep Fraud Net: A Deep Learning Approach for Cyber Security and Financial Fraud Detection and Classification,” J. Internet Serv. Inf. Secur., vol. 13, no. 4, pp. 138–157, 2023, doi: 10.58346/JISIS.2023.I4.010.
[28] F. Khaled Alarfaj and S. Shahzadi, “Enhancing Fraud Detection in Banking with Deep Learning: Graph Neural Networks and Autoencoders for Real-Time Credit Card Fraud Prevention,” IEEE Access, vol. 13, no. August 2024, pp. 20633–20646, 2025, doi: 10.1109/ACCESS.2024.3466288.
[29] B. I. Hameed, M. A. Mohammed, and H. K. Yaseen, “Predicting Fraud: A Machine Learning Approach to Secure Transactions in Credit Card System,” J. Theor. Appl. Inf. Technol., vol. 103, no. 14, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Neetu Sikarwar (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
