Journal of Computers, Mechanical and Management

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I. D. Sonawane, P. R. Suyal, S. Chavan, R. Shirwaikar, Detection of UPI mule accounts using machine learning and Streamlit-based predictive analytics, Journal of Information and Communications Technology: Algorithms, Systems and Applications, 2026, 2(1), 26301, doi: https://doi.org/10.64189/ict.26301.

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© The Author(s) 2026.

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative Commons License and changes need to be indicated if there are any. The images or other third-party material in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons License and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this License, visit: httpshttps://creativecommons.org/licenses/by-nc/4.0/

Abstract

By facilitating quick and easy money transfers, the Unified Payments Interface (UPI), in particular, and the rapid growth of digital payment systems in India have drastically changed the financial landscape. However, the growing use of UPI has also resulted in an increase in fraudulent activity, particularly through mule accounts, which are used to route or launder money while hiding the identity of scammers. This study suggests a machine learning-based framework for identifying UPI mule accounts using transactional data to solve this problem. The suggested approach uses the Light Gradient Boosting Machine (LightGBM) algorithm to determine whether a transaction is authentic or fraudulent. Transaction-related characteristics such as transaction amount, time, user demographics, and fraud risk labels are included in the dataset, which was taken from a publicly accessible Kaggle repository. Methods for preprocessing data, such as label encoding, feature scaling, and Synthetic Minority Oversampling Technique (SMOTE), were used to address class imbalance and enhance model performance.With a Receiver Operating Characteristic–Area Under the Curve (ROC-AUC) score of 0.9962 and an accuracy of 96.55%, the trained LightGBM model proved to have a strong ability to distinguish between fraudulent and legitimate transactions. Additionally, a web application based on Streamlit was created to facilitate interactive model demonstration and real-time fraud risk prediction. The suggested framework offers a scalable and effective way to improve the UPI ecosystem's fraud monitoring systems.

Keywords:

• UPI
• Mule account
• Fraud detection
• Machine learning
• LightGBM
• Streamlit
• ROC-AUC

Graphical Abstract

Novelty statement

This study proposes a LightGBM-based UPI mule detection framework with superior ROC-AUC (0.9962) and real-time analytics support.