ItemConference Paper

Fraud Prevention in Banking: Machine Learning-driven Approaches for Detecting Payment Anomalies

Title
Fraud Prevention in Banking: Machine Learning-driven Approaches for Detecting Payment Anomalies
Creator
Multani, Danish; Radhakrishnan, G.V.; Shankar, Uma; Upreti, Kamal; Gupta, Komal; Tiwari, Akhilesh
Description
The fast-paced development of digital banking has brought with it new convenience but also tremendous challenges in maintaining transaction security. Banks are confronted with mounting threats from malicious activities like identity theft, account takeover, and unauthorized access, which can lead to huge financial losses and loss of customer confidence. This study investigates the formulation of a cybersecurity framework for fraud prevention in banking through machine learning algorithms. A transactional real-world dataset of 200,000 instances from LOL Bank Pvt. Ltd. was used to construct and evaluate predictive models. Preprocessing included categorical encoding, temporal feature engineering, and synthetic minority oversampling (SMOTE) for class imbalance handling. Three machine learning classifiers - Logistic Regression, Random Forest, and XGBoost - have been compared using measures of accuracy, precision, recall, F1-score, and ROC-AUC. Results show that ensemble models significantly outperformed logistic regression by a wide margin, with Random Forest and XGBoost both achieving over 91% accuracy and very good discrimination power. The study emphasizes how well machine learning-based systems detect theft in real time and outlines avenues for future research to enhance detection using adaptive and interpretable AI models. 2025 IEEE.
Source
2025 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Bank Fraud Detection; Cybersecurity; Financial Security; Fraud Analytics; Machine Learning
Coverage
Multani D., UST Global Solutions Gurgaon, Haryana, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Shankar U., Qaiwan International University, Faculty of Management and Social Sciences, Kurdistan, Sulaymaniyah, Iraq; Upreti K., Christ University, Delhi NCR Campus, Department of Computer Science, Ghaziabad, India; Gupta K., Accenture, Banglore, India; Tiwari A., Christ University, Delhi NCR Campus, Department of Business and Management, Ghaziabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152989-5;
Format
online
Language
English
Type
Conference paper

Citation

Multani, Danish; Radhakrishnan, G.V.; Shankar, Uma; Upreti, Kamal; Gupta, Komal; Tiwari, Akhilesh, “Fraud Prevention in Banking: Machine Learning-driven Approaches for Detecting Payment Anomalies,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25764.

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