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Enhanced Fraud Detection in Financial Transactions using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
As digital banking, online payments, and e-commerce rapidly evolve, financial transactions are increasingly vulnerable to fraud. Traditional fraud detection systems often produce high false positives and fail to adapt to changing tactics. Our solution employs advanced supervised learning algorithms like Random Forest, XGBoost, and Logistic Regression to accurately analyse transactional data and quickly identify fraudulent activity. By integrating sophisticated data preprocessing, feature engineering, and techniques to manage class imbalances, we significantly improve prediction accuracy. Our results show that these ensemble-based models outperform conventional classifiers, delivering enhanced fraud detection with fewer false alarms, ultimately strengthening transaction security and reducing financial losses in modern digital banking.
Keywords
Financial Fraud Detection, Machine Learning, XGBoost, Random Forest, Class Imbalance, Digital Transactions
References
[1] E. Ngai et al., Decision Support Systems 50, 2011, 559 – 569, The operation of Data Mining ways in Financial Fraud Detection: A Bracket Framework and an Academic Review of Literature
[2] Albashrawi et al., in Journal of Data Science 14(2016), 553- 570, Detecting Financial Fraud Using Data Mining Ways: A Decade Review from 2004 to 2015.
[3] Synthetic fiscal Datasets for Fraud Detection, TESTIMON atNTNUwasattainedfrom https//www.kaggle.com/ntnutestimon/paysim1
[4] A Novel Approach to Clustering Utilising Multivariate Outlier Identification by Jayakumar et al. Data Science Journal 11(2013) 69 – 84
[5] Jans et al., Expert Systems with Applications 2011; 38 13351 – 13359, A Business Process Mining operation for Internal Transaction Fraud Mitigation
[6] Phua et al., Classifying Skewed Data in Fraud Detection: A Minority Report. 2004; 6 50 – 59 in ACM SIGKDD studies Newsletter.
[7] Dharwa et al., A Grounded-on Data Mining with Hybrid Approach, International Journal of Computer Operations, 2011; 16 18 – 25.
[8] A cost-sensitive decision tree system for fraud discovery was developed by Shin et al. and published in Expert Systems with Applications in 2013; 40 5916 – 5923.
[9] Sorourenejad et al., A Survey of Styles for Detecting Credit Card Fraud: An Approach concentrated on Data and ways, 2016
[10] Wedge et al.," Automated Feature Engineering Solves the False Cons Problem in Fraud Prediction," Machine Learning and Knowledge Discovery in Databases, pp. 372 – 388, 2018
How to cite this paper
@article{1715333,
author = {M Monika, Priyanka M, Nagalakshmi R G, M Lahari Reddy, Mohith T N},
title = {Enhanced Fraud Detection in Financial Transactions using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {1721-1727},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715333.pdf},
abstract = {As digital banking, online payments, and e-commerce rapidly evolve, financial transactions are increasingly vulnerable to fraud. Traditional fraud detection systems often produce high false positives and fail to adapt to changing tactics. Our solution employs advanced supervised learning algorithms like Random Forest, XGBoost, and Logistic Regression to accurately analyse transactional data and quickly identify fraudulent activity. By integrating sophisticated data preprocessing, feature engineering, and techniques to manage class imbalances, we significantly improve prediction accuracy. Our results show that these ensemble-based models outperform conventional classifiers, delivering enhanced fraud detection with fewer false alarms, ultimately strengthening transaction security and reducing financial losses in modern digital banking.},
keywords = {Financial Fraud Detection, Machine Learning, XGBoost, Random Forest, Class Imbalance, Digital Transactions},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715333}
}