Authors : Aravapalli Sri Chaitanya; Mohammad Tasneem Kowsar; Dari Udayasri; Kanaparthi Ravikumar; Borra Sai Ganesh; Basa Datta Manikanta

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/yn7u7pzs

Scribd : https://tinyurl.com/y2yjydzd

DOI : https://doi.org/10.5281/zenodo.14987486

Abstract : As the rise of digital financial transactions exponentially increases, so does the need for efficient detection methods for online fraud, a serious threat in the form of an online cybersecurity oker. In this paper, we proposed an AI-assisted fraud detection framework involving supervised ML models (Logistic Regression, Random Forest, XGBoost) and unsupervised anomaly detection (Isolation Forest, Autoencoders) for real-time fraud detection. Additional feature engineering techniques, such as geolocation tracking, user profiling, and SMOTE for addressing class imbalance serve to improve performance. For real-time monitoring, we use a Streamlit-based interface and deploy with Flask/FastAPI a RESTful API of the trained model to easily integrate within the fintech. Precision, recall, F1-score and ROC-AUC metrics for fraud risk assessment optimize fraud detection while placing a constraint on false positives, making a balanced fraud risk assessment. The experimental results demonstrate their high accuracy and efficiency, which will guarantee this framework a large scale for use in financial institutions, e-commerce and cyber security systems. The study introduces data- driven solutions to beat evolving fraud; it helps enhance adaptive fraud detection for large systems.

Keywords : Fraud Detection, Machine Learning, Anomaly Detection, Online Transactions, Supervised Learning, Unsupervised Learning, Financial Security, Cyber Threats.

References :

1. U. Siddaiah, P. Anjaneyulu, M. Ramesh, Y. Haritha. "Fraud Detection in Online Payments using Machine Learning Techniques" .DOI:10.1109/ICICCS56967.2023.10142404.
2. M. Naga Raju, Yarramreddy Chandrasena Reddy, Polavarapu Nagendra Babu, Venkata Sai Pavan Ravipati, Velpula Chaitanya. "Detection of Fraudulent Activities in Unified Payments Interface using Machine Learning -LSTM Networks". DOI  : 10.1109/ICCPCT61902.2024.10672890.
3. Pinku Ranjan, Kammari Santhosh, Somesh Kumar, Arun Kumar. "Fraud Detection on Bank Payments Using Machine Learning". DOI : 10.1109/ICONAT53423.2022.9726104.
4. Deepanshu Thapa, Aditya Harbola ,Aditya Joshi, Vandana Rawat, Neha Pandey. "Machine learning Models for Detecting Anomalies in Online Payment: A Comparative Analysis". DOI : 10.1109/NMITCON58196.2023.10276124.
5. Reem A. Alzahrani, Malak Aljabri, Rami Mustafa, A. Mohammad. "Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms". DOI : 10.1109/ACCESS.2025.3532200.
6. Abed Mutemi, Fernando Bacao."E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review". DOI: 10.26599/BDMA.2023.9020023.
7. Rayene Bounab, Karim Zarour, Bouchra Guelib, Nawres Khlifa."Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN". DOI - 10.1109/ACCESS.2024.3385781.
8. Dr. Yogesh W Bhowte, Dr. Arundhati Roy, Dr. K. Bhavana Raj, Dr. Megha Sharma, Dr. K. Devi, Dr. Prem Latha Soundarraj."Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector". DOI - 10.1109/ICONSTEM60960.2024.10568756.
9. Dr. Shaik Rehana Banu, Dr. Taviti Naidu Gongada, Kathari Santosh, Harish Chowdhary, Sabareesh R, Dr. S. Muthuperumal."Financial Fraud Detection Using Hybrid Convolutional and Recurrent Neural Networks: An Analysis of Unstructured Data in Banking". DOI - 10.1109/ICCSP60870.2024.10543545.
10. Ibomoiye Domor Mienye, Nobert Jere. "Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions". DOI - 10.1109/ACCESS.2024.3426955.
11. Saad Hammood Mohammed, Abdulmajeed Al-Jumaily, Mandeep S. Jit Singh, Víctor P. Gil Jiménez, Aqeel S. Jaber, Yaseein Soubhi Hussein, Mudhar Mustafa Abdul Kader Al-Najjar, Dhiya Al-Jumeily."A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid". DOI - 10.1109/ACCESS.2024.3370911.