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Enhancing Fraud Detection in Financial Transactions Using AI and Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
The rising number of digital transactions and the increasing complexity of fraudulent activities provide a significant challenge to financial institutions when it comes to detecting fraud in financial transactions. If fraud trends are constantly changing, traditional rule-based fraud detection systems won't be able to keep up. In order to improve the efficiency and accuracy of identifying fraudulent transactions, this study investigates AI-powered fraud detection that makes use of machine learning techniques. We test the efficacy of several ML models for anomaly detection and predicted fraud categorization using both supervised and unsupervised learning techniques. We also go over ways to enhance the performance of the model through feature engineering, data pretreatment, and real-time detection. In order to detect complicated fraud patterns with minimal false positives, the study emphasizes the benefits of deep learning and ensemble learning methods. Issues of ethics, practical difficulties, and potential avenues for further study with AI-powered fraud detection are also covered. According to the results, financial security and loss prevention are both greatly enhanced by AI-based fraud detection.
Keywords
Artificial Intelligence (AI), Financial Transactions, Anomaly Detection, Machine Learning, and Fraud Detection
How to cite this paper
@article{1707332,
author = {Syed Ahad Murtaza Alvi, Ashish Kumar Pandey},
title = {Enhancing Fraud Detection in Financial Transactions Using AI and Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {450-459},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707332.pdf},
abstract = {The rising number of digital transactions and the increasing complexity of fraudulent activities provide a significant challenge to financial institutions when it comes to detecting fraud in financial transactions. If fraud trends are constantly changing, traditional rule-based fraud detection systems won't be able to keep up. In order to improve the efficiency and accuracy of identifying fraudulent transactions, this study investigates AI-powered fraud detection that makes use of machine learning techniques. We test the efficacy of several ML models for anomaly detection and predicted fraud categorization using both supervised and unsupervised learning techniques. We also go over ways to enhance the performance of the model through feature engineering, data pretreatment, and real-time detection. In order to detect complicated fraud patterns with minimal false positives, the study emphasizes the benefits of deep learning and ensemble learning methods. Issues of ethics, practical difficulties, and potential avenues for further study with AI-powered fraud detection are also covered. According to the results, financial security and loss prevention are both greatly enhanced by AI-based fraud detection.},
keywords = {Artificial Intelligence (AI), Financial Transactions, Anomaly Detection, Machine Learning, and Fraud Detection},
month = {April},
}