Enhancing financial fraud detection using adaptive machine learning models and business analytics
1 Independent Researcher, Chicago, Illinois, USA.
2 Independent Researcher; Lagos Nigeria.
3 Independent Researcher, Abuja, Nigeria.
4 Newcross Exploration and Production Limited, Nigeria.
Review
International Journal of Scientific Research Updates, 2024, 08(02), 012–021.
Article DOI: 10.53430/ijsru.2024.8.2.0054
Publication history:
Received on 28 August 2024; revised on 05 October 2024; accepted on 08 October 2024
Abstract:
Financial fraud continues to be a critical threat to businesses and economies worldwide, necessitating advanced detection techniques. This paper reviews the role of adaptive machine learning (ML) models and business analytics in enhancing fraud detection systems. Traditional fraud detection methods often fall short in addressing the complexity and evolving nature of fraudulent activities, making adaptive ML models, such as decision trees and neural networks, more effective in identifying subtle patterns in large datasets. Organizations can refine ML models by integrating business analytics, ensuring real-time detection and continuous improvement of fraud detection systems. The paper also explores the challenges in deploying these technologies, including data privacy concerns and model accuracy, as well as the potential impact of emerging technologies such as blockchain and quantum computing. Future directions emphasize improving the interpretability of ML models and leveraging multi-modal data for a more holistic approach to fraud prevention. The synergy between adaptive machine learning and business analytics promises a more resilient and effective framework for combating financial fraud.
Keywords:
Financial fraud detection; Adaptive machine learning; Business analytics; Fraud prevention; Real-time detection
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0