THE FUTURE OF FRAUD PREVENTION: MLOPS, FEATURE ENGINEERING, AND THE BALANCE OF AUTOMATION AND ETHICS
Keywords:
MLOps (Machine Learning Operations), Feature Engineering, Fraud Detection, Adaptive Algorithms, Predictive AnalyticsAbstract
This article explores the dynamic landscape of fraud detection in the digital age, focusing on the integration of Machine Learning Operations (MLOps) and advanced feature engineering techniques. It examines the rapid evolution of fraud trends and the challenges organizations face in maintaining effective detection systems. The paper delves into the critical role of MLOps in automating and streamlining the machine learning lifecycle for fraud detection, highlighting key components of a robust MLOps pipeline. Additionally, it emphasizes the importance of feature engineering in enhancing model accuracy and adaptability. The synergy between MLOps and feature engineering is discussed, showcasing how their integration creates a powerful, adaptive fraud detection system capable of anticipating and mitigating future threats. The article also addresses emerging technologies and trends in fraud detection, while considering ethical implications, data privacy concerns, and the balance between automation and human oversight. By examining these interconnected aspects, the paper provides a comprehensive overview of cutting-edge approaches to combating fraud in an ever-evolving technological landscape.
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Copyright (c) 2024 Bhupendrasinh Thakre (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
