International Journal on Science and Technology

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Call for Paper Volume 16 Issue 4 October-December 2025 Submit your research before last 3 days of December to publish your research paper in the issue of October-December.
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Volume 16 Issue 4
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Data-Driven Optimization of Fraud Detection Pipelines Through Contemporary Machine Learning Frameworks

Author(s) Ms. Manjali Gupta, Ms. Preeti, Mr. Jitender Kumar
Country India
Abstract In the quickly changing digital economy, financial fraud has become a serious issue, requiring the implementation of increasingly advanced and trustworthy detection systems. This study investigates the efficacy of cutting-edge machine learning technologies in reducing financial fraud in a variety of transactional contexts. The study examines how well state-of-the-art algorithms like ensemble learning, deep neural networks, anomaly detection models, and hybrid machine learning frameworks perform in detecting nuanced, intricate, and dynamic fraudulent patterns. The work provides a thorough evaluation of model resilience by using actual and synthetic financial datasets to examine accuracy, precision, recall, and false-positive rates. Results show that advanced machine learning techniques greatly improve fraud detection capabilities, especially when models include dynamic learning, feature engineering, and real-time monitoring. The study also emphasizes how crucial explainability, algorithmic fairness, and scalability are when implementing ML-based fraud prevention systems in financial institutions. Overall, the study provides insightful information on how cutting-edge machine learning techniques may assist regulatory compliance, bolster financial security, and offer an adaptable defense against more complex fraud schemes
Keywords Deep Learning, Artificial Intelligence, Fraud Detection Models
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-12-07
DOI https://doi.org/10.71097/IJSAT.v16.i4.9819
Short DOI https://doi.org/hbf83w
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