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Fraud Detection Systems Empowered by Context-Awareness: Leveraging Dynamic Machine Learning Techniques
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Resumo(s)
In the dynamic and rapidly evolving field of financial technology, effective fraud detection
systems (FDS) are crucial for maintaining security and customer trust. Traditional FDS methods
often rely on static models, which struggle to adapt to emerging fraud patterns and evolving
data distributions. This thesis explores the application of dynamic techniques to optimize
fraud detection results, moving beyond conventional risk scores and static thresholds. The
research employs a combination of machine learning (ML) approaches, including the use of
secondary models for dynamic threshold adjustment and reinforcement learning (RL) for
direct decision-making. A robust methodology was adopted, starting with an in-depth
understanding of the business context, data exploration, feature engineering, and advanced
sampling strategies to address data imbalance. Various strategies were tested, including
anomaly detection using Isolation Forests, dynamic threshold setting through meta-learning,
and RL to eliminate traditional score-based decision processes. The results demonstrated
improvements in maximizing the true positive rate (TPR) while maintaining the false positive
rate (FPR), ensuring more accurate and reliable fraud detection. By leveraging real-world data
from a European transaction processor, this study provides empirical evidence of the benefits
of dynamic optimization techniques in practical applications. The findings contribute to the
ongoing development of adaptive and efficient FDS, highlighting the potential of combining
advanced ML and RL methods to enhance the robustness and effectiveness of FDSs.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Fraud Detection Fraud Detection Systems Dynamic Systems Machine Learning Reinforcement Learning SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities