Real-Time Transaction Fraud Detection Using Adaptive Hoeffding Trees for Concept-Drift Resilience
DOI:
https://doi.org/10.63503/j.ijcma.2025.156Keywords:
Quantum Machine Learning, Drug Discovery, Molecular Simulation, Molecule Generation, Noisy intermediate-scale Quantum (NISQ)Abstract
The fight between the fraudsters and financial institutions is not a static battle its ever changing. Fraudsters always keep up- dating their techniques, so financial institutions also need to evolve their technologies. Therefore, it requires a development of technology that can learn in real time that is dynamic system instead of batch-trained models. This paper introduces a online learning framework for real-time transactional fraud detection that directly explains the challenge of concept drift. We treat the complex IEEE-CIS dataset not as a static file, but as a continuous flow of live transactions. Our methodology is cantered on the river machine learning library, employing an Adaptive Hoeffding Tree Classifier an incremental decision tree capable of learning from data one sample at a time and adapting its very structure to changes in the fundamental data distribution. We illustrate using a sequential evaluation that the model’s performance gradually improves as the system processes a large amount of data, and dramatically, and it can recover and adapt after the introduction of new fraud patterns. This work represents the fundamental superiority of online learning for real-time applications and provides a practical blueprint for building fraud detection systems that learn and evolve, rather than simply executing a static set of learned rules.
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