Fraud detection using machine leaning: a comparative analysis of neural networks & support vector machines
Gitonga, Joseph Theuri
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Fraud detection and prevention tools have been evolving over the past decade with the ever growing combination of resources, tools, and applications in big data analytics. The rapid adoption of a new breed of models is offering much deeper insights into data. There are numerous machine learning techniques in use today but irrespective of the method employed the objective remains to demonstrate comparable or better recognition performance in terms of the precision and recall metrics. This study evaluates two advanced Machine Learning approaches: Support Vector Machines and Neural Networks while taking a look at Deep Learning. The aim is to identify the approach that best identifies fraud cases and discuss challenges in their implementation. The approaches were evaluated on real-life credit card transaction data. Support Vector Machines demonstrated overall better performance across the various evaluation measures although Deep Neural Networks showed impressive results with better computational efficiency.