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dc.contributor.authorGitonga, Joseph Theuri
dc.date.accessioned2019-05-09T07:43:37Z
dc.date.available2019-05-09T07:43:37Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/11071/6496
dc.descriptionSubmitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science in Finance at Strathmore Universityen_US
dc.description.abstractFraud 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.en_US
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectfraud detectionen_US
dc.subjectmachine learningen_US
dc.subjectneural networken_US
dc.titleFraud detection using machine leaning: a comparative analysis of neural networks & support vector machinesen_US
dc.typeThesisen_US


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