Beschreibung
As financial institutions in the world drive towards achieving a cashless economy, by increasing citizens spending power and reducing the high cost of money handling, the use of credit cards is of great necessity for this purpose, hence, with this new drive for a cashless economy, there will be significant increase of the use of credit card and also fraudulent activities associated with it. This work serves as a proactive measure in detecting fraudulent activities regarding the credit card. The study presents a hierarchical temporal memory based model that can detect fraudulent transactions carried out with the use of credit card. A novel approach in machine learning known as the Cortical Learning Algorithm was adopted to build the credit card fraud detection model. The algorithm worked on the credit card data obtained from the UCI Repository, it converted the highly populated data to a sparse representation, and then used its learning columns to learn spatial patterns. The Object Oriented Analysis and Design methodology was used in this work and was implemented using Java programming language. The resulting model performed online learning and recorded high percentage accuracy.
Autorenporträt
Oghenekaro, Linda Uchenna is a Lecturer in the Department of Computer Science, University of Port Harcourt, Rivers State, Nigeria. She obtained her B.Sc (Hons) and M.Sc degrees in Computer Science from the University of Port Harcourt. Her research interest include Machine Learning, Distributed Database and Data Security.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
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