Publisher's Synopsis
In order to thwart fraudsters, financial institutions must use current, advanced, customized predictive analytics to protect themselves. Data scientists and statisticians who understand machine learning and statistical methods are in increasingly high-demand and the demand for them is growing each year. Technically, machine learning is a subfield of artificial intelligence whereas statistics is subdivision of mathematics and many believe they only need in depth knowledge of one in order to be a predictive modeler. This fallacy leads to inefficient
and/or inaccurate models, and sadly, many industries have not yet realized that the mathematics behind the model is just as important, if not more important, than the computer science needed to implement it. However, some businesses have and this thesis will hopefully help both industry and academia move further along in this direction.
