Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are asking is how
to extract this value. The answer is Data mining.
There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations.
In our current research we are attempting to compare the aforementioned technologies and determine if Neural Networks outperform more traditional statistical techniques. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.
Data mining is the term used to describe the process of extracting value from a database. A data-warehouse is a location where information is stored. The type of data stored depends largely on the type of industry and the company. Many companies store every piece of data they have collected, while others are more ruthless in what they deem to be “important”.
Income is a very important socio-economic indicator. If a bank knows a
person’s income, they can offer a higher credit card limit or determine if they are likely to want information on a home loan or managed investments. Even though this financial institution had the ability to determine a customer’s income in two ways, from their credit card application, or through regular direct deposits into their bank account, they did not extract and utilize this information.
Another example of where this institution has failed to utilise its data-warehouse is in cross-selling insurance products (e.g. home, contents, life and motor vehicle insurance). By using transaction information they may have the ability to determine if a customer is making payments to another insurance broker. This would enable the
institution to select prospects for their insurance products. These are simple examples of what could be achieved using data mining.